Methylation marker for identifying cancer and application

Abstract

The invention relates to a cancer identification method and application of a methylation marker and a detection reagent thereof in preparation of a kit for diagnosing cancer of an object.

Description

Identification and application of methylation markers for cancer Technical Field

[0001] This application relates to the field of biomedicine, specifically to a method for early diagnosis and screening of cancer Background Art

[0002] According to the "2020 Global Cancer Burden Data" recently released by the World Health Organization's International Agency for Research on Cancer (IARC), 19.29 million new cancer cases were reported worldwide in 2020, including 4.57 million in China. Malignant tumors have become the leading cause of death and serious threats to people's lives and health. Early detection, diagnosis, and treatment are currently recognized as the most effective means of combating cancer.

[0003] Colorectal cancer is the third most common and deadly cancer worldwide. In recent years, changes in diet and other lifestyle factors have led to a gradual increase in its incidence, posing a significant threat to human health. The cure rate for colorectal cancer is closely linked to the stage of the cancer. While the five-year survival rate for patients with stage I and II colorectal cancer reaches 80%, it drops to 50% for stage III patients and only 8% for stage IV patients. Unfortunately, most patients experience no obvious symptoms in the early stages of colorectal cancer and are already in the middle or late stages by the time they seek medical attention, missing the optimal treatment window. Therefore, regular colorectal cancer screening for at-risk individuals is crucial for improving treatment outcomes and saving lives. In clinical practice, colonoscopy is the gold standard for colorectal cancer diagnosis, but its drawbacks include high operational difficulty, lengthy preparation, significant patient pain, and high cost, making it unsuitable for large-scale screening. Currently, there are a variety of non-invasive detection methods available for colorectal cancer screening, such as the stool-based fecal immunochemical test (FIT). These methods have the advantages of convenience and speed, but their detection performance is poor, they are easily interfered by factors such as diet, and their sensitivity and specificity are relatively low.

[0004] Gastric cancer is the second most common type of cancer worldwide, with nearly two-thirds of cases occurring in developing countries. According to existing data, gastric cancer is the fourth most common cancer among men and the seventh most common cancer among women. Gastric cancer has become a serious threat to public health. Developing convenient and effective methods for early diagnosis of gastric cancer is crucial for reducing mortality and improving survival. Tumor markers are an important diagnostic tool, providing simple and affordable evidence for clinical diagnosis and treatment, while reducing screening costs for patients. Blood is the preferred source of candidate tumor markers for gastric cancer screening. Blood-based biomarkers provide a comprehensive overview of the patient's entire body, including the primary tumor, metastatic disease, immune response, and the surrounding stroma. Common blood markers for gastric cancer include CEA, CA19-9, and CA72-4. These tumor markers suffer from low sensitivity, with detection rates of only approximately 50%. Furthermore, poor specificity is a significant drawback. For example, serum levels of CA19-9 are elevated in various adenocarcinomas (including pancreatic cancer, hepatobiliary cancer, and gastric cancer). CEA is elevated in various cancers and even non-cancerous conditions. Due to their low sensitivity and specificity, the use of these blood markers in actual clinical practice, especially for early screening of gastric cancer, is relatively limited.

[0005] Esophageal cancer is one of the most common malignancies worldwide, characterized by high morbidity and mortality, posing a serious threat to public health. Early symptoms of esophageal cancer are subtle, and specific diagnostic methods are unavailable. Consequently, most patients are diagnosed at an advanced stage. Tumor markers are also a key diagnostic tool for esophageal cancer. Previous studies have primarily examined the differences between single serum biomarkers in esophageal cancer patients and healthy controls, such as miR-138, but their sensitivity and specificity have not met expectations. Some studies have combined serum biomarkers, such as the combined detection of multiple small RNAs, but improvements in sensitivity and specificity have been limited. Furthermore, despite the accumulation of extensive data on the efficacy of several serum biomarkers over the past few decades, guidelines and standardized protocols for their implementation in esophageal cancer patients for early detection are lacking. Circulating tumor DNA (ctDNA), derived from apoptotic or necrotic tumor cells, carries tumor-specific DNA methylation signatures from early-stage malignancies and has been investigated in recent years as a promising new target for developing noninvasive early screening tools for various cancers. However, most of these studies have yielded inconclusive results.

[0006] Liver cancer (HCC) is a serious health threat in my country. With its insidious onset, patients often develop clinical symptoms in the advanced stages, making curative treatment impossible and the prognosis extremely dire. Therefore, the earlier a liver cancer patient is diagnosed, the better the treatment outcome and the higher the survival rate. Common diagnostic methods include alpha-fetoprotein (AFP), an immunoassay that detects embryonic antigens. It is one of the most specific methods for diagnosing HCC and is relatively specific for this condition. In the absence of other evidence of HCC, a positive α-fetoprotein counter-flow immunoelectrophoresis assay or a quantitative level >500 ng / ml for at least one month, combined with the exclusion of pregnancy, active liver disease, or embryonal gonadal tumors, confirms HCC. Blood enzyme tests: Serum levels of γ-glutamyl transpeptidase, alkaline phosphatase, and lactate dehydrogenase isoenzymes may be elevated in HCC patients. However, due to their lack of specificity, these tests are primarily used as supplementary diagnostic tools.

[0007] Therefore, finding convenient and effective methods for cancer diagnosis or prognosis and recurrence monitoring plays a vital role in reducing the mortality rate caused by cancer and improving its survival rate.

[0008] Summary of the Invention

[0009] The present application provides a kind of methylation marker and application for early non-invasive identification of cancer (for example, colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer), based on the methylation level of the biomarker group of the present application in plasma, can conveniently, accurately and efficiently identify (for example, colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer) patients, for (for example, colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer) early diagnosis provides a new method, the detection process of the present application is non-invasive, highly safe, and convenient for large-scale clinical application. The present application only needs to detect the methylation level of several or even one gene to detect benign and malignant, significantly reduces the target detection area, improves the application range of the technology, and can include more samples. The methylation marker, detection method and / or kit of the present application have the characteristics of high sensitivity and specificity in applications such as early diagnosis and recurrence monitoring of cancer.

[0010] This application detects the methylation levels of DNA methylation markers in patient samples, and uses the detected methylation level data to predict scores based on a diagnostic model to distinguish between cancer patients and non-cancer patients, thereby achieving the goal of early cancer diagnosis with higher accuracy and lower cost during early screening.

[0011] On the one hand, the present application provides a colorectal cancer methylation marker, which is an isolated nucleic acid molecule from a mammal, the sequence of which includes: (1) any one or more (e.g., at least 6, at least 7, at least 8 or at least 9) or all of the sequences shown in SEQ ID NO: 1-47 or their complementary sequences or variants, wherein the variant is a variant having at least 70% sequence identity with the corresponding sequence, and the methylation site in the variant has not been mutated, or (2) a treated sequence of (1), wherein the treatment converts the unmethylated cytosine into a base having a lower ability to bind to guanine than to cytosine.

[0012] In one or more embodiments, the (1) is selected from any one of the following groups:

[0013] (1.1) any one or more or all of the following sequences: SEQ ID NO: 4 or its complementary sequence or variant, SEQ ID NO: 11 or its complementary sequence or variant, SEQ ID NO: 15 or its complementary sequence or variant, SEQ ID NO: 18 or its complementary sequence or variant, SEQ ID NO: 19 or its complementary sequence or variant, SEQ ID NO: 30 or its complementary sequence or variant, SEQ ID NO: 34 or its complementary sequence or variant, SEQ ID NO: 37 or its complementary sequence or variant, SEQ ID NO: 41 or its complementary sequence or variant, and optionally further comprising any one or more of the remaining sequences in SEQ ID NOs: 1-47 or their complementary sequences or variants,

[0014] (1.2) any one or more or all of the following sequences: SEQ ID NO: 1 or its complementary sequence or variant, SEQ ID NO: 21 or its complementary sequence or variant, SEQ ID NO: 29 or its complementary sequence or variant, SEQ ID NO: 36 or its complementary sequence or variant, SEQ ID NO: 44 or its complementary sequence or variant, SEQ ID NO: 47 or its complementary sequence or variant, optionally including any one or more of the remaining sequences in SEQ ID NOs: 1-47 or their complementary sequences or variants,

[0015] (1.3) any one or more or all of the following sequences: SEQ ID NO: 6 or its complementary sequence or variant, SEQ ID NO: 10 or its complementary sequence or variant, SEQ ID NO: 13 or its complementary sequence or variant, SEQ ID NO: 14 or its complementary sequence or variant, SEQ ID NO: 22 or its complementary sequence or variant, SEQ ID NO: 28 or its complementary sequence or variant, SEQ ID NO: 43 or its complementary sequence or variant, and optionally including any one or more of the remaining sequences in SEQ ID NO: 1-47 or their complementary sequences or variants.

[0016] In one or more embodiments, the methylation sites are consecutive CpGs.

[0017] In one or more embodiments, the methylation marker may be any one or more CpG sites in the sequence region.

[0018] In one or more embodiments, the nucleic acid molecule is used as an internal standard or control for detecting the DNA methylation level of the corresponding sequence in a sample.

[0019] On the other hand, the present application provides a reagent for detecting DNA methylation for screening colorectal cancer risk, diagnosing colorectal cancer, and evaluating colorectal cancer prognosis, wherein the reagent comprises a reagent for detecting the methylation level of a marker in a sample of a subject, wherein the marker is a DNA sequence and the upstream 5kb and downstream 5kb of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, wherein the DNA sequence includes one or more or all of the following gene sequences: (p) TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EV C2, HMX1, PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SO RCS3, VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2.

[0020] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p).

[0021] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally includes one or more or all of the other gene sequences in (p).

[0022] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p).

[0023] In one or more embodiments, the marker comprises at least 3 CpG dinucleotides.

[0024] In one or more embodiments, the DNA sequence comprises a sense strand or an antisense strand of DNA.

[0025] In one or more embodiments, the fragment is 1-1000 bp in length, preferably 1-700 bp in length. In one or more embodiments, the fragment is a promoter region of a gene sequence or a portion thereof. In one or more embodiments, the fragment comprises at least one, preferably at least three, CpG dinucleotides. Preferably, the marker has the sequence of a nucleic acid molecule as described herein.

[0026] In one or more embodiments, the reagent is a primer molecule that hybridizes to the marker or its transformed sequence. The primer molecule is capable of amplifying the marker or its transformed variant. In one or more embodiments, the primer sequence is methylation-specific or non-specific. The primer molecule is at least 9 bp.

[0027] In one or more embodiments, the reagent is a probe molecule that hybridizes with a marker or its converted sequence. In one or more embodiments, the probe further comprises a detectable substance. In one or more embodiments, the detectable substance is a 5' end fluorescent reporter group and a 3' end labeled quencher group. In one or more embodiments, the fluorescent reporter gene is selected from Cy5, FAM, and VIC. The probe molecule is at least 12 bp.

[0028] In one or more embodiments, the sample is from a mammal, preferably a human.

[0029] On the other hand, the present application provides a medium recording a DNA sequence or a fragment thereof and / or methylation information thereof, wherein the DNA sequence comprises:

[0030] (i) one or more or all of the following gene sequences: (p) TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, and SIM2,

[0031] or (ii) a sequence treated according to (i), wherein the treatment converts unmethylated cytosine into a base having a lower binding ability with guanine than with cytosine.

[0032] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p).

[0033] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally includes one or more or all of the other gene sequences in (p).

[0034] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p).

[0035] In one or more embodiments, the medium is used to compare with gene methylation sequencing data to determine the presence, amount and / or methylation level of nucleic acid molecules containing the sequence or fragments thereof.

[0036] In one or more embodiments, the marker comprises at least 3 CpG dinucleotides.

[0037] In one or more embodiments, the DNA sequence comprises a sense strand or an antisense strand of DNA.

[0038] In one or more embodiments, the fragment is 1-1000 bp in length, preferably 1-700 bp in length. In one or more embodiments, the fragment is a promoter region of a gene sequence or a portion thereof. In one or more embodiments, the fragment comprises at least one, preferably at least three, CpG dinucleotides. Preferably, the marker has a sequence as shown in any one of SEQ ID NOs: 1-47 of the nucleic acid molecules described herein.

[0039] In one or more embodiments, the medium is a carrier printed with the DNA sequence or fragment thereof and / or methylation information, including a card, such as a paper, plastic, metal, or glass card.

[0040] In one or more embodiments, the medium is a computer-readable medium storing the sequence and / or its methylation information and a computer program, and when the computer program is executed by a processor, the following steps are performed: comparing the methylation sequencing data of the sample with the sequence or information to obtain the presence, content, and / or methylation level of the nucleic acid molecule containing the sequence in the sample. The presence, content, and / or methylation level of the nucleic acid molecule containing the sequence is used to screen for colorectal cancer risk, diagnose colorectal cancer, and assess colorectal cancer prognosis.

[0041] On the other hand, the present application also provides the use of the following (a) and optionally (b) in preparing a kit for screening colorectal cancer risk, diagnosing colorectal cancer, and evaluating the prognosis of colorectal cancer,

[0042] (a) a reagent or device for determining the methylation level of a marker in a sample of a subject, wherein the marker is a DNA sequence and 5 kb upstream and 5 kb downstream of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein,

[0043] (b) the marker or a nucleic acid molecule thereof treated so as to convert unmethylated cytosine into a base having a lower binding ability with guanine than with cytosine,

[0044] wherein the DNA sequence comprises one or more or all of the following gene sequences: (p)TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, and SIM2.

[0045] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p).

[0046] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally includes one or more or all of the other gene sequences in (p).

[0047] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p).

[0048] In one or more embodiments, the marker comprises at least 3 CpG dinucleotides.

[0049] In one or more embodiments, the DNA sequence comprises a sense strand or an antisense strand of DNA.

[0050] In one or more embodiments, the fragment length is 1-1000bp, preferably 1-700bp. In one or more embodiments, the fragment is a promoter region or portion thereof of a gene sequence. In one or more embodiments, the fragment comprises at least 1, preferably at least 3 CpG dinucleotides. Preferably, the marker has a sequence as shown in any one of SEQ ID NOs: 1-47 of the nucleic acid molecules described herein.

[0051] In one or more embodiments, the nucleic acid molecule of (b) is a nucleic acid molecule comprising the sequence shown in any one of SEQ ID NOs: 1-47.

[0052] In one or more embodiments, the reagents comprise primer molecules and / or probe molecules.

[0053] In one or more embodiments, the reagent comprises a primer molecule that hybridizes to the marker or its transformed sequence. The primer molecule is capable of amplifying the DNA sequence or its fragment or its transformed variant. In one or more embodiments, the primer sequence is methylation-specific or non-specific. The primer molecule is at least 9 bp.

[0054] In one or more embodiments, the reagent is a probe molecule that hybridizes with the marker or its converted sequence. In one or more embodiments, the probe also contains a detectable substance. In one or more embodiments, the detectable substance is a 5' end fluorescent reporter group and a 3' end labeled quencher group. In one or more embodiments, the fluorescent reporter gene is selected from Cy5, FAM and VIC. The probe molecule is at least 12bp.

[0055] In one or more embodiments, the reagent comprises a medium as described in any of the embodiments herein.

[0056] In one or more embodiments, the kit is a non-invasive diagnostic kit.

[0057] In one or more embodiments, the subject is a mammal, preferably a human.

[0058] In one or more embodiments, the sample is from a mammalian tissue, cell, or body fluid, such as an intestinal tissue sample, blood, serum, or plasma. The mammal is preferably a human. In one or more embodiments, the sample comprises genomic DNA. Preferably, the sample is blood.

[0059] In one or more embodiments, the DNA sequence is a genomic sequence of a corresponding marker, or a converted sequence thereof, or a sequence thereof treated with a methylation-sensitive restriction endonuclease, wherein the conversion converts unmethylated cytosine to a base with a lower binding ability with guanine than cytosine. The conversion is performed using an enzymatic method, preferably deaminase treatment, or a non-enzymatic method, preferably treatment with bisulfite, bisulfite, or pyrosulfite, or a combination thereof.

[0060] In one or more embodiments, the kit further comprises PCR reaction reagents. Preferably, the PCR reaction reagents include DNA polymerase, PCR buffer, dNTP, Mg 2+ .

[0061] In one or more embodiments, the kit further comprises other reagents for detecting DNA methylation, wherein the other reagents are selected from one or more of the following methods: PCR based on bisulfite conversion (e.g., methylation-specific PCR), DNA sequencing (e.g., bisulfite sequencing, whole genome methylation sequencing, simplified methylation sequencing), methylation-sensitive restriction endonuclease analysis, fluorescence quantitative method, methylation-sensitive high-resolution melting curve method, chip-based methylation mapping and mass spectrometry (e.g., flight mass spectrometry). Preferably, the other reagents are selected from one or more of the following: bisulfite, bisulfite, acid sulfite or pyrosulfite or derivatives thereof, methylation-sensitive or insensitive restriction endonuclease, enzyme digestion buffer, fluorescent dye, fluorescence quencher, fluorescent reporter, exonuclease, alkaline phosphatase, internal standard and control.

[0062] In one or more embodiments, the PCR reaction solution comprises Taq DNA polymerase, PCR buffer, dNTPs, KCl, MgCl2 and (NH4)2SO4. Preferably, the Taq DNA polymerase is a hot start Taq DNA polymerase. Preferably, MgCl2 is a hot start Taq DNA polymerase. 2+ The final concentration is 1.0-10.0 mM.

[0063] In one or more embodiments, the screening of colorectal cancer risk, diagnosis of colorectal cancer, and assessment of colorectal cancer prognosis include: comparing the methylation level of the marker with the corresponding reference level, and screening the risk of colorectal cancer, diagnosing colorectal cancer, and assessing the prognosis of colorectal cancer based on the score.

[0064] In one or more embodiments, the comparison comprises: directly comparing the methylation level of the marker with a reference level, or calculating a score and comparing the score of the methylation level of the marker with a corresponding reference score. Preferably, the calculation is performed by constructing a logistic regression model.

[0065] On the other hand, the present application also provides a method for screening colorectal cancer risk, diagnosing colorectal cancer, or evaluating the prognosis of colorectal cancer, comprising:

[0066] (1) Detecting the methylation level of a marker in a sample of a subject, wherein the marker is a DNA sequence and the upstream 5 kb and downstream 5 kb of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, wherein the DNA sequence includes one or more or all of the following gene sequences: (p) TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IR X1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYS L4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2,

[0067] (2) comparing the methylation level of the marker in step (1) with the corresponding reference level,

[0068] (3) Screen the risk of colorectal cancer, diagnose colorectal cancer, or evaluate the prognosis of colorectal cancer based on the comparison results.

[0069] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p).

[0070] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally includes one or more or all of the other gene sequences in (p).

[0071] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p).

[0072] In one or more embodiments, the marker comprises at least 3 CpG dinucleotides.

[0073] In one or more embodiments, the DNA sequence comprises a sense strand or an antisense strand of DNA.

[0074] In one or more embodiments, the fragment length is 1-1000bp, preferably 1-700bp. In one or more embodiments, the fragment is the promoter region of a gene. In one or more embodiments, the fragment comprises at least 1, preferably at least 3 CpG dinucleotides. Preferably, the marker has the sequence of the nucleic acid molecule described in the first aspect of the present invention.

[0075] In one or more embodiments, the method further comprises, before step (1), a step of obtaining a biological sample containing DNA from the subject, such as DNA extraction and / or quality control.

[0076] In one or more embodiments, step (1) comprises performing the detection using primer molecules, probe molecules and / or mediators described herein, and optionally nucleic acid molecules described herein.

[0077] In one or more embodiments, the detection includes but is not limited to: PCR based on bisulfite conversion, DNA sequencing, methylation-sensitive restriction endonuclease analysis, fluorescence quantitative method, methylation-sensitive high-resolution melting curve method, chip-based methylation profiling, and mass spectrometry.

[0078] In one or more embodiments, the detection is DNA sequencing. In one or more embodiments, the sequencing depth of the DNA sequencing is at least 10X, preferably 20X, more preferably 30X.

[0079] In one or more embodiments, the sample is from a mammalian tissue, cell, or body fluid, such as an intestinal tissue sample, blood, serum, or plasma. The mammal is preferably a human. Preferably, the sample is blood.

[0080] In one or more embodiments, the sample comprises genomic DNA.

[0081] In one or more embodiments, the DNA sequence is a genomic sequence of a corresponding marker, or a converted sequence thereof, or a sequence thereof treated with a methylation-sensitive restriction endonuclease, wherein the conversion converts unmethylated cytosines therein to bases that do not bind to guanine. The conversion is performed using an enzymatic method, preferably deaminase treatment, or the conversion is performed using a non-enzymatic method, preferably treatment with bisulfite, acid sulfite, or pyrosulfite, or a combination thereof.

[0082] In one or more embodiments, the comparison in step (2) comprises: directly comparing the methylation level of the marker in step (1) with a reference level, or calculating a score and comparing the score of the methylation level of the marker with a corresponding reference score. Preferably, the score is calculated using a logistic regression model.

[0083] In one or more embodiments, step (3) includes: when the methylation level of the marker is greater than the reference level, or the methylation level score is greater than the reference score, the subject is at risk of developing colorectal cancer, has colorectal cancer, or has a poor prognosis for colorectal cancer.

[0084] On the other hand, the present application also provides a kit for screening the risk of colorectal cancer, diagnosing colorectal cancer, or evaluating the prognosis of colorectal cancer, comprising:

[0085] (a) a reagent or device for determining the methylation level of a marker in a sample of a subject, wherein the marker is a DNA sequence and 5 kb upstream and 5 kb downstream of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, and

[0086] Optionally, (b) the marker or a treated nucleic acid molecule thereof, wherein the treatment converts unmethylated cytosine into a base having a lower binding ability with guanine than with cytosine,

[0087] wherein the DNA sequence comprises one or more or all of the following gene sequences: (p)TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, and SIM2.

[0088] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p).

[0089] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally includes one or more or all of the other gene sequences in (p).

[0090] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p).

[0091] In one or more embodiments, the marker comprises at least 3 CpG dinucleotides.

[0092] In one or more embodiments, the DNA sequence comprises a sense strand or an antisense strand of DNA.

[0093] In one or more embodiments, the fragment is 1-1000 bp in length, preferably 1-700 bp. In one or more embodiments, the fragment is a promoter region of a gene. In one or more embodiments, the fragment comprises at least 1, preferably at least 3 CpG dinucleotides. Preferably, the marker comprises the sequence of a nucleic acid molecule described herein.

[0094] In one or more embodiments, the kit is suitable for the use described in any embodiment of the present application.

[0095] In one or more embodiments, the nucleic acid molecule is a nucleic acid molecule described herein.

[0096] In one or more embodiments, the reagents comprise primer molecules and / or probe molecules.

[0097] In one or more embodiments, the reagent comprises a primer molecule that hybridizes to the DNA sequence or its fragment or its transformed sequence. The primer molecule is capable of amplifying the DNA sequence or its fragment or its transformed variant. In one or more embodiments, the primer sequence is methylation-specific or non-specific. The primer molecule is at least 9 bp.

[0098] In one or more embodiments, the reagent is a probe molecule that hybridizes to the DNA sequence or its fragment or its converted sequence. In one or more embodiments, the probe also contains a detectable substance. In one or more embodiments, the detectable substance is a 5' end fluorescent reporter group and a 3' end labeled quencher group. In one or more embodiments, the fluorescent reporter gene is selected from Cy5, FAM and VIC. The probe molecule is at least 12bp.

[0099] In one or more embodiments, the reagent comprises the medium described in any embodiment of the present application.

[0100] In one or more embodiments, the kit is a non-invasive diagnostic kit.

[0101] In one or more embodiments, the subject is a mammal, preferably a human.

[0102] In one or more embodiments, the sample is from a mammalian tissue, cell, or body fluid, such as an intestinal tissue sample, blood, serum, or plasma. The mammal is preferably a human. The sample comprises genomic DNA. Preferably, the sample is blood.

[0103] In one or more embodiments, the DNA sequence is a genomic sequence of a corresponding marker, or a converted sequence thereof, or a sequence thereof treated with a methylation-sensitive restriction endonuclease, wherein the conversion converts unmethylated cytosine to a base with a lower binding ability with guanine than cytosine. The conversion is performed using an enzymatic method, preferably deaminase treatment, or a non-enzymatic method, preferably treatment with bisulfite, bisulfite, or pyrosulfite, or a combination thereof.

[0104] In one or more embodiments, the kit further comprises PCR reaction reagents. Preferably, the PCR reaction reagents include DNA polymerase, PCR buffer, dNTP, Mg 2+ .

[0105] In one or more embodiments, the kit further comprises a reagent for detecting DNA methylation, wherein the reagent is selected from one or more of the following methods: PCR based on bisulfite conversion (e.g., methylation-specific PCR), DNA sequencing (e.g., bisulfite sequencing, whole genome methylation sequencing, simplified methylation sequencing), methylation-sensitive restriction endonuclease analysis, fluorescence quantitative method, methylation-sensitive high-resolution melting curve method, chip-based methylation profile analysis, mass spectrometry (e.g., flight mass spectrometry). Preferably, the reagent is selected from one or more of the following: bisulfite and its derivatives, methylation-sensitive or -insensitive restriction endonucleases, enzyme digestion buffer, fluorescent dye, fluorescence quencher, fluorescent reporter, exonuclease, alkaline phosphatase, internal standard, control.

[0106] On the other hand, the present application also provides a device for screening colorectal cancer risk, diagnosing colorectal cancer, or evaluating the prognosis of colorectal cancer, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the following steps are implemented:

[0107] (1) Obtaining the methylation level of a marker in a sample of a subject, wherein the marker is a DNA sequence and 5 kb upstream and 5 kb downstream of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, wherein the DNA sequence includes one or more or all of the following gene sequences: (p) TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2,

[0108] (2) comparing the methylation level of the marker in step (1) with the corresponding reference level,

[0109] (3) Screen the risk of colorectal cancer, diagnose colorectal cancer, or evaluate the prognosis of colorectal cancer based on the comparison results.

[0110] In one or more embodiments, the DNA sequence comprises the following gene sequence:

[0111] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p).

[0112] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally includes one or more or all of the other gene sequences in (p).

[0113] In one or more embodiments, the DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p).

[0114] In one or more embodiments, the marker comprises at least 3 CpG dinucleotides.

[0115] In one or more embodiments, the DNA sequence comprises a sense strand or an antisense strand of DNA.

[0116] In one or more embodiments, the fragment length is 1-1000bp, preferably 1-700bp. In one or more embodiments, the fragment is the promoter region of a gene. In one or more embodiments, the fragment comprises at least 1, preferably at least 3 CpG dinucleotides. Preferably, the marker has the sequence of a nucleic acid molecule described herein.

[0117] In one or more embodiments, step (1) further includes a step of obtaining DNA, such as DNA extraction and / or quality control.

[0118] In one or more embodiments, step (1) includes using primer molecules, probe molecules and / or media as described in the present application, and optional nucleic acid molecules as described in the present application to detect the methylation level of the sequence in the sample. In one or more embodiments, the detection includes but is not limited to: PCR based on bisulfite conversion, DNA sequencing (such as bisulfite sequencing, whole genome methylation sequencing, simplified methylation sequencing), methylation-sensitive restriction endonuclease analysis, fluorescence quantitative method, methylation-sensitive high-resolution melting curve method, chip-based methylation map analysis, mass spectrometry (such as flight mass spectrometry). In one or more embodiments, the detection is DNA sequencing. Preferably, the sequencing depth of the DNA sequencing is at least 10X, preferably 20X, and more preferably 30X.

[0119] In one or more embodiments, the sample is from a mammalian tissue, cell, or body fluid, such as an intestinal tissue sample, blood, serum, or plasma. The mammal is preferably a human. In one or more embodiments, the sample comprises genomic DNA. Preferably, the sample is blood.

[0120] In one or more embodiments, the DNA sequence is a genomic sequence of a corresponding marker, or a converted sequence thereof, or a sequence thereof treated with a methylation-sensitive restriction endonuclease, wherein the conversion converts unmethylated cytosine to a base that does not bind to guanine. The conversion is performed using an enzymatic method, preferably deaminase treatment, or a non-enzymatic method, preferably treatment with bisulfite, bisulfite, or pyrosulfite, or a combination thereof.

[0121] In one or more embodiments, the comparison in step (2) comprises: directly comparing the methylation level of the marker in step (1) with a reference level, or calculating a score and comparing the score of the methylation level of the marker with a corresponding reference score. Preferably, the score is calculated using a logistic regression model.

[0122] In one or more embodiments, step (3) includes: when the methylation level of the marker is greater than the reference level, or the methylation level score is greater than the reference score, the subject is at risk of developing colorectal cancer, has colorectal cancer, or has a poor prognosis for colorectal cancer.

[0123] On the other hand, the present application provides the use of a reagent for detecting the methylation state or level of at least one CpG dinucleotide of one or more target markers in the preparation of a detection reagent or diagnostic kit for diagnosing gastric cancer, and the use of an apparatus for determining the methylation state or level of at least one CpG dinucleotide of one or more target markers in the preparation of a diagnostic kit for diagnosing gastric cancer; wherein the one or more target markers are selected from any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47 or all 48 sequences in the following sequences (1)-(48):

[0124] (1) containing chr6:166970625:166970825 (SEQ ID NO:48) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0125] (2) containing chr11:11600237:11600617 (SEQ ID NO:49) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0126] (3) containing chr17:76929754:76929954 (SEQ ID NO: 50) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0127] (4) containing chr6:391738:391938 (SEQ ID NO:51) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0128] (5) containing chr12:2282090:2282290 (SEQ ID NO:52) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0129] (6) containing chr2:177030134:177030449 (SEQ ID NO:53) and sequences within 5 kb upstream and / or 5 kb downstream;

[0130] (7) containing chr7:35301095:35301411 (SEQ ID NO:54) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0131] (8) containing chr7:8482114:8482413 (SEQ ID NO:55) and sequences within 5 kb upstream and / or 5 kb downstream;

[0132] (9) containing chr2:72371208:72371433 (SEQ ID NO:56) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0133] (10) containing chr5:134364359:134364559 (SEQ ID NO:57) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0134] (11) containing chr10:118892523:118892723 (SEQ ID NO:58) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0135] (12) containing chr12:113901298:113901498 (SEQ ID NO:59) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0136] (13) containing chr8:143613755:143613955 (SEQ ID NO:60) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0137] (14) containing chr8:20375580:20375780 (SEQ ID NO:61) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0138] (15) containing chr7:107499318:107499518 (SEQ ID NO:62) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0139] (16) containing chr6:1378941:1379141 (SEQ ID NO:63) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0140] (17) containing chr15:34786976:34787337 (SEQ ID NO: 64) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0141] (18) containing chr1:156405314:156405514 (SEQ ID NO:65) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0142] (19) containing chr8:10588811:10589173 (SEQ ID NO:66) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0143] (20) containing chr4:85418610:85418919 (SEQ ID NO:67) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0144] (21) containing chr5:140871317:140871517 (SEQ ID NO:68) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0145] (22) containing chr5:92906255:92906617 (SEQ ID NO:69) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0146] (23) containing chr14:57265398:57265598 (SEQ ID NO: 70) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0147] (24) containing chr19:19650947:19651147 (SEQ ID NO:71) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0148] (25) containing chr11:20618486:20618686 (SEQ ID NO: 72) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0149] (26) containing chr7:73407894:73408161 (SEQ ID NO:73) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0150] (27) containing chr16:82660460:82660774 (SEQ ID NO:74) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0151] (28) containing chr13:24844736:24844936 (SEQ ID NO:75) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0152] (29) containing chr20:55500358:55500677 (SEQ ID NO:76) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0153] (30) containing chr10:123923943:123924143 (SEQ ID NO:77) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0154] (31) containing chr20:59827678:59827907 (SEQ ID NO:78) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0155] (32) containing chr20:62330559:62330808 (SEQ ID NO: 79) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0156] (33) containing chr19:13209774:13209974 (SEQ ID NO:80) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0157] (34) containing chr16:2085778:2086156 (SEQ ID NO:81) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0158] (35) containing chr6:108488634:108488917 (SEQ ID NO:82) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0159] (36) containing chr12:115124911:115125191 (SEQ ID NO:83) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0160] (37) containing chr10:124896740:124897020 (SEQ ID NO:84) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0161] (38) containing chr14:55243006:55243206 (SEQ ID NO: 85) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0162] (39) containing chr13:36729096:36729334 (SEQ ID NO: 86) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0163] (40) containing chr2:10444997:10445197 (SEQ ID NO:87) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0164] (41) containing chr9:2157701:2157901 (SEQ ID NO:88) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0165] (42) containing chr12:57529619:57529819 (SEQ ID NO: 89) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0166] (43) containing chr1:119527250:119527450 (SEQ ID NO:90) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0167] (44) containing chr1:119532788:119532988 (SEQ ID NO:91) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0168] (45) containing chr15:96909441:96909641 (SEQ ID NO:92) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0169] (46) containing chr1:146551463:146551747 (SEQ ID NO:93) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0170] (47) containing chr17:35293755:35293955 (SEQ ID NO: 94) or sequences within 5 kb upstream and downstream thereof; and

[0171] (48) Containing chr17:59482763:59482963 (SEQ ID NO: 95) or sequences within 5 kb upstream and downstream thereof.

[0172] In one or more embodiments, the one or more gastric cancer target markers include the sequences described in items (3), (8), (13), (15), (17), (19), (22), (25), (29), (31), (37), (38), (40), (41), (42), (43), (45), (47) and (48).

[0173] In one or more embodiments, the one or more gastric cancer target markers include the sequences described in items (2), (6), (7), (8), (12), (15), (19), (25), (28), (32), (33), (36), (37), (40), (42), (43), (44), (46) and (48).

[0174] In one or more embodiments, the one or more gastric cancer target markers include the sequences described in items (3), (13), (14), (20), (22), (28), (30) and (36); or

[0175] In one or more embodiments, the one or more gastric cancer target markers include the sequences described in items (3), (13), (27), (30) and (35).

[0176] In one or more embodiments, the one or more gastric cancer target markers include the sequences described in items (7), (14), (22), (26), (35), (38), (40), (43), (47) and (48).

[0177] In one or more embodiments, the one or more gastric cancer target markers are selected from the sequences described in any 1, 2, 3, 4, 5, 6, 7, 8 or 9 of items (7), (14), (22), (26), (35), (38), (40), (43), (47) and (48).

[0178] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (40), and any one or more sequences of items (1)-(39) and (41)-(48).

[0179] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (47), and any one or more sequences of items (1)-(46) and (48).

[0180] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (43), and any one or more sequences of items (1)-(42) and (44)-(48).

[0181] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (26), and any one or more sequences of items (1)-(25) and (27)-(48).

[0182] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (35), and any one or more sequences of items (1)-(34) and (36)-(48).

[0183] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (14), and any one or more sequences of items (1)-(13) and (15)-(48).

[0184] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (38), and any one or more sequences of items (1)-(37) and (39)-(48).

[0185] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (22), and any one or more sequences of items (1)-(21) and (23)-(48).

[0186] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (7), and any one or more sequences of items (1)-(6) and (8)-(48).

[0187] In one or more embodiments, the gastric cancer target marker includes the sequence described in item (48), and any one or more sequences among (1)-(47).

[0188] In one or more embodiments, the gastric cancer target marker includes a sequence within 1 kb upstream of each starting site of any one of the sequences in SEQ ID NO: 48-95, preferably within 500 bp, more preferably within 300 bp, more preferably within 100 bp and / or a sequence within 1 kb downstream of each end site, preferably within 500 bp, preferably within 300 bp, preferably within 100 bp; preferably, the target marker is a gene sequence containing any one of the sequences in SEQ ID NO: 48-95 and having a length of less than 400 bp.

[0189] In one or more embodiments, the sequences described in items (1) to (48) are the sequences shown in SEQ ID NOs: 48-95, respectively.

[0190] On the other hand, the present application provides the use of a reagent for detecting the methylation state or level of at least one CpG dinucleotide of one or more target markers in the preparation of a detection reagent or diagnostic kit for diagnosing esophageal cancer, and the use of a device for determining the methylation state or level of at least one CpG dinucleotide of one or more target markers in the preparation of a diagnostic kit for diagnosing esophageal cancer; wherein the one or more esophageal cancer target markers are selected from any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42 or all 43 sequences in SEQ ID NO: 96-138, and sequences within 5 kb upstream and / or within 5 kb downstream thereof. In one or more embodiments, the target marker includes a sequence within 1 kb upstream of each starting site of any one of the sequences in SEQ ID NOs: 96-138, preferably within 500 bp, more preferably within 300 bp, more preferably within 100 bp, and / or a sequence within 1 kb downstream of each end site, preferably within 500 bp, preferably within 300 bp, preferably within 100 bp; preferably, the target marker is a gene sequence containing any one of the sequences in SEQ ID NOs: 96-138 and having a length of less than 400 bp.

[0191] In another aspect, the present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of a DNA region selected from the chromosome range numbered 1 to 43 in Table 1 below, or a complementary region thereof, or a fragment thereof, in a test sample:

[0192] Table 1

[0193] On the other hand, the present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of the modification state of a DNA region within 5k bp upstream or downstream of any one of SEQ ID NOs: 96 to 138, or its complementary region, or the above-mentioned fragment in a test sample.

[0194] On the other hand, the present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of the modification state of a DNA region or a fragment thereof selected from a region within 5k bp upstream or downstream of SEQ ID NO: 105 and a gene numbered 1 to 76 in Table 2 below, in a test sample.

[0195] Table 2

[0196] On the other hand, the present application provides a nucleic acid comprising the presence and / or content of a modified state capable of binding to a DNA region selected from chromosome range numbers 1 to 43 in Table 1 above, or its complementary region, or the above-mentioned fragment.

[0197] On the other hand, the present application provides a nucleic acid comprising the presence and / or content of a modified state capable of binding to a DNA region within 5k bp upstream or downstream of any one of SEQ ID NOs: 96 to 138, or its complementary region, or the above-mentioned fragment.

[0198] On the other hand, the present application provides a nucleic acid comprising a region within 5k bp upstream or downstream of SEQ ID NO: 105 and a DNA region where genes numbered 1 to 76 in Table 2 above of the present application are located, or the presence and / or content of the modification state of a fragment thereof.

[0199] On the other hand, the present application provides a kit comprising the nucleic acid described in the present application.

[0200] On the other hand, the present application provides a method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region, or its complementary region, or the above-mentioned transformed region, or the above-mentioned fragment based on the modification state of the DNA region selected from the chromosome range numbered 1 to 43 mentioned in the present application, or the above-mentioned fragment.

[0201] On the other hand, the present application provides a method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region within 5k bp upstream or downstream of any one of SEQ ID NOs: 96 to 138, or its complementary region, or the above-mentioned fragment based on the modification state of the DNA region, or its complementary region, or the above-mentioned converted region, or the above-mentioned fragment.

[0202] On the other hand, the present application provides a method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a region selected from within 5 kbp upstream or downstream of SEQ ID NO: 105 and a DNA region or a fragment thereof in which genes numbered 1 to 76 in Table 2 above are located, or the modification state of the DNA region or a fragment thereof.

[0203] On the other hand, the present application provides nucleic acids, nucleic acid groups and / or kits for determining the modification status of DNA regions, and their use in preparing substances for assessing the presence and / or progression of esophageal cancer, wherein the DNA regions to be determined comprise the modification status of DNA regions numbered 1 to 43 in the chromosome range of Table 1 above, or their complementary regions, or the above-mentioned fragments, and sequences designed to be able to bind to the DNA regions, or their complementary regions, or the above-mentioned transformed regions, or the above-mentioned fragments.

[0204] On the other hand, the present application provides nucleic acids, nucleic acid groups and / or kits for determining the modification status of DNA regions, and their use in preparing substances for evaluating the presence and / or progression of esophageal cancer, wherein the DNA region for determination comprises a DNA region within 5k bp upstream or downstream of any one of SEQ ID NOs: 96 to 138, or its complementary region, or the modification status of the above-mentioned fragment, and a sequence designed to be able to bind to the DNA region, or its complementary region, or the above-mentioned converted region, or the above-mentioned fragment.

[0205] On the other hand, the present application provides nucleic acids, nucleic acid groups and / or kits for determining the modification status of DNA regions, and their use in preparing substances for assessing the presence and / or progression of esophageal cancer, wherein the DNA region for determination comprises a region within 5k bp upstream or downstream of SEQ ID NO: 105 and the modification status of the DNA region or fragments thereof where the genes numbered 1 to 76 in Table 2 above of the present application are located, and the sequence is designed to be able to bind to the DNA region, or its complementary region, or the converted region or the fragment thereof.

[0206] On the other hand, the present application provides the use of a reagent for detecting the methylation state or level of at least one CpG dinucleotide of one or more target markers in the preparation of a detection reagent or diagnostic kit for diagnosing liver cancer, and the use of a device for determining the methylation state or level of at least one CpG dinucleotide of one or more target markers in the preparation of a diagnostic kit for diagnosing liver cancer; wherein, the one or more liver cancer target markers are selected from SEQ ID NO: 139-340, any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117 17, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163 , 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201 or all 202 sequences, and sequences within 5 kb upstream and / or within 5 kb downstream thereof.In one or more embodiments, the target marker includes a sequence within 1 kb upstream of each start site of any one of the sequences in SEQ ID NOs: 139-340, preferably within 500 bp, more preferably within 300 bp, more preferably within 100 bp, and / or a sequence within 1 kb downstream of each end site, preferably within 500 bp, preferably within 300 bp, preferably within 100 bp; preferably, the target marker is a gene sequence containing any one of the sequences in SEQ ID NOs: 139-340 and having a length of less than 400 bp.

[0207] On the other hand, the present application provides a method for assessing the presence and / or progression of liver cancer, comprising determining the presence and / or amount of a DNA region within 5k bp upstream or downstream of chromosome range numbered 44 to 245 as shown in Table 3 below, or its complementary region, or the modification status of the aforementioned fragment in a test sample.

[0208] Table 3

[0209] The present application provides a method for assessing the presence and / or progression of liver cancer, comprising determining the presence and / or amount of a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340, or a complementary region thereof, or a modification state of the aforementioned fragment in a test sample.

[0210] The present application provides a method for assessing the presence and / or progression of liver cancer, comprising determining the presence and / or amount of the modification state of a DNA region within 5k bp upstream or downstream of a gene selected from genes numbered 77 to 354 in Table 4, or a fragment thereof in a test sample.

[0211] Table 4

[0212] The present application provides a nucleic acid comprising the ability to bind to a DNA region within 5k bp upstream or downstream of chromosome range numbered 44 to 245 selected from Table 3 above, or its complementary region, or the presence and / or content of the modification state of the above-mentioned fragment.

[0213] The present application provides a nucleic acid comprising the presence and / or content of a modified state capable of binding to a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340, or a complementary region thereof, or a fragment thereof.

[0214] The present application provides a nucleic acid comprising the presence and / or content of a modified state of a DNA region within 5k bp upstream or downstream of a gene numbered 77 to 354 in Table 4 above, or a fragment thereof.

[0215] The present application provides a kit comprising the nucleic acid described in the present application.

[0216] The present application provides a method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region within 5k bp upstream or downstream of chromosome range numbered 44 to 245 selected from Table 3 above, or its complementary region, or the above-mentioned fragment, or the modification state of the above-mentioned fragment.

[0217] The present application provides a method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340, or a complementary region thereof, or the modified state of the fragment thereof.

[0218] The present application provides a method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region within 5 kbp upstream or downstream of a gene numbered 77 to 354 in Table 4 above, or a fragment thereof, based on the modification state of the DNA region, or its complementary region, or the converted region, or the fragment thereof.

[0219] The present application provides nucleic acids, nucleic acid groups and / or kits for determining the modification status of DNA regions, and their use in preparing substances for assessing the presence and / or progression of liver cancer, wherein the nucleic acids, nucleic acid groups and / or kits for determining the modification status of DNA regions within 5k bp upstream or downstream comprise a DNA region within 5k bp upstream or downstream of chromosome range numbers 44 to 245 selected from Table 3 above, or its complementary region, or the above-mentioned fragment, and are designed to bind to the DNA region, or its complementary region, or the above-mentioned converted region, or the above-mentioned fragment sequence.

[0220] The present application provides nucleic acids, nucleic acid groups and / or kits for determining the modification status of a DNA region, and their use in preparing substances for assessing the presence and / or progression of liver cancer, wherein the nucleic acids, nucleic acid groups and / or kits for determining the modification status of a DNA region within 5k bp upstream or downstream comprise a DNA region within 5k bp upstream or downstream selected from any one of SEQ ID NOs: 139 to 340, or a complementary region thereof, or a fragment thereof, and a sequence designed to be capable of binding to the DNA region, or a complementary region thereof, or a converted region, or a fragment thereof.

[0221] The present application provides nucleic acids, nucleic acid groups and / or kits for determining the modification status of DNA regions, and their use in preparing substances for assessing the presence and / or progression of liver cancer. The methods for determining the modification status of DNA regions within 5k bp upstream or downstream include DNA regions within 5k bp upstream or downstream of genes numbered 77 to 354 in the present application, or fragments thereof, and are designed to bind to the DNA regions, or their complementary regions, or the converted regions, or the sequences of the fragments thereof.

[0222] In one or more embodiments, the reagents include primers and / or probe molecules; preferably, the primer molecules are identical to, complementary to, or hybridize to the one or more target markers under stringent conditions and contain at least 9 consecutive nucleotides, and the probe molecules hybridize to the amplified products of the one or more target markers under stringent conditions.

[0223] In one or more embodiments, the reagents are reagents required for implementing genome-reduced methylation sequencing technology.

[0224] On the other hand, the present application also provides a diagnostic reagent or diagnostic kit for detecting the methylation state or methylation level of at least one CpG dinucleotide of one or more target markers to diagnose cancer, which comprises a reagent for detecting the methylation state or level of at least one CpG dinucleotide of one or more target markers; wherein, the one or more target markers are as described above.

[0225] In one or more embodiments, the diagnostic reagent or diagnostic kit comprises primers and / or probe molecules; preferably, the primer molecules are identical to, complementary to, or hybridize under stringent conditions to the one or more target markers and contain at least 9 consecutive nucleotides, and the probe molecules hybridize to the amplified products of the one or more target markers under stringent conditions.

[0226] In one or more embodiments, the diagnostic reagent or diagnostic kit further comprises primer molecules and / or probe molecules for detecting the internal reference gene ACTB.

[0227] In one or more embodiments, the diagnostic reagent or diagnostic kit further comprises one or more substances selected from the group consisting of: PCR buffer, polymerase, dNTP, restriction endonuclease, enzyme digestion buffer, fluorescent dye, fluorescence quencher, fluorescent reporter, exonuclease, alkaline phosphatase, internal standard, control, KCl, MgCl2 and (NH4)2SO4.

[0228] In one or more embodiments, the reagents also include reagents used in one or more of the following methods: PCR based on bisulfite conversion, DNA sequencing, methylation-sensitive restriction endonuclease analysis, fluorescence quantification, methylation-sensitive high-resolution melting curve method, chip-based methylation profiling and mass spectrometry.

[0229] In one or more embodiments, the reagent is selected from one or more of the following: bisulfite and its derivatives, fluorescent dyes, fluorescence quenchers, fluorescent reporters, internal standards, and controls.

[0230] On the other hand, the present application also provides the use of at least one reagent or a set of reagents for distinguishing between methylated and unmethylated CpG dinucleotides in at least one target region of genomic DNA in the preparation of a kit for a method for detecting and / or classifying cancer in an individual, wherein the method comprises contacting genomic DNA isolated from a biological sample of the individual with the at least one reagent or set of reagents, wherein the target region is identical, equivalent or complementary to a sequence of at least 16 consecutive nucleotides of one or more target markers, wherein the consecutive nucleotides contain at least one CpG dinucleotide sequence, thereby at least partially providing detection and / or classification of cancer, wherein the one or more target markers are as described above.

[0231] In another aspect, the present application also provides the use of one or more reagents for converting unmethylated cytosine base at position 5 to uracil or other bases detectably different from cytosine in hybridization properties, an amplification enzyme, and at least one primer comprising at least 9 consecutive nucleotides in the preparation of a kit for a method for detecting and / or classifying gastric cancer in an individual, wherein the method comprises:

[0232] a) isolating genomic DNA from a biological sample of the individual;

[0233] b) treating the genomic DNA or fragments thereof of a) with the one or more reagents;

[0234] c) contacting the treated genomic DNA or treated fragments thereof with the amplification enzyme and the at least one primer that is identical to, complementary to, or hybridizes under stringent conditions to one or more target markers, wherein the treated genomic DNA or fragments thereof is amplified to produce at least one amplification product or is not amplified; and

[0235] d) determining the methylation status or level of at least one CpG dinucleotide of the one or more target markers, or a mean or value reflecting the average methylation status or level of a plurality of CpG dinucleotides of the one or more target markers, based on the presence or nature of the amplicon, thereby at least in part detecting and / or classifying cancer in the individual;

[0236] Wherein, the one or more target markers are as described above.

[0237] In one or more embodiments, wherein in step b), the genomic DNA or fragments thereof is treated with a reagent selected from the group consisting of bisulfite, acid sulfite, metabisulfite, and combinations thereof.

[0238] In one or more embodiments, in c), the contacting or amplification of nucleic acid molecules is performed by using a thermostable DNA polymerase as the amplification enzyme, using a polymerase lacking 5'-3' exonuclease activity, using polymerase chain reaction, and / or generating an amplification product with a detectable label.

[0239] In one or more embodiments, the contacting or amplifying in c) comprises using methylation-specific primers.

[0240] In another aspect, the present application also provides use of one or more methylation-sensitive restriction enzymes and amplification enzymes and at least one primer comprising at least 9 consecutive nucleotides in preparing a kit for a method for detecting and / or classifying cancer in an individual, wherein the primers are identical to, complementary to, or hybridize under stringent conditions to one or more target markers; the method comprising:

[0241] a) isolating genomic DNA from a biological sample of the individual;

[0242] b) digesting the genomic DNA or fragment thereof described in a) with the one or more methylation-sensitive restriction enzymes, and contacting the resulting digestion product with the amplification enzyme and the at least one primer; and

[0243] c) determining the methylation status or level of at least one CpG dinucleotide of the one or more target markers based on the presence or nature of the amplicon, thereby at least in part detecting and / or classifying cancer in the individual;

[0244] Wherein, the one or more target markers are as described above.

[0245] In one or more embodiments, the presence or absence of an amplification product is determined by hybridizing at least one nucleic acid or peptide nucleic acid that is identical or complementary to a fragment of at least 16 bases long selected from the sequence of the one or more target markers.

[0246] In another aspect, the present application also provides a method for detecting and / or classifying cancer in an individual, the method comprising the steps of:

[0247] a) isolating genomic DNA from a biological sample of the individual;

[0248] b) b1) treating the genomic DNA or fragments thereof of a) with one or more reagents that convert the unmethylated cytosine base at position 5 into uracil or other bases that are detectably different from cytosine in hybridization properties; or b2) digesting the genomic DNA or fragments thereof of a) with one or more methylation-sensitive restriction enzymes,

[0249] c) contacting the treated or digested products obtained in b) with an amplification enzyme and at least one primer comprising at least 9 consecutive nucleotides that is identical to, complementary to, or hybridizes under stringent conditions to one or more target markers, wherein the treated or digested products are amplified to produce at least one amplification product or are not amplified; and

[0250] d) determining the methylation status or level of at least one CpG dinucleotide of the one or more target markers, or a mean or value reflecting the average methylation status or level of a plurality of CpG dinucleotides of the one or more target markers, based on the presence or nature of the amplicon, thereby at least in part detecting and / or classifying cancer in the individual;

[0251] Wherein, the one or more target markers are as described above.

[0252] In one or more embodiments, in step b1), the genomic DNA or fragments thereof is treated with a reagent selected from the group consisting of bisulfite, acid sulfite, metabisulfite, and combinations thereof.

[0253] In one or more embodiments, in c), the contacting or amplification of nucleic acid molecules is performed by using a thermostable DNA polymerase as the amplification enzyme, using a polymerase lacking 5'-3' exonuclease activity, using polymerase chain reaction, and / or generating amplification products with a detectable label.

[0254] In one or more embodiments, the contacting or amplifying in c) comprises using methylation-specific primers.

[0255] In one or more embodiments, the presence or absence of an amplification product is determined by hybridizing at least one nucleic acid or peptide nucleic acid that is identical, identical, or complementary to a fragment of at least 16 bases long of a sequence selected from the one or more target markers.

[0256] On the other hand, the present application also provides the use of a treated nucleic acid derived from one or more target markers in the preparation of a kit for diagnosing cancer, wherein the treatment is suitable for converting at least one unmethylated cytosine base of the one or more target markers to uracil or other bases that are detectably different from cytosine in hybridization, and the one or more target markers are as described above.

[0257] On the other hand, the present application also provides a device for detecting and diagnosing individual cancer, the device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the following steps when executing the program: (1) obtaining the methylation level or methylation status of at least one CpG dinucleotide of one or more target markers in a sample, and (2) interpreting cancer based on the methylation level or methylation status of (1); wherein the one or more target markers are as described above.

[0258] On the other hand, the present application provides a storage medium recording a program capable of running the method described in the present application.

[0259] On the other hand, the present application provides a device comprising the storage medium described in the present application, and optionally also comprising a processor coupled to the storage medium, the processor being configured to execute based on the program stored in the storage medium to implement the method described in the present application.

[0260] Those skilled in the art can easily discern other aspects and advantages of the present application from the detailed description below. In the detailed description below, only exemplary embodiments of the present application are shown and described. As will be appreciated by those skilled in the art, the content of this application enables those skilled in the art to modify the disclosed specific embodiments without departing from the spirit and scope of the invention to which this application relates. Accordingly, the descriptions in the drawings and specification of this application are merely exemplary and not restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0261] The specific features of the invention involved in this application are shown in the appended claims. The features and advantages of the invention involved in this application can be better understood by referring to the exemplary embodiments described in detail below and the accompanying drawings. A brief description of the drawings is as follows:

[0262] Figure 1 shows the process of screening methylation markers for colorectal cancer.

[0263] Figure 2 shows the distribution of methylation levels in colorectal cancer and non-colorectal cancer samples in the training set.

[0264] FIG3 shows the distribution of methylation levels of colorectal cancer and non-colorectal cancer samples in the test set.

[0265] Figure 4 shows the distribution of ALLMODEL prediction scores for colorectal cancer.

[0266] Figure 5 shows the ALLMODEL ROC curve for colorectal cancer.

[0267] Figure 6 shows the distribution of colorectal cancer SUBMODEL1 prediction scores.

[0268] FIG7 shows the ROC curve of SUBMODEL1 for colorectal cancer.

[0269] Figure 8 shows the distribution of colorectal cancer SUBMODEL2 prediction scores.

[0270] Figure 9 shows the ROC curve of SUBMODEL2 for colorectal cancer.

[0271] FIG10 shows the distribution of colorectal cancer SUBMODEL3 prediction scores.

[0272] FIG11 shows the ROC curve of SUBMODEL3 for colorectal cancer.

[0273] FIG12 shows a flowchart of the performance evaluation of a single methylation marker for gastric cancer.

[0274] FIG13 shows the distribution of model prediction scores of the model constructed for all target markers of gastric cancer in the training set and test set samples.

[0275] FIG14 shows the ROC curve of the model constructed by all target markers of gastric cancer for diagnosing gastric cancer in the training set and test set samples.

[0276] FIG15 shows the distribution of model prediction scores of the model constructed by gastric cancer markers in the training set and test set samples.

[0277] FIG16 shows the ROC curve of the model constructed by gastric cancer markers for diagnosing gastric cancer in the training set and test set samples.

[0278] FIG17 shows the distribution of model prediction scores of the gastric cancer marker constructed model in the training set and test set samples.

[0279] FIG18 shows the ROC curve of the model constructed by gastric cancer markers for diagnosing gastric cancer in the training set and test set samples.

[0280] FIG19 shows the distribution of model prediction scores of the model constructed by gastric cancer markers in the training set and test set samples.

[0281] FIG20 shows the ROC curve of the model constructed by gastric cancer markers for diagnosing gastric cancer in the training set and test set samples.

[0282] FIG21 shows the distribution of model prediction scores of the model constructed by gastric cancer markers in the training set and test set samples.

[0283] FIG22 shows the ROC curve of the model constructed by gastric cancer markers for diagnosing gastric cancer in the training set and test set samples.

[0284] FIG23 shows the ROC curve of the prediction model for diagnosing esophageal cancer.

[0285] FIG24 shows the distribution of prediction scores of the esophageal cancer prediction model in each group.

[0286] FIG25 shows the ROC curve of the prediction model for diagnosing esophageal cancer using a combination of 16 esophageal cancer methylation markers.

[0287] FIG26 shows the distribution of prediction scores of the prediction model of the combination of 16 esophageal cancer methylation markers in each group.

[0288] FIG27 shows the ROC curve of the prediction model for diagnosing esophageal cancer using a combination of 16 esophageal cancer methylation markers.

[0289] FIG28 shows the distribution of prediction scores of the prediction model of the combination of 16 esophageal cancer methylation markers in each group.

[0290] FIG29 shows the ROC curve of the prediction model for diagnosing esophageal cancer.

[0291] FIG30 shows the distribution of prediction scores of the esophageal cancer prediction model in each group.

[0292] FIG31 shows the ROC curve of the prediction model for diagnosing esophageal cancer using a combination of seven esophageal cancer methylation markers.

[0293] FIG32 shows the distribution of prediction scores of the prediction model of the combination of 7 esophageal cancer methylation markers in each group.

[0294] FIG33 shows the ROC curve of the prediction model for diagnosing esophageal cancer using a combination of seven esophageal cancer methylation markers.

[0295] FIG34 shows the distribution of prediction scores of the prediction model of the combination of 7 esophageal cancer methylation markers in each group.

[0296] FIG35 shows the ROC curve of the prediction model for diagnosing esophageal cancer.

[0297] FIG36 shows the distribution of prediction scores of the esophageal cancer prediction model in each group.

[0298] FIG37 shows the ROC curve of the prediction model for diagnosing esophageal cancer using a combination of 17 esophageal cancer methylation markers.

[0299] FIG38 shows the distribution of prediction scores of the prediction model of the combination of 17 esophageal cancer methylation markers in each group.

[0300] FIG39 shows the ROC curve of the prediction model for diagnosing esophageal cancer using a combination of 15 esophageal cancer methylation markers.

[0301] FIG40 shows the distribution of prediction scores of the prediction model of the combination of 15 esophageal cancer methylation markers in each group.

[0302] FIG41 shows the ROC curve of the prediction model for diagnosing liver cancer.

[0303] FIG42 shows the distribution of prediction scores of the liver cancer prediction model in each group.

[0304] FIG43 shows the ROC curve of the prediction model for diagnosing liver cancer using a combination of 25 liver cancer methylation markers.

[0305] FIG44 shows the distribution of prediction scores of the prediction model for the combination of 25 liver cancer methylation markers in each group.

[0306] FIG45 shows the ROC curve of the prediction model for diagnosing liver cancer using a combination of 52 liver cancer methylation markers.

[0307] FIG46 shows the distribution of prediction scores of the prediction model for the combination of 52 liver cancer methylation markers in each group. DETAILED DESCRIPTION

[0308] The following describes the implementation of the present invention through specific embodiments. People familiar with this technology can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

[0309] Definition of terms

[0310] It should be noted that, in the specification and claims of this application, the singular forms "a", "an", and "the" include plural forms thereof unless the context indicates otherwise. Thus, for example, "an agent" includes a plurality of agents.

[0311] In the specification and claims of this application, unless otherwise stated, the terms "comprise", "include" or "contain" mean that the listed values, steps or components are included, but other values, steps or components are not excluded.

[0312] As used in this application, the term "methylation marker" refers to such a target nucleic acid or gene region, a methylation site: its methylation level or the score of the calculation model based on the methylation level indicates the cancer state. As used herein, the term "target marker" refers to such a target nucleic acid or gene region: its methylation level indicates whether the subject suffers from cancer. The term "methylation marker" or "target marker" should be considered to include all its transcriptional variants and all its promoters and regulatory elements. As understood by those skilled in the art, it is known that certain genes show allelic variation or single nucleotide polymorphism ("SNP") between individuals. SNP includes insertions and deletions of simple repeat sequences (such as dinucleotide and trinucleotide repeats) of different lengths. Therefore, this application should be understood to be extended to all forms of markers / genes produced by any other mutation, polymorphism or allelic variation. In addition, it should be understood that the term "methylation marker" should include both the positive strand sequence of the marker or gene and the antisense strand sequence of the marker or gene.

[0313] The term "methylation marker" or "target marker" used in the present application is broadly interpreted as including 1) the original marker (being in specific methylation) found in biological sample or genomic DNA, and also including 2) its processed sequence (for example the corresponding region after bisulfite conversion or the corresponding region after methylation-sensitive restriction endonuclease MSRE is processed). The corresponding region after bisulfite conversion is different from the target marker in the genomic sequence in that one or more unmethylated cytosine residues are converted into uracil bases, thymine bases or other bases different from cytosine in hybridization behavior. The corresponding region processed through MSRE is different from the target marker in the genomic sequence in that the sequence is cut at one or more MSRE cleavage sites. The methylation marker or target marker of the present application also include non-enzymatic conversion (such as the corresponding region after bisulfite conversion), and the corresponding region obtained after enzymatic conversion (such as MSRE conversion).

[0314] In some embodiments, the target marker of the present application also includes various variants of the above-mentioned each gene. Variant includes a nucleic acid sequence from the same region, with a gene or region as described herein having at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% sequence identity (that is, with one or more deletions, insertions, substitutions, reverse sequences, etc.). Therefore, the present application should be understood to extend to such variants that achieve the same result, despite the fact that the actual nucleic acid sequence between individuals has slight genetic variation.

[0315] As used herein, the term "percentage (%) of sequence identity" refers to the percentage of identical amino acid (or nucleic acid) residues in a candidate sequence and the amino acid (or nucleic acid) residues in a reference sequence after sequence alignment, wherein intervals may be introduced (if necessary) to maximize the number of identical amino acids (or nucleic acids). In other words, the percentage (%) of sequence identity of an amino acid sequence (or nucleic acid sequence) can be calculated by dividing the number of identical amino acid residues (or bases) in the candidate sequence or the reference sequence by the total number of amino acid residues (or bases), whichever is shorter, in the amino acid sequence. Conservative substitutions of amino acid residues may or may not be considered to be identical residues. The percentage of amino acid (or nucleic acid) sequence identity can be determined by, for example, using publicly available tools such as BLASTN, BLASTp (available on the website of the National Center for Biotechnology Information (NCBI), see also Altschul SF et al., J. Mol. Biol., 215:403–410 (1990); Stephen F et al., Nucleic Acids Res., 25:3389–3402 (1997)), ClustalW2 (available on the website of the European Bioinformatics Institute), see also Higgins D et al., Methods in Enzymology, 266:383-402 (1996); Larkin MA et al., Bioinformatics (Oxford, England), 23(21):2947-8 (2007)) and ALIGN or Megalign (DNASTAR) software. Those skilled in the art can use the default parameters provided by the tool, or can customize parameters suitable for the alignment (e.g., by selecting an appropriate algorithm).

[0316] The methylation markers or target markers of the present application also include the corresponding regions 5 kb upstream of the start site and 5 kb downstream of the end site of the above-mentioned gene after non-enzymatic conversion (such as bisulfite conversion) or the corresponding regions after enzymatic treatment (such as methylation-sensitive restriction enzyme treatment).

[0317] The "methylation level" described in this application refers to the methylation level of the CpG site involved or the average methylation level of multiple or all CpG sites in the sequence involved. In an exemplary embodiment of the present invention, the methylation level of a site generally refers to the percentage of methylated C at the site. If all Cs in the CpG site are unmethylated, their methylation level is zero. The methylation level can also be other types of calculation results, which are within the knowledge of those skilled in the art. In addition, an increase or decrease in the methylation level of a sequence does not mean that the methylation levels of all CpG sites in the region increase or decrease. It is known in the art that the process of converting the results obtained by the method for detecting DNA methylation (such as simplified methylation sequencing) into methylation levels. For example, according to the methylation level of the CpG sites detected in each gene promoter region, the average methylation is calculated and used as the DNA methylation level of the gene promoter region. In some embodiments, the methylation level is obtained by MethylTitan (CN201910515830, Xunyuan) methylation sequencing method. The methylation level can be standardized.

[0318] The "methylation information" described in this application includes characteristic information related to the cytosine that may be methylated in the sequence. The cytosine that may be methylated is usually the C in CpG. Such features include, but are not limited to: whether any cytosine (C) residue in the sequence is methylated, the position and / or methylation level of one or more methylation sites (such as CpG dinucleotides), the methylation level of any specific region of the nucleic acid, the frequency or percentage of methylated C, the relative concentration, absolute concentration or pattern of methylated C or unmethylated C, the methylation haplotype ratio (MHL), the average methylation level (AMF), and the methylation allele difference caused by, for example, the difference in allele starting points. For example, if one or more cytosine (C) residues in the nucleic acid sequence are methylated, it can be referred to as "hypermethylation" or having "increased methylation", and if one or more cytosine (C) residues in the DNA sequence are unmethylated, it can be referred to as "demethylation" or having "reduced methylation".

[0319] The methylation level of the gene being measured can be mathematically analyzed to obtain a score. The term "methylation score" represents the numerical value obtained by calculating the methylation level using a mathematical method (e.g., a mathematical model). For the sample being detected, when the score is greater than a threshold value, the result is determined to be positive, i.e., cancer or having a risk of cancer or a poor prognosis for cancer, otherwise it is negative. The method of conventional mathematical analysis and the process of determining the threshold value are known in the art. An exemplary method is a mathematical model, including but not limited to a regression model, a support vector machine, a random forest, etc. For example, for differential methylation markers, a support vector machine (SVM) is constructed for the training group samples, and the accuracy, sensitivity, specificity, and area under the predictive value characteristic curve (AUC) of the model statistics test results are used to calculate the test set sample prediction score. For another example, a logistic regression (Logistic Regression) is constructed for the methylation level of the differential methylation marker, and the accuracy, sensitivity, specificity, and area under the predictive value characteristic curve (AUC) of the model statistics test results are used to calculate the test set sample prediction score.

[0320] The term "subject" or "individual" as used herein includes humans and non-human animals. Non-human animals include all vertebrates, such as mammals and non-mammals. In some embodiments, the subject is a human.

[0321] In this application, the term "gene" includes coding sequences and non-coding sequences of the gene in question on the genome, wherein the non-coding sequences include introns, promoters, and regulatory elements or sequences.

[0322] Molecular diagnosis in the present invention includes not only early diagnosis of cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer, and / or liver cancer), but also late diagnosis of cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer, and / or liver cancer), and also includes cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer, and / or liver cancer) screening, risk assessment, prognosis, and disease identification. Early diagnosis refers to the possibility of detecting cancer before it develops and / or metastasizes, preferably before morphological changes in tissues or cells are observed.

[0323] The term "variant" or "mutant" herein refers to a polynucleotide whose nucleic acid sequence has been altered by the insertion, deletion, or substitution of one or more nucleotides compared to a reference sequence, while retaining its ability to hybridize with other nucleic acids. Mutants described in any embodiment of the present application include nucleotide sequences that have at least 70%, preferably at least 80%, preferably at least 85%, preferably at least 90%, preferably at least 95%, and preferably at least 97% sequence identity with a reference sequence and retain the biological activity of the reference sequence. Sequence identity between two aligned sequences can be calculated using, for example, NCBI's BLASTn. Mutants also include nucleotide sequences that have one or more mutations (insertions, deletions, or substitutions) in the reference sequence and nucleotide sequence while still retaining the biological activity of the reference sequence. The multiple mutations typically refer to 1-10, such as 1-8, 1-5, or 1-3. Substitutions can be between purine and pyrimidine nucleotides, between purine nucleotides, or between pyrimidine nucleotides. Substitutions are preferably conservative. For example, in the art, conservative substitutions with nucleotides having similar or similar properties generally do not alter the stability and function of a polynucleotide. Conservative substitutions include, for example, the interchange of (A and G) between purine nucleotides and the interchange of (T or U and C) between pyrimidine nucleotides. Therefore, replacing one or several sites with residues from the same residue in the polynucleotides of the present invention will not substantially affect its activity. In addition, the methylation sites (e.g., continuous CG) in the variants of the present invention are not mutated. That is, the method of the present invention detects the methylation of the methylable sites in the corresponding sequence, and mutations can occur for the bases of non-methylable sites. Typically, the methylation sites are continuous CpG dinucleotides.

[0324] As described herein, the bases of DNA or RNA may be converted. "Conversion," "cytosine conversion," or "CT conversion" as described herein is a process in which DNA is treated using non-enzymatic or enzymatic methods to convert an unmodified cytosine base (cytosine, C) into a base that has a lower binding ability to guanine than cytosine (e.g., uracil base (uracil, U)). Non-enzymatic or enzymatic methods for cytosine conversion are well known in the art. Exemplarily, non-enzymatic methods include treatment with conversion reagents such as bisulfite, acid sulfite, or pyrosulfite, such as calcium bisulfite, sodium bisulfite, potassium bisulfite, ammonium bisulfite, magnesium bisulfite, aluminum bisulfite, bisulfite ions, sodium bisulfate, potassium bisulfate, and ammonium bisulfate, and any combination thereof. Exemplarily, enzymatic methods include treatment with deaminase. The converted DNA is optionally purified. DNA purification methods suitable for use in the present application are well known in the art.

[0325] "Diagnosis" as used in this invention encompasses not only early diagnosis of colorectal cancer but also late diagnosis, and also encompasses colorectal cancer screening, risk assessment, prognosis, and disease identification. Early diagnosis refers to the potential for detecting cancer before metastasis, preferably before morphological changes in tissue or cells are observed.

[0326] Detailed Description of the Invention

[0327] I marker

[0328] a) Methylation markers in colorectal cancer

[0329] After research, the inventors screened out 47 genes from a large number of genes and found that the methylation levels of these genes (such as promoter regions) were related to the nature of colorectal cancer: TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TB X20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2. The present invention provides a method for performing methylation detection on the above genes in a sample (particularly blood), and distinguishing colorectal cancer based on their methylation levels using a mathematical model, thereby achieving the purpose of non-invasive and accurate diagnosis of colorectal cancer.

[0330] Therefore, in the present application, the methylation marker of colorectal cancer includes a DNA sequence and the upstream 5kb and downstream 5kb of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, wherein the DNA sequence includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46 or 47 of the above-mentioned gene sequences, for example, at least 6, at least 7, at least 8 or at least 9. In one or more embodiments, the DNA sequence comprises 1, 2, 3, 4, 5, 6, 7 or 8 selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally further comprises one or more or all of the other gene sequences in (p). In one or more embodiments, the DNA sequence comprises 1, 2, 3, 4, 5 or 6 selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally further comprises one or more or all of the other gene sequences in (p). In one or more embodiments, the DNA sequence comprises 1, 2, 3, 4, 5, 6 or 7 selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally further comprises one or more or all of the other gene sequences in (p). The present invention provides uses and methods of these markers and detection reagents thereof for screening colorectal cancer risk, diagnosing colorectal cancer, and assessing colorectal cancer prognosis. The term "colorectal cancer" as used in this application has the common meaning in the art and includes tumors present in the colon, rectum, and / or appendix.

[0331] In one or more embodiments, the nature of colorectal cancer is related to the methylation of the fragments of the above-mentioned genes. Such fragments can be derived from one or more of the gene sequences. The length of the fragment is 1bp-1kb, preferably 1bp-700bp; the fragment contains one or more methylation sites in the chromosomal region of the corresponding gene. The fragment is, for example, the promoter region of the above-mentioned gene. Usually, the DNA sequence 1k bp upstream and 200bp downstream of the transcription start site (TSS) is defined as the promoter region. If a gene has multiple transcripts (i.e., there are multiple promoter regions), any of the promoter regions can be selected. In some embodiments, the detected fragment contains at least 3 CpG dinucleotides. Therefore, further, the nature of colorectal cancer is related to the methylation level of the fragment shown in SEQ ID NO: 1-47 of each gene shown in Table 5.

[0332] The "colorectal cancer-associated sequences" described herein include any of the 47 genes listed above, sequences within 20 kb (preferably within 5 kb) upstream or downstream thereof, or fragments thereof, or any combination of the 47 sequences listed above (SEQ ID NOs: 1-47) or their complementary sequences. The sequences of the aforementioned genes in the Hg19 genome, as well as sequences within 20 kb upstream or downstream of each gene, can be obtained from public databases (e.g., the NCBI website).

[0333] The locations of the above genes in human chromosomes are shown in Table 5 below, where the base numbers correspond to the reference genome HG19:

[0334] Table 5 Methylation marker genes and locations

[0335] b) Methylation markers in gastric cancer

[0336] The gastric cancer methylation markers described in the present application are selected from any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47 or all 48 of the following gene sequences (Hg19 coordinates):

[0337] Containing chr6:166970625:166970825 (SEQ ID NO:48) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0338] Containing chr11:11600237:11600617 (SEQ ID NO:49) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0339] Containing chr17:76929754:76929954 (SEQ ID NO: 50) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0340] Containing chr6:391738:391938 (SEQ ID NO:51) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0341] Containing chr12:2282090:2282290 (SEQ ID NO:52) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0342] Containing chr2:177030134:177030449 (SEQ ID NO:53) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0343] Containing chr7:35301095:35301411 (SEQ ID NO:54) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0344] Containing chr7:8482114:8482413 (SEQ ID NO:55) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0345] Containing chr2:72371208:72371433 (SEQ ID NO:56) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0346] Containing chr5:134364359:134364559 (SEQ ID NO:57) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0347] Containing chr10:118892523:118892723 (SEQ ID NO:58) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0348] Containing chr12:113901298:113901498 (SEQ ID NO:59) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0349] Containing chr8:143613755:143613955 (SEQ ID NO:60) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0350] Containing chr8:20375580:20375780 (SEQ ID NO:61) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0351] Containing chr7:107499318:107499518 (SEQ ID NO:62) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0352] Containing chr6:1378941:1379141 (SEQ ID NO:63) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0353] Containing chr15:34786976:34787337 (SEQ ID NO: 64) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0354] Containing chr1:156405314:156405514 (SEQ ID NO:65) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0355] Containing chr8:10588811:10589173 (SEQ ID NO:66) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0356] Containing chr4:85418610:85418919 (SEQ ID NO:67) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0357] Containing chr5:140871317:140871517 (SEQ ID NO:68) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0358] Containing chr5:92906255:92906617 (SEQ ID NO:69) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0359] Containing chr14:57265398:57265598 (SEQ ID NO: 70) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0360] Containing chr19:19650947:19651147 (SEQ ID NO:71) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0361] Containing chr11:20618486:20618686 (SEQ ID NO: 72) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0362] Containing chr7:73407894:73408161 (SEQ ID NO:73) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0363] Containing chr16:82660460:82660774 (SEQ ID NO:74) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0364] Containing chr13:24844736:24844936 (SEQ ID NO:75) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0365] Containing chr20:55500358:55500677 (SEQ ID NO:76) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0366] Containing chr10:123923943:123924143 (SEQ ID NO:77) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0367] Containing chr20:59827678:59827907 (SEQ ID NO:78) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0368] Containing chr20:62330559:62330808 (SEQ ID NO: 79) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0369] Containing chr19:13209774:13209974 (SEQ ID NO:80) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0370] Containing chr16:2085778:2086156 (SEQ ID NO:81) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0371] Containing chr6:108488634:108488917 (SEQ ID NO:82) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0372] Containing chr12:115124911:115125191 (SEQ ID NO:83) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0373] Containing chr10:124896740:124897020 (SEQ ID NO:84) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0374] Containing chr14:55243006:55243206 (SEQ ID NO: 85) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0375] Containing chr13:36729096:36729334 (SEQ ID NO:86) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0376] Containing chr2:10444997:10445197 (SEQ ID NO:87) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0377] Containing chr9:2157701:2157901 (SEQ ID NO:88) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0378] Containing chr12:57529619:57529819 (SEQ ID NO: 89) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0379] Containing chr1:119527250:119527450 (SEQ ID NO:90) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0380] Containing chr1:119532788:119532988 (SEQ ID NO:91) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0381] Containing chr15:96909441:96909641 (SEQ ID NO:92) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0382] Containing chr1:146551463:146551747 (SEQ ID NO:93) and sequences within 5 kb upstream and / or within 5 kb downstream thereof;

[0383] Containing chr17:35293755:35293955 (SEQ ID NO: 94) or sequences within 5 kb upstream and downstream thereof; and

[0384] Contains chr17:59482763:59482963 (SEQ ID NO: 95) or sequences within 5 kb upstream and downstream thereof.

[0385] In some embodiments, the one or more gastric cancer methylation markers described herein include: chr17:76929754:76929954 (SEQ ID NO: 50) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr7:8482114:8482413 (SEQ ID NO: 55) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr8:143613755:143613955 (SEQ ID NO: 60) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr7:107499318:107499518 (SEQ ID NO: 62) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr15:34786976:34787337 (SEQ ID NO: NO: 64) and sequences within 5 kb upstream and / or downstream thereof; chr8:10588811:10589173 (SEQ ID NO: 66) and sequences within 5 kb upstream and / or downstream thereof; chr5:92906255:92906617 (SEQ ID NO: 69) and sequences within 5 kb upstream and / or downstream thereof; chr11:20618486:20618686 (SEQ ID NO: 72) and sequences within 5 kb upstream and / or downstream thereof; chr20:55500358:55500677 (SEQ ID NO: 76) and sequences within 5 kb upstream and / or downstream thereof; chr20:59827678:59827907 (SEQ ID NO: NO: 78) and sequences within 5 kb upstream and / or downstream thereof; chr10: 124896740: 124897020 (SEQ ID NO: 84) and sequences within 5 kb upstream and / or downstream thereof; chr14: 55243006: 55243206 (SEQ ID NO: 85) and sequences within 5 kb upstream and / or downstream thereof; chr2: 10444997: 10445197 (SEQ ID NO: 87) and sequences within 5 kb upstream and / or downstream thereof; chr9: 2157701: 2157901 (SEQ ID NO: 88) and sequences within 5 kb upstream and / or downstream thereof; chr12: 57529619: 57529819 (SEQ ID NO: NO: 89) and its upstream and / or downstream sequences within 5 kb;Containing chr1:119527250:119527450 (SEQ ID NO:90) and sequences within 5 kb upstream and / or downstream thereof; containing chr15:96909441:96909641 (SEQ ID NO:92) and sequences within 5 kb upstream and / or downstream thereof; containing chr17:35293755:35293955 (SEQ ID NO:94) and sequences within 5 kb upstream and downstream thereof; and containing chr17:59482763:59482963 (SEQ ID NO:95) and sequences within 5 kb upstream and downstream thereof.

[0386] In some embodiments, the one or more gastric cancer methylation markers described herein include: chr11: 11600237: 11600617 (SEQ ID NO: 40) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr2: 177030134: 177030449 (SEQ ID NO: 53) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr7: 35301095: 35301411 (SEQ ID NO: 54) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr7: 8482114: 8482413 (SEQ ID NO: 55) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr12: 113901298: 113901498 (SEQ ID NO: NO: 59) and sequences within 5 kb upstream and / or downstream thereof; chr7: 107499318: 107499518 (SEQ ID NO: 62) and sequences within 5 kb upstream and / or downstream thereof; chr8: 10588811: 10589173 (SEQ ID NO: 66) and sequences within 5 kb upstream and / or downstream thereof; chr11: 20618486: 20618686 (SEQ ID NO: 72) and sequences within 5 kb upstream and / or downstream thereof; chr13: 24844736: 24844936 (SEQ ID NO: 75) and sequences within 5 kb upstream and / or downstream thereof; chr20: 62330559: 62330808 (SEQ ID NO: NO: 79) and sequences within 5 kb upstream and / or downstream thereof; chr19: 13209774: 13209974 (SEQ ID NO: 80) and sequences within 5 kb upstream and / or downstream thereof; chr12: 115124911: 115125191 (SEQ ID NO: 83) and sequences within 5 kb upstream and / or downstream thereof; chr10: 124896740: 124897020 (SEQ ID NO: 84) and sequences within 5 kb upstream and / or downstream thereof; chr2: 10444997: 10445197 (SEQ ID NO: NO: 87) and sequences within 5 kb upstream and / or downstream thereof; containing chr12: 57529619: 57529819 (SEQ ID NO: 89) and sequences within 5 kb upstream and / or downstream thereof;Containing chr1:119527250:119527450 (SEQ ID NO:90) and sequences within 5 kb upstream and / or downstream thereof; containing chr1:119532788:119532988 (SEQ ID NO:91) and sequences within 5 kb upstream and / or downstream thereof; containing chr1:146551463:146551747 (SEQ ID NO:93) and sequences within 5 kb upstream and / or downstream thereof; and containing chr17:59482763:59482963 (SEQ ID NO:95) or sequences within 5 kb upstream and downstream thereof.

[0387] In some embodiments, the one or more gastric cancer methylation markers described herein include: chr17:76929754:76929954 (SEQ ID NO: 50) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr8:143613755:143613955 (SEQ ID NO: 60) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr8:20375580:20375780 (SEQ ID NO: 61) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr4:85418610:85418919 (SEQ ID NO: 67) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr5:92906255:92906617 (SEQ ID NO: NO: 69) and sequences within 5 kb upstream and / or downstream thereof; chr13: 24844736: 24844936 (SEQ ID NO: 75) and sequences within 5 kb upstream and / or downstream thereof; chr10: 123923943: 123924143 (SEQ ID NO: 77) and sequences within 5 kb upstream and / or downstream thereof; and chr12: 115124911: 115125191 (SEQ ID NO: 83) and sequences within 5 kb upstream and / or downstream thereof.

[0388] In some embodiments, the one or more gastric cancer methylation markers described herein include: chr17:76929754:76929954 (SEQ ID NO: 50) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr8:143613755:143613955 (SEQ ID NO: 60) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr16:82660460:82660774 (SEQ ID NO: 74) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr10:123923943:123924143 (SEQ ID NO: 77) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; and chr6:108488634:108488917 (SEQ ID NO: 82) and sequences within 5 kb upstream and / or within 5 kb downstream thereof.

[0389] In some embodiments, the Hg coordinate region of one or more gastric cancer methylation markers described herein is selected from any one or a combination of any two or more of the following sequences: chr7: 35301095: 35301411 (SEQ ID NO: 54) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr8: 20375580: 20375780 (SEQ ID NO: 61) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr5: 92906255: 92906617 (SEQ ID NO: 69) and a sequence within 5 kb upstream and / or within 5 kb downstream thereof; chr7: 73407894: 73408161 (SEQ ID NO: NO: 73) and sequences within 5 kb upstream and / or downstream thereof; chr6: 108488634: 108488917 (SEQ ID NO: 82) and sequences within 5 kb upstream and / or downstream thereof; chr14: 55243006: 55243206 (SEQ ID NO: 85) and sequences within 5 kb upstream and / or downstream thereof; chr2: 10444997: 10445197 (SEQ ID NO: 88) and sequences within 5 kb upstream and / or downstream thereof; chr1: 119527250: 119527450 (SEQ ID NO: 50) and sequences within 5 kb upstream and / or downstream thereof; chr17: 35293755: 35293955 (SEQ ID NO: NO: 84) or a sequence within 5 kb upstream and downstream thereof; and a sequence containing chr17: 59482763: 59482963 (SEQ ID NO: 95) or a sequence within 5 kb upstream and downstream thereof.

[0390] The chromosome coordinates are consistent with the Hg19 version of the human genome database released in February 2009 (referred to as "Hg19 coordinates" in this application). In some embodiments, the gastric cancer methylation marker described in this application includes a sequence within 3 kb upstream of each start site of each sequence of SEQ ID NO: 48-95, preferably within 2 kb, more preferably within 1 kb, more preferably within 500 bp, more preferably within 300 bp, more preferably within 100 bp, and / or a sequence within 3 kb downstream of each end site, preferably within 2 kb, preferably within 1 kb, preferably within 500 bp, preferably within 300 bp, preferably within 100 bp. In some embodiments, the gastric cancer methylation marker described in this application is a gene sequence containing any one of the above SEQ ID NO: 48-95 sequences and having a length of within 1000 bp, preferably within 600 bp, more preferably within 400 bp.

[0391] In some embodiments, the gastric cancer methylation marker described herein is selected from any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47 or all 48 sequences in SEQ ID NO:48-95.

[0392] In some embodiments, the gastric cancer methylation markers described herein include: SEQ ID NO:50, SEQ ID NO:55, SEQ ID NO:60, SEQ ID NO:62, SEQ ID NO:64, SEQ ID NO:66, SEQ ID NO:69, SEQ ID NO:72, SEQ ID NO:76, SEQ ID NO:78, SEQ ID NO:84, SEQ ID NO:85, SEQ ID NO:87, SEQ ID NO:88, SEQ ID NO:89, SEQ ID NO:90, SEQ ID NO:92, SEQ ID NO:94 and SEQ ID NO:95.

[0393] In some embodiments, the gastric cancer methylation markers described herein include: SEQ ID NO:49, SEQ ID NO:53, SEQ ID NO:54, SEQ ID NO:55, SEQ ID NO:59, SEQ ID NO:62, SEQ ID NO:66, SEQ ID NO:74, SEQ ID NO:75, SEQ ID NO:79, SEQ ID NO:80, SEQ ID NO:83, SEQ ID NO:84, SEQ ID NO:87, SEQ ID NO:89, SEQ ID NO:90, SEQ ID NO:91, SEQ ID NO:93 and SEQ ID NO:95.

[0394] In some embodiments, the gastric cancer methylation markers described herein include: SEQ ID NO: 50, SEQ ID NO: 60, SEQ ID NO: 61, SEQ ID NO: 67, SEQ ID NO: 69, SEQ ID NO: 75, SEQ ID NO: 77, and SEQ ID NO: 83.

[0395] In some embodiments, the gastric cancer methylation markers described herein include: SEQ ID NO: 50, SEQ ID NO: 60, SEQ ID NO: 74, SEQ ID NO: 77, and SEQ ID NO: 82.

[0396] In some embodiments, the gastric cancer methylation marker described herein includes any one or a combination of any multiple of the following sequences: SEQ ID NO: 54, SEQ ID NO: 61, SEQ ID NO: 69, SEQ ID NO: 73, SEQ ID NO: 82, SEQ ID NO: 85, SEQ ID NO: 87, SEQ ID NO: 90, SEQ ID NO: 94, and SEQ ID NO: 95.

[0397] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 87, and any one or more of SEQ ID NOs: 1-39 and 448-95.

[0398] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 94, and any one or more of SEQ ID NOs: 1-46 and 48.

[0399] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 90, and any one or more of SEQ ID NOs: 1-42 and 44-48.

[0400] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 73, and any one or more of SEQ ID NOs: 1-25 and 27-48.

[0401] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 82, and any one or more of SEQ ID NOs: 1-34 and 36-48.

[0402] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 61, and any one or more of SEQ ID NOs: 1-13 and 15-48.

[0403] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 85, and any one or more of SEQ ID NOs: 1-37 and 39-48.

[0404] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 67, and any one or more of SEQ ID NOs: 1-21 and 23-48.

[0405] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 54, and any one or more of SEQ ID NOs: 1-6 and 8-48.

[0406] In some embodiments, the gastric cancer methylation marker described herein includes SEQ ID NO: 95, and any one or more of SEQ ID NOs: 1-47.

[0407] The specific nucleotide sequences of the above Hg19 coordinates, as well as 5 kb upstream of each start site and 5 kb downstream of each end site of each region, are available in public databases (eg, UCSC Genome Browser, Ensemble, and NCBI websites).

[0408] c) Esophageal cancer methylation markers

[0409] The present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of a DNA region selected from the chromosome range numbered 1 to 43 of the present application, or a complementary region thereof, or a modification state of such a fragment in a test sample. For example, the method of the present application determines one or more DNA regions selected from the chromosome range numbered 1 to 43 of the present application in a test sample. For example, the method of the present application determines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, or 43 DNA regions selected from the chromosome range numbered 1 to 43 of the present application in the test sample. One or more of the DNA regions in the chromosome range numbered 1 to 43 can be methylation markers for esophageal cancer.

[0410] The present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 96 to 138, or a complementary region thereof, or a fragment thereof in a test sample. For example, the method of the present application determines in a test sample that one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirty-two, thirty-three, four, five, six, seven, eight, nine, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven, ten, eleven,

[0411] The present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of a modification state of a DNA region or a fragment thereof selected from a region within 5 kbp upstream or downstream of SEQ ID NO: 105 and a gene numbered 1 to 76 of the present application in a test sample. For example, the method of the present application determines the presence and / or amount of a modification state of a region within 5 kbp upstream or downstream of SEQ ID NO: 105 and a gene numbered 1 to 76 of the present application in a test sample. 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135 The aforementioned region selected from within 5 kbp upstream or downstream of SEQ ID NO: 105 and the DNA region or fragment where the genes numbered 1 to 76 of the present application are located can be methylation markers for esophageal cancer.

[0412] The present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of a DNA region selected from the chromosome range numbers 3, 5, 7, 8, 12, 13, 14, 16, 17, 18, 19, 21, 22, 26, 28, 29, 30, 32, 33, 35, 36, 38, 39, 40, 41, 42, and 43 of the present application, or a complementary region thereof, or a modification state of the aforementioned fragments in a test sample. For example, the method of the present application determines one or more DNA regions selected from the chromosome range numbers 1 to 27 of the present application in a test sample. For example, the methods of the present application determine 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, or 27 DNA regions selected from the chromosome range numbered 1 to 27 of the present application in the test sample.

[0413] The present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 98, 100, 102, 103, 107, 108, 109, 111, 112, 113, 114, 116, 117, 121, 123, 124, 125, 127, 128, 130, 131, 133, 134, 135, 136, 137 and 138, or a complementary region thereof, or a modification state of the aforementioned fragment in a test sample. For example, the method of the present application determines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, or 27 DNA regions within 5 kbp upstream or downstream of any one of SEQ ID NOs: 1 to 27 in the test sample.

[0414] The present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of the modification state of a DNA region or a fragment thereof in a test sample selected from the following group: IRF2BP2, LZTS1, DMRTA2, CLVS1, CSNK2A3, HOXD12, FAM109A, HOXD13, FKBP4, TOMM20, AVPR1A, ELAVL4, CARKD, GALNT18, DLL4, CUX2, NR2F2, CACNA1C, Z SCAN10, CARS2, MTSS1L, VPS18, TIMP2, IL32, TBCD, VAC14, RAB3D, LGALS3BP, HOXD10, ZNF750, HOXD1, HOXD11, FAM150 B. HOXD4, TNFRSF6B, TMEM18, ETV5, ARFRP1, BDH1, DGKG, PCDHGC5, DIAPH1, MPC1, RPS6KA2, ELN, WBSCR28, SOX7, and CHD7. . For example, the method of the present application determines the presence and / or amount of the modification state of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes selected from the above genes in the test sample, or a fragment thereof.

[0415] The present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of a DNA region selected from the chromosome ranges numbered 1, 2, 4, 6, 9, 10, 11, 15, 20, 23, 24, 25, 26, 27, 30, 31, 34, 35, 37, 38, 39, 40, and 42 of the present application, or a complementary region thereof, or a modification state of the aforementioned fragments in a test sample. For example, the method of the present application determines one or more DNA regions selected from the chromosome ranges numbered 1 to 23 of the present application in a test sample. For example, the method of the present application determines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 DNA regions or fragments selected from the chromosome range numbered 1 to 23 of the present application in the test sample.

[0416] The present application provides a method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 96, 97, 99, 101, 104, 105, 106, 110, 115, 118, 119, 120, 121, 122, 125, 126, 129, 130, 132, 133, 134, 135 and 137, or a complementary region thereof, or a modification state of the aforementioned fragment in a test sample. For example, the method of the present application determines 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 DNA regions or fragments within 5 kbp upstream or downstream of any one of SEQ ID NOs: 96 to 118 in the test sample.

[0417] The present application provides a method for evaluating the presence and / or progression of esophageal cancer, comprising determining a gene selected from the group consisting of 5k residues upstream or downstream of SEQ ID NO: 111 in a sample to be tested. The region within bp and the presence and / or content of the modification status of the DNA region where CSF1, DNM2, EPS8L3, RAB3D, RER1, RPL18, SKI, DBP, ARHGEF16, HOXD1, PRDM16, HOXD4, RNF207, PDCD1, ICMT, EP300, TBX5, RBX1, TBX3, ETV5, CHFR, DGKG, ZNF605, SLC2A9, DIO3, DRD5, ENSG00000269375, PCDHGC5, CTU2, DIAPH1, RNF166, MPC1, MBP, RPS6KA2, ZNF236, ELN, ICAM5, WBSCR28, ZGLP1, and LZTS1 are located, or a fragment thereof. For example, the method of the present application determines the presence and / or amount of the modification state of a DNA region or a fragment thereof selected from the region within 5 kbp upstream or downstream of SEQ ID NO: 111 and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 genes of the above-mentioned genes provided herein in the test sample.

[0418] d) Methylation markers for liver cancer

[0419] The present application provides a method for assessing the presence and / or progression of liver cancer, comprising determining the presence and / or amount of a DNA region within 5 kbp upstream or downstream of a chromosome range numbered 44 to 245 (SEQ ID NOs: 139-340) selected from the present application, or a complementary region thereof, or a modification state of the aforementioned fragment in a test sample. For example, the method of the present application determines one or more DNA regions within 5 kbp upstream or downstream of a chromosome range numbered 44 to 245 (SEQ ID NOs: 139-340) selected from the present application in a test sample. For example, the methods of the present application determine 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 50, 60, 70, 80, 90, 100, 150, 200, or 202 DNA regions within 5 kbp upstream or downstream of chromosome range numbered 44 to 245 (SEQ ID NOs: 139-340) of the test sample. A DNA region within 5 kbp upstream or downstream of chromosome range numbered 44 to 245 (SEQ ID NOs: 139-340), or a complementary region or fragment thereof can be a methylation marker for liver cancer.

[0420] The present application provides a method for assessing the presence and / or progression of liver cancer, comprising determining the presence and / or amount of a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340, or a complementary region thereof, or a modification state of the aforementioned fragment in a test sample. For example, the methods of the present application determine whether 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 50, 60, 70, 80, 90, 100, 150, 200, or 202 DNA regions within 5 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340 in the test sample are located. A DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340, or a complementary region or fragment thereof can be a methylation marker for liver cancer.

[0421] The present application provides a method for assessing the presence and / or progression of liver cancer, comprising determining the presence and / or amount of a modification state of a DNA region or a fragment thereof within 5k bp upstream or downstream of a gene selected from among genes numbered 77 to 354 of the present application in a test sample. For example, the method of the present application determines the presence and / or amount of modification state of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 80, 90, 100, 150, 200, 201, or 202 genes, or the presence and / or amount of the modification state of a DNA region within 5 kbp upstream or downstream of a gene.

[0422] II. Sample Source and Preparation

[0423] In the present application, the marker can be from a biological sample of any individual of interest. The term "individual" as used herein includes humans and non-human animals. Non-human animals include all vertebrates, such as mammals and non-mammals. An "individual" can also be livestock, such as cattle, pigs, sheep, poultry, and horses; or rodents, such as rats and mice; or non-human primates, such as apes, monkeys, rhesus macaques; or domesticated animals, such as dogs or cats. In some embodiments, the individual is a human or a non-human primate. In some embodiments, the individual is a human. In the present application, "individual," "object," and "subject" are used interchangeably.

[0424] It should be understood that the sequences given in Section I "Markers" above are human sequences. When sequences of non-human animals are involved, the corresponding positions and sequences of the above genes in the non-human animal genome can be easily determined using existing technologies.

[0425] As used herein, the terms "sample," "specimen," "test sample," or "biological sample" refer to a biological composition obtained or derived from an individual that contains cells and / or other molecular entities (e.g., DNA) to be characterized or identified based on physical, biochemical, chemical, and / or physiological characteristics. Biological samples include, but are not limited to, cells, tissues, organs, and / or biological fluids of an individual obtained by any method known to those skilled in the art. In some embodiments, the test sample or biological sample is selected from the group consisting of: histological sections, tissue biopsies, paraffin-embedded tissues, body fluids, surgical resection specimens, isolated blood cells, cells isolated from blood, and any combination thereof. In some embodiments, the body fluid is selected from the group consisting of: whole blood, serum, plasma, and any combination thereof. The selection of the most appropriate sample will depend on the nature of the context. In some embodiments, the test sample or biological sample is whole blood from an individual. In some embodiments, the test sample or biological sample is plasma from an individual. Various methods for preparing plasma from whole blood are known to those skilled in the art. For example, in some embodiments, plasma is obtained by centrifuging whole blood from an individual one, two, three, four, five, or more times. In some embodiments, the test specimen or biological sample is a gastric cancer biopsy.

[0426] The DNA to be detected can be separated from the biological sample. The DNA to be detected can be separated and purified from the biological sample using various methods known in the art. Commercially available kits can be used for separation and purification. For example, DNA can be isolated from cells and tissues by the following methods: cracking the raw material under highly denaturing and reducing conditions, partially using protein degrading enzymes, purifying the nucleic acid components obtained by the phenol / chloroform extraction process, and recovering the nucleic acid from the aqueous phase by dialysis or ethanol precipitation (see, for example, Sambrook, J., Fritsch, EFin T. Maniatis, CSH, Molecular Cloning, 1989). For another example, there are many reagent systems that are particularly suitable for purifying DNA fragments from agarose gels, separating plasmid DNA from bacterial lysates, and separating longer-chain nucleic acids (genomic DNA, total cellular RNA) from blood, tissues or cell cultures. Many of these commercially available purification systems are based on quite well-known principles, that is, nucleic acids are combined with mineral carriers in the presence of solutions of different chaotropic salts. In these systems, suspensions of finely ground glass powder, diatomaceous earth or silica gel are used as carrier materials. Some other methods for isolating and purifying DNA from biological samples are described in, for example, US7888006B2 and EP1626085A1. The choice between methods will be influenced by several factors, including time, cost and the amount of DNA required.

[0427] In some embodiments, the DNA contained in the sample to be tested or the biological sample includes genomic DNA. The term "genomic DNA" as used herein refers to DNA comprising the complete genome of a cell or organism and fragments or portions thereof. Genomic DNA is a large segment of DNA (e.g., longer than about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, or 300 kb) derived from an individual and may have natural modifications, such as DNA methylation.

[0428] In some embodiments, the DNA contained in the sample or biological sample to be tested includes cellular DNA. As used herein, the term "cellular DNA" refers to DNA present in cells, or DNA obtained from cells in vivo and isolated in vitro, or otherwise manipulated in vitro, as long as the DNA is not removed from cells in vivo.

[0429] In some embodiments, the DNA contained in the sample to be tested or the biological sample includes extracellular free DNA. The term "extracellular free DNA" as used herein refers to DNA fragments present outside the cells in the body. The term can also be used to refer to DNA fragments obtained from extracellular sources in the body and separated or manipulated in vitro. The DNA fragments in extracellular free DNA are generally about 100 to 200 bp in length, which is presumably related to the length of the DNA fragments wrapped in nucleosomes. Extracellular free DNA (cfDNA) includes, for example, extracellular free fetal DNA and circulating tumor DNA. Extracellular free fetal DNA circulates in the body (e.g., blood) of pregnant women and represents the fetal genome, while circulating tumor DNA circulates in the body (e.g., blood) of cancer patients. In some embodiments, extracellular free DNA may be substantially free of individual cellular DNA. For example, the extracellular free DNA may include less than about 1,000 ng / mL, less than about 100 ng / mL, less than about 10 ng / mL, or less than about 1 ng / mL of cellular DNA.

[0430] Extracellular free DNA can be prepared by using conventional techniques known in the art. For example, the extracellular free DNA of a blood sample can be obtained by centrifuging a blood sample at a speed of about 200-20,000g, about 200-10,000g, about 200-5,000g, about 300-4000g, etc. for about 3-30 minutes, about 3-15 minutes, about 3-10 minutes, about 3-5 minutes. For example, in some embodiments, the extracellular free DNA of a blood sample can be obtained by centrifuging the plasma or serum of an individual one, two, three, four, five times or more. In some embodiments, in order to separate cells and their fragments from the cell-free components comprising soluble DNA, the biological sample can be obtained by microfiltration. Generally speaking, microfiltration can be performed using a filter, for example, a membrane filter of 0.1 micron to 0.45 micron, such as a 0.22 micron membrane filter.

[0431] In some embodiments, extracellular DNA is extracted from whole blood, serum, or plasma using commercially available DNA extraction products for analysis. This extraction method is reported to have a high recovery rate (>50%) of circulating DNA, and some products (e.g., the QIAamp Circulating Nucleic Acid Kit from Qiagen) are reported to be able to extract small DNA fragments. Typical sample volumes used are 1-5 mL of serum or plasma.

[0432] In some embodiments, extracellular free DNA includes circulating tumor DNA. Circulating tumor DNA ("ctDNA") is fragmented DNA of tumor origin in body fluids (such as blood, urine, saliva, sputum, feces, pleural fluid, cerebrospinal fluid, etc.) that are not associated with cells. Typically, ctDNA is highly fragmented, with an average length of about 150 base pairs. ctDNA typically includes a very small portion of extracellular free DNA in body fluids (such as plasma), for example, ctDNA may constitute less than about 10% of plasma DNA. Typically, this percentage is less than about 1%, for example, less than about 0.5% or less than about 0.01%. In addition, the total amount of plasma DNA is usually very low, for example, about 10 ng / mL plasma. The amount of ctDNA varies from person to person and depends on the type and location of the tumor, and for cancerous tumors, the stage of the cancer. However, ctDNA is usually very rare in body fluids and can only be detected by extremely sensitive and specific techniques. Detecting ctDNA may help detect and diagnose tumors, guide tumor-specific treatment, monitor treatment, and monitor the remission of cancer.

[0433] III base conversion

[0434] DNA methylation is a biological process in which a methyl group is added to a DNA molecule (e.g., to one or more cytosine bases of a DNA molecule) (e.g., by the action of a DNA methyltransferase). In mammals, DNA methylation occurs at the 5' position of a cytosine-phosphate-guanine (CpG) dinucleotide (i.e., a "CpG site") and, when present in a 5'-CpG-3' dinucleotide in the promoter or first exon of a gene, results in epigenetic inactivation of the gene. It has been well documented that DNA methylation plays an important role in regulating gene expression, tumorigenesis, and other genetic and epigenetic diseases.

[0435] As used herein, the term "methylated cytosine residue" refers to a derivative of a cytosine residue in which one methyl group is attached to a carbon atom of the cytosine ring (e.g., C5). The term "unmethylated cytosine residue" refers to an underivatized cytosine residue in which, in contrast to a "methylated cytosine residue," no methyl group is attached to a carbon atom of the cytosine ring (e.g., C5). A CpG site in which the cytosine residue is methylated is a methylated CpG site, while a CpG site in which the cytosine residue is not methylated is an unmethylated CpG site.

[0436] As described herein, conversion can occur between bases in DNA or RNA. "Conversion," "cytosine conversion," or "CT conversion" as described herein refers to the process of treating DNA using non-enzymatic or enzymatic methods to convert unmodified cytosine bases (cytosine, C) into bases that do not bind to guanine (G), such as uracil bases (uracil, U). Some reagents are capable of distinguishing between unmethylated and methylated CpG sites in DNA, thereby obtaining treated DNA. The reagent can selectively act on unmethylated cytosine residues but not significantly act on methylated cytosine residues. Alternatively, the reagent can selectively act on methylated cytosine residues but not significantly act on unmethylated cytosine residues. For example, some reagents can selectively convert unmethylated cytosine residues into uracil, thymine, or another base that is different from cytosine in hybridization, while methylated cytosine residues remain in an unconverted state; for another example, some reagents can selectively cleave methylated residues or selectively cleave unmethylated residues. Thereby, the original DNA is converted into the treated DNA in a manner depending on whether it is methylated or not, so that the treated DNA can be distinguished from the original DNA by its hybridization behavior.

[0437] As used herein, "treated DNA," "treated sequence," and "treated fragment" refer to DNA, nucleic acid sequences, and gene fragments that have been treated with a reagent that can distinguish between unmethylated and methylated CpG sites in DNA, nucleic acid sequences, and gene fragments.

[0438] More specifically, cytosine conversion can be performed using non-enzymatic or enzymatic methods. Exemplarily, non-enzymatic methods include bisulfite or bisulfate treatment. In some embodiments, the reagent used in the non-enzymatic method includes a bisulfite reagent. As used herein, the term "bisulfite reagent" refers to a reagent comprising bisulfite, bisulfite ions, or any combination thereof, which can be used to distinguish methylated and unmethylated CpG dinucleotide sequences, such as those disclosed herein. In this application, treating DNA with a bisulfite reagent is also described as a "bisulfite reaction" or "bisulfite treatment," which refers to a reaction that converts unmethylated cytosine residues, particularly in the presence of bisulfite ions, where unmethylated cytosine residues in nucleic acids are converted to uracil bases, thymine bases, or other bases that differ from cytosine in hybridization behavior, while methylated cytosine residues are not significantly converted. In other words, bisulfite treatment can be used to distinguish methylated CpG dinucleotides from unmethylated CpG dinucleotides. The bisulfite reaction for detecting methylated cytosine residues is described in detail in Frommer, M., et al., Proc Natl Acad Sci USA 89 (1992) 1827-31 and Grigg, G., Clark, S., Bioessays 16 (1994) 431-6. The bisulfite reaction includes a deamination step and a desulfonation step (see Grigg and Clark, supra). The statement “methylated cytosine residues are not significantly converted” does not exclude that a very small percentage (e.g., less than 0.1%, less than 0.2%, less than 0.3%, less than 0.4%, less than 0.5%, less than 0.6%, less than 0.7%, less than 0.8%, less than 0.9%, less than 1%, less than 2%, less than 3%, less than 4%, less than 5%, less than 6%, less than 7%, less than 8%, less than 9%, less than 10%, less than 11%, less than 12%, less than 13%, less than 14%, less than 15%, less than 16%, less than 17%, less than 18%, less than 19%, less than 20%) of the methylated cytosine residues are converted to uracil, thymine, or other bases that differ from cytosine in hybridization behavior, although it is intended that only unmethylated cytosine residues are converted.

[0439] Those skilled in the art will know how to perform bisulfite treatment, particularly the deamination and desulfonation steps, by reference, for example, to Frommer M., et al. (supra) or Grigg and Clark (supra), which disclose the basic parameters of bisulfite treatment. The effects of incubation time and temperature on deamination efficiency, as well as parameters that influence DNA degradation, are disclosed.

[0440] In some embodiments, the bisulfite reagent is selected from the group consisting of ammonium bisulfite, sodium bisulfite, potassium bisulfite, calcium bisulfite, magnesium bisulfite, aluminum bisulfite, bisulfite ion, and any combination thereof. In some embodiments, the bisulfite reagent is sodium bisulfite. In some embodiments, bisulfite reagents are commercially available, such as MethylCode™ Bisulfite Conversion Kit, EpiMark™ Bisulfite Conversion Kit, EpiJET™ Bisulfite Conversion Kit, EZDNAMethylation-Gold™ Kit, and the like. In some embodiments, the bisulfite reaction is performed according to the kit's instructions.

[0441] Exemplary enzymatic methods include deaminase treatment and the use of reagents that selectively cleave unmethylated residues but not methylated residues, or selectively cleave methylated residues but not unmethylated residues. Preferably, the reagent is a methylation-sensitive restriction enzyme (MSRE).

[0442] The term "methylation-sensitive restriction enzyme" refers to an enzyme that selectively digests nucleic acids based on the methylation state of its recognition site. For restriction enzymes that specifically shear when the recognition site is not methylated or hemimethylated, shearing will not occur, or shearing will be significantly reduced in efficiency when the recognition site is methylated. For restriction enzymes that specifically shear when the recognition site is methylated, shearing will not occur, or shearing will be significantly reduced in efficiency when the recognition site is not methylated. In some embodiments, the recognition sequence of the methylation-sensitive restriction enzyme contains a CG dinucleotide (e.g., cgcg or cccggg). In some embodiments, when the cytosine in the CG dinucleotide is methylated at the C5 carbon atom, the methylation-sensitive restriction enzyme does not shear.

[0443] Exemplary MSREs are selected from the group consisting of HpaII enzyme, SalI enzyme, enzyme, ScrFI enzyme, BbeI enzyme, NotI enzyme, SmaI enzyme, XmaI enzyme, MboI enzyme, BstBI enzyme, ClaI enzyme, MluI enzyme, NaeI enzyme, NarI enzyme, PvuI enzyme, SacII enzyme, HhaI enzyme, and any combination thereof.

[0444] Methylation is determined using methods known in the art, such as, but not limited to, differential methylation hybridization ("DMH"), using a methylation-sensitive restriction enzyme or a panel of restriction enzyme reagents comprising a methylation-sensitive restriction enzyme that can distinguish between methylated and unmethylated CpG dinucleotides in the target region.

[0445] In some embodiments, the DNA in the biological sample can be cut before being treated with a methylation-sensitive restriction enzyme. Such methods are known in the art and can include both physical and enzymatic methods. Particularly preferably, one or more restriction enzymes that are insensitive to methylation and whose recognition sites are rich in AT and do not comprise CG dinucleotides are used. This type of enzyme is used to preserve the CpG sites and CpG enrichment regions in the DNA fragmentation. In some embodiments, this type of restriction enzyme is selected from MseI enzyme, BfaI enzyme, Csp6I15 enzyme, Tru1I enzyme, Tru9I enzyme, MaeI enzyme, XspI enzyme, and any combination thereof.

[0446] The transformed DNA is optionally purified. Methods for purifying DNA suitable for use herein are well known in the art.

[0447] IV quantitative analysis

[0448] Any 1, any 2, any 3, any 4, any 5, any 6, any 7, any 8, any 9, any 10, any 11, any 12, any 13, any 14, any 15, any 16, any 17, any 18, any 19, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, any 27 or more, any 28 or more, any 29 or more can be detected. The methylation status or methylation level of at least one CpG dinucleotide in any of the target markers is used to identify whether the subject has gastric cancer.

[0449] Any 1, any 2, any 3, any 4, any 5, any 6, any 7, any 8, any 9, any 10, any 11, any 12, any 13, any 14, any 15, any 16, any 17, any 18, any 19, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, any 27 or more, any 28 or more, any 29 or more, any 30 or more, any 31 or more, any 32 or more, any 33 or more, any 34 or more, any 35 or more, any 36 or more, any 37 or more, any 38 or more, any 39 or more, any 40 or more, any 41 or more, any 42 or more, any 43 or more, any 44 or more, any 45 or more, any 46 or more, any 47 or more, any 48 or more, any 49 or more, any 50 or more, any 51 or more, any 52 or more, any 53 or more, any 54 or more, any 55 or more, any 56 or more, any 57 or more, any 58 or more, any 59 or more, any 60 or more, any 61 or more, any 62 or more, any 63 or more, any 64 or more, any 65 or more, any 66 or more, any 67 or more, any 68 or more, any 69 or more, any 70 or more, any 71 or more, any 72 or more, any 73 or more, any 74 or more, any 75 or more, any 76 or more, any 77 or more, any 78 or more, any 79 or more, any 80 or The methylation status or methylation level of at least one CpG dinucleotide in any 29 or more, any 30 or more, any 31 or more, any 32 or more, any 33 or more, any 34 or more, any 35 or more, any 36 or more, any 37 or more, any 38 or more, any 39 or more, any 40 or more, any 41 or more, any 42 or more, any 43 or more, any 44 or more, any 45 or more, any 46 or more, or all 47 of the target markers is used to identify whether the subject has colorectal cancer.

[0450] Any 1, any 2, any 3, any 4, any 5, any 6, any 7, any 8, any 9, any 10, any 11, any 12, any 13, any 14, any 15, any 16, any 17, any 18, any 19, any 20 or more, any 21 or more, any 22 or more, any 23 or more, any 24 or more, any 25 or more, any 26 or more, any 27 or more, any 28 or more, any 29 or more, any 30 or more, any 31 or more, any 32 or more, any 33 or more, any 34 or more, any 35 or more, any 36 or more, any 37 or more, any 38 or more, any 39 or more, any 40 or more, any 41 or more, any 42 or more, any 43 or more, any 44 or more, any 45 or more, any 46 or more, any 47 or more, any 48 or more, any 49 or more, any 50 or more, any 51 or more, any 52 or more, any 53 or more, any 54 or more, any 55 or more, any 56 or more, any 57 or more, any 58 or more, any 59 or more, any 60 or more, any 61 or more, any 62 or more, any 63 or more, any 64 or more, any 65 or more, any 66 or more, any 67 or more, any 68 or more, any 69 or more, any 70 or more, any 71 or more, any 72 or more, any 73 or more, any 74 or more, any 75 or more, any 76 or more, any 77 or more, any 78 or more, any 79 or more, any 80 or The methylation status or methylation level of at least one CpG dinucleotide in any 27 or more, any 28 or more, any 29 or more, any 30 or more, any 31 or more, any 32 or more, any 33 or more, any 34 or more, any 35 or more, any 36 or more, any 37 or more, any 38 or more, any 39 or more, any 40 or more, any 41 or more, any 42 or more, or all 43 of the target markers is used to identify whether the subject has esophageal cancer.

[0451] Any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 or more, 30 or more, 31 or more, 32 or more, 33 or more, 34 or more, 35 or more, 36 or more, 37 or more, 38 or more, 39 or more, 40 or more, 41 or more, 42 or more, 43 or more can be detected. above, any 44 or more types, any 45 or more types, any 46 or more types, any 47 or more types, any 48 or more types, any 49 or more types, any 50 or more types, any 51 or more types, any 52 or more types, any 53 or more types, any 54 or more types, any 55 or more types, any 56 or more types, any 57 or more types, any 58 or more types, any 59 or more types, any 60 or more types, any 65 or more types, any 70 or more types, any 75 or more types, any 80 or more types, any 85 or more types, any 90 or more types, The methylation status or methylation level of at least one CpG dinucleotide in any 100 or more, any 105, any 110, any 120, any 130, any 140, any 145, any 150, any 155 or more, any 160, any 170, any 180, any 190, any 200 or more, or all 202 of the target markers is used to identify whether the subject has liver cancer.

[0452] The detection reagents and diagnostic kits described in the present application can be used to detect the methylation status or methylation level.

[0453] As used herein, "methylation state" refers to the presence or absence of one or more methylated nucleotide bases in a nucleic acid molecule. For example, a nucleic acid molecule containing methylated cytosine is considered to be methylated (e.g., the methylation state of the nucleic acid molecule is methylated). A nucleic acid molecule that does not contain any methylated nucleotides is considered to be unmethylated. In some embodiments, a nucleic acid can be characterized as "unmethylated" if it is not methylated at a particular locus (e.g., a locus of a particular single CpG dinucleotide) or a particular combination of loci, even if it is methylated at other loci of the same gene or molecule.

[0454] Therefore, methylation state describes the methylated state of nucleic acid (such as genomic sequence or target marker as described herein, DNA region or its fragment).In addition, methylation state refers to the feature relevant to methylation of the nucleic acid segment at specific genomic locus.Such feature includes but is not limited to whether any cytosine (C) residue in this DNA sequence is methylated, the position of one or more methylated C residues, the frequency or percentage of methylated C in any specific region of running through nucleic acid and the methylated allele difference caused by the difference of, for example, allele starting point. " Methylation state " refers to the relative concentration, absolute concentration or pattern of methylated C or unmethylated C in any specific region of running through nucleic acid in biological sample.For example, if one or more cytosine (C) residues in nucleic acid sequence are methylated, then it can be referred to as " hypermethylation " or have " increased methylation ", and if one or more cytosine (C) residues in DNA sequence are unmethylated, then it can be referred to as " demethylation " or have " reduced methylation ". Likewise, if one or more cytosine (C) residues within a nucleic acid sequence are methylated compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.), the sequence is considered to be hypermethylated or to have increased methylation compared to the other nucleic acid sequence. Alternatively, if one or more cytosine (C) residues within a DNA sequence are unmethylated compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.), the sequence is considered to be demethylated or to have decreased methylation compared to the other nucleic acid sequence.

[0455] Methylation level represents the ratio (or percentage, number, ratio, degree) that one or more sites are in methylation state. The methylation level of a region (or a group of sites) is the mean of the methylation levels of all sites (or all sites in the group) in the region. Therefore, the methylation level in a region rises or falls and does not represent that the methylation levels of all methylation sites in the region rise or fall. It is known in the art that the method for detecting DNA methylation (such as simplified methylation sequencing) obtained results are converted into the process of methylation level. Methylation level can be determined by, for example, quantitative analysis of the amount of complete DNA present after restrictive digestion with methylation-sensitive restriction enzymes. In this example, if quantitative PCR is used to carry out quantitative analysis of the specific sequence in DNA, the amount of template DNA approximately equals the control of simulation treatment, which shows that the sequence is not highly methylated, and the amount of template is significantly less than the amount of template in the sample of simulation treatment, which shows that methylated DNA exists in the sequence. Therefore, the methylation level in the above example can be used as a quantitative indicator of methylation state. When the methylation level of the sequence in the sample needs to be compared with the threshold level, this is particularly useful.

[0456] The methylation level / status of one or more CpG dinucleotide sequences within a DNA sequence (e.g., a target marker) can be determined by various analytical methods known in the art, preferably quantitative analytical methods. Exemplary analytical methods include: polymerase chain reaction, including real-time polymerase chain reaction, digital polymerase chain reaction, and bisulfite-converted PCR (e.g., methylation-specific PCR (MSP)) and sequences within 5 kb upstream and / or within 5 kb downstream; nucleic acid sequencing; whole genome methylation sequencing (RRBS) and sequences within 5 kb upstream and / or within 5 kb downstream; simplified methylation sequencing; mass-based separation (e.g., electrophoresis, mass spectrometry) and sequences within 5 kb upstream and / or within 5 kb downstream; target capture (e.g., hybridization, microarray) and sequences within 5 kb upstream and / or within 5 kb downstream; methylation-sensitive restriction endonuclease analysis; methylation-sensitive high-resolution melting curve analysis; chip-based methylation profiling; mass spectrometry; and fluorescence quantitative analysis. Herein, detection includes detection of either strand at a gene or locus.

[0457] In some embodiments, quantitative analysis is performed by real-time PCR. Non-limiting examples of real-time PCR include HeavyMethyl™ PCR described by Cottrell et al., Nucl. Acids Res. 32:e10, 2003; MethyLight™ PCR described by Eads et al., Cancer Res. 59:2302-2306, 1999. TM PCR; Headloop PCR described by Rand et al., Nucl. Acids Res. 33:e 127, 2005.

[0458] As used herein, the term "Heavy Methyl TM "PCR" refers to a real-time PCR technique recognized in the art in which one or more non-extendable nucleic acid (e.g., oligonucleotide) blocks bind to bisulfite-treated nucleic acid in a methylation-specific manner (i.e., the blockers bind specifically to non-mutated DNA under moderate to high stringency conditions). An amplification reaction is performed using one or more primers, which may optionally be methylation-specific but are flanked by one or more blockers. In the presence of non-methylated nucleic acid (i.e., mutated DNA), the blockers bind and no PCR product is produced. The methylation level of the nucleic acid in the sample is determined using a TaqMan™ assay essentially as described, for example, in Holland et al., Proc. Natl. Acad. Sci. USA, 88:7276-7280, 1991.

[0459] As used herein, the term "MethyLight TM PCR" refers to a real-time PCR technique based on fluorescence that is recognized in the art and uses a technique called TaqMan TM The TaqMan probe is a dual-labeled fluorescent oligonucleotide probe designed to hybridize to a CpG-rich sequence located between the forward and reverse amplification primers. TM The probe consists of a fluorescent "reporter moiety" and a "quencher moiety" covalently bound to the TaqMan TM The linker portion (e.g., phosphoramidite) to which the nucleotides of an oligonucleotide are attached. During PCR amplification, TaqMan TM The probe is cleaved by the 5' nuclease activity of Taq polymerase, thereby generating a signal that is detected in real time during the PCR reaction. In this method, molecular beacons can be used as detectable probes, and the system is independent of the 5'-3' exonuclease activity of the DNA polymerase used (see Mhlanga and Malmberg, Methods 25:463-471, 2001).

[0460] As used herein, the term "Headloop PCR" refers to an art-recognized real-time PCR method that selectively amplifies a target nucleic acid but suppresses the amplification of non-amplified target variants by extending the 3' stem-loop to form a hairpin structure that cannot provide further amplification template.

[0461] In some embodiments, the real-time PCR is a multiplex real-time PCR. As used herein, the term "multiplex" can refer to an assay or other analytical method that can simultaneously measure the presence and / or amount of multiple markers (e.g., multiple nucleic acid sequences) by using more than one marker, each marker having at least one different detection characteristic, such as a fluorescence characteristic (e.g., excitation wavelength, emission wavelength, emission intensity, FWHM (full width at half maximum) or fluorescence lifetime) or a unique nucleic acid or protein sequence characteristic.

[0462] In some embodiments, quantitative analysis is performed by nucleic acid sequencing. Exemplary methods of nucleic acid sequencing are known in the art, see, for example, Frommer et al., Proc. Natl. Acad. Sci. USA 89: 1827-1831, 1992; Clark et al., Nucl. Acids Res. 22: 2990-2997, 1994. For example, by comparing the sequence obtained from a sample not treated with bisulfite or the known nucleotide sequence of the target region with the sequence obtained from a sample treated with bisulfite, it is helpful to identify methylated cytosine in the DNA sequence. Compared with the untreated sample, a thymine residue detected at any cytosine site in the bisulfite-treated sample can be considered to be a mutation caused by bisulfite treatment, that is, there is a methylated cytosine at that site.

[0463] Methods for sequencing DNA are known in the art and include, for example, the dideoxy chain termination method or the Maxam-Gilbert method (see Sambrook et al., Molecular Cloning, A Laboratory Manual (2nd Ed., CSHP, New York 1989)), pyrosequencing (see Uhlmann et al., Electrophoresis, 23:4072-4079, 2002), solid-phase pyrosequencing (see Landegren et al., Genome Res., 8(8):769-776, 1998), solid-phase minisequencing (see, for example, Southern et al., Genomics, 13:1008-1017, 1992), minisequencing using FRET (see, for example, Chen and Kwok, Nucleic Acids Res. 25:347-353, 1997), sequencing by ligation, or ultra-deep sequencing (see Marguiles et al., Nature 437(7057):376-80(2005)).

[0464] In some embodiments, quantitative analysis is performed by mass-based separation (e.g., electrophoresis, mass spectrometry). For example, the presence of methylated cytosine residues can be detected by combined bisulfite restriction analysis (COBRA), substantially as described by Xiong and Laird, Nucl. Acids Res., 25:2532-2534, 2001. This method utilizes the difference in restriction enzyme recognition sites between methylated and unmethylated nucleic acids after treatment with a compound (e.g., bisulfite) that can selectively mutate unmethylated cytosine residues. For example, the restriction endonuclease Taq1 cuts the sequence TCGA, which will be TTGA after bisulfite treatment of unmethylated nucleic acids and therefore will not be cut. Digested and / or undigested nucleic acids are then detected using detection means known in the art, such as electrophoresis and / or mass spectrometry. As another example, after treatment with a compound that selectively mutates unmethylated cytosine residues, different techniques are used to detect nucleic acid differences in the amplified products based on differences in nucleotide sequence and / or secondary structure, such as methylation-specific single-strand conformation analysis (MS-SSCA) (Bianco et al., Hum. Mutat., 14:289-293, 1999), methylation-specific denaturing gradient gel electrophoresis (MS-DGGE) (Abrams and Stanton, Methods Enzymol., 212:71-74, 1992), and methylation-specific denaturing high performance liquid chromatography (MS-DHPLC) (Deng et al., Chin. J. Cancer Res., 12:171-191, 2000).

[0465] In some embodiments, quantitative analysis is performed by target capture (e.g., hybridization, microarray). Suitable detection methods by hybridization are known in the art, such as Southern, dot blot, slot blot or other nucleic acid hybridization methods (Kawai et al., Mol. Cell. Biol. 14:7421-7427, 1994; Gonzalgo et al., Cancer Res. 57:594-599, 1997). In some embodiments, the probes used for hybridization analysis are detectably labeled. In some embodiments, the nucleic acid-based probes used for hybridization analysis are unlabeled. Such unlabeled probes can be fixed on a solid support such as a microarray and can hybridize with a detectably labeled target nucleic acid molecule. An example of a microarray is a methylation-specific microarray, which can be used to distinguish between sequences with converted cytosine residues and sequences with unconverted cytosine residues (see Adorjan et al., Nucl. Acids Res., 30:e21, 2002). Hybridization-based analysis can also be used for nucleic acids treated with methylation-sensitive restriction enzymes. For example, the methylation status of CpG dinucleotide sequences within a DNA sequence can be determined by oligonucleotide probes that hybridize with PCR amplification primers simultaneously with bisulfite-treated DNA (wherein the primers can be methylation-specific primers or standard primers).

[0466] In some embodiments, the quantitative analysis is performed in the presence of a detection reagent. As used herein, the term "detection reagent" is a reagent used to detect the presence, absence, or amount of nucleic acid in a quantitative analysis step. Various detection reagents known in the art can be used in this application. In some embodiments, the detection reagent is selected from the group consisting of fluorescent probes, intercalating dyes, chromophore-labeled probes, radioisotope-labeled probes, and biotin-labeled probes.

[0467] In some embodiments, the quantitative analysis comprises amplifying the treated DNA using a quantitative primer pair and a DNA polymerase. As used herein, the term "quantitative primer pair" refers to one or more primer pairs used in the quantitative analysis step. Preferably, the quantitative primer pair is capable of hybridizing to at least 9 consecutive nucleotides of the treated DNA under stringent conditions, moderately stringent conditions, or highly stringent conditions.

[0468] In some embodiments, the quantitative analysis includes determining the methylation level of one or more target markers based on the presence or level of multiple CpG dinucleotides, TpG dinucleotides, or CpA dinucleotides in the treated DNA. In some embodiments, the quantitative analysis includes determining the methylation level of cytosine residues based on the presence or level of one or more CpG dinucleotides in the treated DNA. In some embodiments, the quantitative analysis includes determining the methylation level of cytosine residues based on the presence or level of one or more TpG dinucleotides in the treated DNA. In some embodiments, the quantitative analysis includes determining the methylation level of cytosine residues based on the presence of CpA dinucleotides in the treated DNA.

[0469] In some embodiments, the quantitative analysis step is performed by separating the treated DNA product into a plurality of components. In some embodiments, the plurality of components are subjected to a plurality of different quantitative analysis tests, wherein different combinations of the treated DNA product (if present in the component) are quantitatively analyzed in one of the plurality of components. In some embodiments, a control marker is quantitatively analyzed in each component.

[0470] In some embodiments, the methylation level of each target marker is quantitatively analyzed based on pre-amplified DNA by using MSP (see Herman et al., Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci USA. 1996 September 3; 93(18): 9821-6 and United States Patent No. 6,265,171). For example, by using one or more primers that specifically hybridize to unconverted sequences under moderate and / or high stringency conditions, amplification products are generated only when the template contains methylated cytosine at the CpG site.

[0471] In some embodiments, the quantitative primer pair is designed to amplify at least a portion of the treated DNA product, i.e., the quantitative analysis is designed as nested PCR. Nested PCR is an improvement on PCR that aims to improve sensitivity and specificity. Nested PCR involves the use of two primer sets and two consecutive PCR reactions. A first round of amplification is performed to produce a first amplicon, and a second round of amplification is performed using a primer pair, in which one or both primers anneal to a site within the region defined by the initial primer pair, i.e., the second primer pair is considered to be "nested" in the first pair of primers. In this way, background amplification products from the first PCR reaction that do not contain the correct internal sequence are no longer further amplified in the second PCR reaction.

[0472] Typically, the PCR reaction solution contains Taq DNA polymerase, PCR buffer, primers, probe, dNTPs, Mg 2+ Preferably, the Taq DNA polymerase is a hot start Taq DNA polymerase. 2+ The final concentration is 1.0-20.0 mM; the concentration of each primer is 100-500 nM; the concentration of each probe is 100-500 nM. Exemplary PCR reaction conditions are: 95°C pre-denaturation for 5 minutes; 95°C denaturation for 15 seconds, 60°C annealing and extension for 60 seconds, 50 cycles.

[0473] In some embodiments, the method of the present application includes a pre-amplification step. One of the purposes of pre-amplification of target markers is to increase the quantity of target markers in treated DNA. As used herein, the term "amplification" generally refers to any process that can cause the copy number of a molecule or a group of related molecules to increase. When "amplification" is used for polynucleotide molecules, it refers to multiple copies of a polynucleotide molecule or a part of a polynucleotide molecule that usually starts from a small amount of polynucleotides, wherein the amplified material (amplicon, PCR amplicon) is usually detectable. The amplification of polynucleotides covers multiple chemical and enzymatic processes. The form of amplification includes generating multiple DNA copies from one or several copies of template RNA or DNA molecules by polymerase chain reaction (reverse transcription PCR, PCR), chain displacement amplification (SDA) reaction, transcription-mediated amplification (TMA) reaction, nucleic acid sequence-based amplification (NASBA) reaction or ligase chain reaction (LCR).

[0474] Available pre-amplification primers can be used to pre-amplify the markers in the treated DNA. As used herein, the term "primer" refers to a single-stranded oligonucleotide that can, under suitable conditions (e.g., buffer and temperature), in the presence of four different nucleoside triphosphates and a reagent (e.g., DNA polymerase) for polymerization, serve as the starting point for template-guided DNA synthesis. In any given case, the length of the primer depends on, for example, the intended use of the primer, and is typically within the range of 15 to 30 nucleotides. Short primer molecules typically require lower temperatures to form sufficiently stable hybrid complexes with the template. Primers do not necessarily reflect the exact sequence of the template, but must be sufficiently complementary to hybridize with the template. A primer site is a region on the template that hybridizes with a primer. A primer pair is a set of primers comprising a 5' forward primer that hybridizes with the 5' end of the sequence to be amplified and a 3' reverse primer that hybridizes with the complementary strand at the 3' end of the sequence to be amplified. A person skilled in the art can design primers according to the marker to be amplified based on common knowledge in the art (see, for example, PCR Primer: A Laboratory Manual, Cold Spring Harbor Laboratories, NY, 1995). In addition, some software packages for designing optimal probes and / or primers for use in various analyses are publicly available, such as Primer 3 available from the Center for Genome Research, Cambridge, Mass., USA. Obviously, their potential uses should also be taken into account when designing probes or primers. For example, the primers designed for the purposes of the present invention may include at least one CpG site, or the amplification product obtained from the primers may include at least one CpG site. Tools for designing primers for detecting DNA methylation status are also known in the art, such as MethPrimer (Li LC and Dahiya R. MethPrimer: designing primers for methylation PCRs. Bioinformatics. 2002 Nov; 18(11): 1427-31). In the present application, by using the preamplification primers as a primer pool, any target marker (each at least a portion of the target marker or a subregion of the target marker) in the treated DNA can be preamplified.

[0475] As used herein, the term "complementary" refers to hybridization or base pairing between nucleotides or nucleic acids, for example, between the two strands of a double-stranded DNA molecule, or between a primer binding site and an oligonucleotide primer on a single-stranded nucleic acid to be sequenced or amplified. Complementary nucleotides are typically A and T (or A and U), or C and G. Two single-stranded RNA or DNA molecules are said to be complementary when the nucleotides of one strand, after optimal alignment and comparison, and with appropriate nucleotide insertions or deletions, pair with at least about 80% (typically at least about 90% to 95%, more preferably about 98% to 100%) of the nucleotides of the other strand. Alternatively, complementarity exists when an RNA or DNA strand hybridizes to its complementary sequence under selective hybridization conditions. Typically, selective hybridization occurs when there is at least about 65% (preferably at least about 75%, more preferably at least about 90%) complementarity over a stretch of at least 14 to 25 nucleotides. See M. Kanehisa, Nucleic Acids Res. 12:203 (1984), incorporated herein by reference.

[0476] In some embodiments, the preamplification primer pool comprises at least one methylation-specific primer pair. In some embodiments, the preamplification primer pool comprises a plurality of methylation-specific primer pairs. In some embodiments, the preamplification step is performed by methylation-specific PCR ("MSP"), which is PCR using methylation-specific primers. This technique (i.e., MSP) has been described in Herman et al., supra.

[0477] As used herein, the term "methylation-specific primer pair" refers to a primer pair specifically designed to recognize CpG sites in order to exploit differences in methylation to amplify a specific target marker in the treated DNA. The primers act only on molecules with a specific methylation state or without a specific methylation state. For example, the primers can be oligonucleotides that, under stringent, moderately stringent, or highly stringent conditions, can hybridize specifically with a specific CpG site that is methylated in a methylation-specific manner, but cannot hybridize with a specific CpG site that is not methylated. Thus, the primers will specifically amplify a target marker that is methylated at a specific CpG site. For another example, the primers can be oligonucleotides that, under stringent, moderately stringent, or highly stringent conditions, can hybridize specifically with a specific CpG site that is unmethylated in a methylation-specific manner, but cannot hybridize with a specific CpG site that is methylated. Thus, the primers will specifically amplify a target marker that is not methylated at a specific CpG site. Therefore, in the present application, the use of methylation-specific primers in the pre-amplification of at least one target marker in the treated DNA can distinguish between methylated and unmethylated CpG sites. The methylation-specific primer pair of the present application comprises at least one primer that hybridizes to a bisulfite-treated CpG dinucleotide. Thus, the sequence of the primer specific for methylated DNA comprises at least one CpG dinucleotide, and the sequence of the primer specific for unmethylated DNA comprises a "T" at the C position of CpG and / or an "A" at the G position of CpG.

[0478] A methylation-specific primer pair typically comprises a forward primer and a reverse primer, each of which comprises an oligonucleotide sequence that hybridizes to at least 9 consecutive nucleotides of one of the target markers (or a subregion of the target marker) under stringent conditions, moderately stringent conditions, or highly stringent conditions, wherein the at least 9 consecutive nucleotides of one of the target markers (or a subregion of the target marker) comprise at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more) CpG site.

[0479] As used herein, the term "hybridization" may refer to a process in which two single-stranded polynucleotides non-covalently associate to form a stable double-stranded polynucleotide. In one aspect, the resulting double-stranded polynucleotide may be a "hybrid" or "duplex." The salt concentration in "hybridization conditions" is typically less than about 1 M, often less than about 500 mM, and can be less than about 200 mM. A "hybridization buffer" includes a buffered saline solution, such as 5% SSPE, or other such buffers known in the art. The hybridization temperature can be as low as 5°C, but is typically above 22°C, more typically above about 30°C, and often above 37°C. Hybridization is typically performed under stringent conditions, meaning that a sequence will hybridize to its target sequence but not to other non-complementary sequences. Stringent conditions are sequence-dependent and vary in different circumstances. For example, longer fragments may require higher hybridization temperatures than shorter fragments for specific hybridization. Because other factors may affect the stringency of hybridization, including base composition and length of the complementary strands, the presence of organic solvents, and the extent of base mismatches, the combination of parameters is more important than the absolute measurement of any one parameter alone. Typically, stringent conditions are selected to be about 5°C lower than the melting temperature (Tm) of a specific sequence at a specific ionic strength and pH. Tm can be the temperature at which half of a population of double-stranded nucleic acid molecules is separated into single strands. Several equations for calculating the Tm of nucleic acids are well known in the art. As shown in standard references, a simple estimate of the Tm value can be calculated by the formula Tm = 81.5 + 0.41 (% G + C) when the nucleic acid is in a 1M NaCl aqueous solution (see, for example, Anderson and Young, Quantitative Filter Hybridization, in Nucleic Acid Hybridization (1985)). Other references (e.g., Allawi and SantaLucia, Jr., Biochemistry, 36: 10581-94 (1997)) include alternative calculation methods that take into account structure and environment as well as sequence characteristics when calculating Tm.

[0480] In general, the stability of hybrids is a function of ion concentration and temperature. Typically, hybridization reactions are performed under relatively low stringency conditions, followed by washing in wash solutions of varying, but higher, stringency. Exemplary stringent conditions include a pH of about 7.0 to about 8.3, a temperature of at least 25°C, and a sodium ion (or other salt) concentration of at least 0.01 M to no more than 1 M. For example, 5x SSPE (750 mM NaCl, 50 mM sodium phosphate, 5 mM EDTA, pH 7.4) and a temperature of about 30°C are suitable for allele-specific hybridization, although the appropriate temperature depends on the length and / or GC content of the hybridization region. In one aspect, "hybridization stringency" for determining mismatch percentages can be as follows: 1) high stringency: 0.1x SSPE, 0.1% SDS, 65°C; 2) moderate stringency (also known as moderate stringency): 0.2x SSPE, 0.1% SDS, 50°C; and 3) low stringency: 1.0x SSPE, 0.1% SDS, 50°C. It should be understood that the same stringency can be achieved using alternative buffers, salts, and temperatures. For example, moderately stringent hybridization can refer to conditions that allow nucleic acid molecules (e.g., probes) to bind to complementary nucleic acid molecules. The hybridized nucleic acid molecules typically have at least 60% identity, including, for example, at least 70%, 75%, 80%, 85%, 90%, or 95% identity. Moderately stringent conditions can be conditions that achieve the same effect as the following conditions: hybridization at 42°C, 50% formamide, 5x Denhardt's solution, 5x SSPE, 0.2% SDS, followed by washing at 42°C, 0.2x SSPE, 0.2% SDS. Highly stringent conditions can be provided by, for example, hybridization at 42°C, 50% formamide, 5x Denhardt's solution, 5x SSPE, 0.2% SDS, followed by washing at 65°C, 0.1x SSPE, and 0.1% SDS. Low stringency hybridization can be achieved using the following conditions: 22°C, 10% formamide, 5x Denhardt's solution, 6x SSPE, 0.2% SDS hybridization, followed by washing in 1x SSPE, 0.2% SDS at 37°C. Denhardt's solution contains 1% polysucrose, 1% polyvinylpyrrolidone, and 1% bovine serum albumin (BSA). 20x SSPE (sodium chloride, sodium phosphate, EDTA) contains 3M sodium chloride, 0.2M sodium phosphate, and 0.025M EDTA.Other suitable moderate stringency and high stringency hybridization buffers and conditions are well known to those skilled in the art and are described, for example, in Sambrook et al., Molecular Cloning: A Laboratory Manual, 2nd ed., Cold Spring Harbor Press, Plainview, NY (1989) and Ausubel et al., Short Protocols in Molecular Biology, 4th ed., John Wiley & Sons (1999).

[0481] In some embodiments, the pre-amplification primer pool also includes a control primer pair for amplifying a control marker. Typically, a control marker is a nucleic acid with a known characteristic (e.g., a known sequence, a known copy number per cell), which is used to compare with an experimental target (e.g., a nucleic acid with an unknown concentration). The control can be an endogenous, preferably an invariant gene, to which the experimental nucleic acid or target nucleic acid in the analysis can be standardized. This type of control standardized because of differences between samples may occur in, for example, sample processing, analysis efficiency, etc., and allows accurate data comparison between samples, quantitative analysis of amplification efficiency and deviation.

[0482] In some embodiments, the present application uses RRBS technology to detect the methylation level of the CpG site of the target marker of interest, and then calculates the methylation haplotype ratio (MHF) of the marker as the DNA methylation level of the marker. The calculation of MHF can be performed as described in the present application.

[0483] Determination of whether the subject V has cancer

[0484] The inventors of the present application have discovered that the methylation level of one or more target markers described herein can be used to determine cancer. In one or more embodiments, the methylation level of the CpG sites in the target markers described herein can be detected in a sample, and then the methylation haplotype ratio (MHF) of the target marker is calculated and used as the DNA methylation level of the marker.

[0485] In this paper, MHF can be calculated by the following formula:

[0486] MHFi,h=(Ni,h) / Ni

[0487] Where i represents the target methylation interval, h represents the target methylation haplotype, Ni represents the number of reads located in the target methylation interval, and Ni,h represents the number of reads containing the target methylation haplotype.

[0488] You can also calculate the average methylation level (AMF) for each target region and calculate the average level of methylation within the region. The formula is as follows:

[0489] Where m is the total number of CpG sites in the target, i is each CpG site in the interval, and N C,i is the number of reads with C as the base of the CpG site (i.e., the number of reads with methylation at the site), N T,i is the number of reads with a T base at the CpG site (i.e., the number of sequencing reads with unmethylated residues at the site).

[0490] The logistic regression model in the sklearn (V1.0.1) package in Python (V3.9.7) can be used to calculate the model prediction score y for each target marker or multiple target markers: model = LogisticRegression(). The formula of the model is as follows, where x is the methylation level value (MHF) of the sample target marker, w is the coefficient of different markers, b is the intercept value, and T represents transposition:

[0491] In this paper, the training set was constructed based on the DNA methylation level of each marker in the training set samples. The threshold defined by the Youden index of the training set was used as the cancer prediction threshold. The cancer prediction thresholds of each marker described in this paper were obtained. The cancer prediction thresholds of each marker can be found in Tables 8, 11, 15, and 19 of this paper.

[0492] In some embodiments, the methylation level of a single target marker described herein is used as a basis for judgment, the MHF of the target marker in each sample is calculated according to the above formula, and the predicted score of the target marker is obtained by the trained model. If the value is higher than the threshold value of the target marker shown in Table 8, it is judged that the patient has colorectal cancer or is at risk of having colorectal cancer.

[0493] In some embodiments, the methylation level of a single target marker described herein is used as a basis for judgment, the MHF of the target marker in each sample is calculated according to the above formula, and the predicted score of the target marker is obtained by the trained model. If the value is higher than the threshold value of the target marker shown in Table 11, it is judged that the patient has gastric cancer or is at risk of having gastric cancer.

[0494] In some embodiments, the methylation level of a single target marker described herein is used as a basis for judgment, the MHF of the target marker in each sample is calculated according to the above formula, and the predicted score of the target marker is obtained by the trained model. If the value is higher than the threshold value of the target marker shown in Table 15, it is judged that the patient has esophageal cancer or is at risk of having esophageal cancer.

[0495] In some embodiments, the methylation level of a single target marker described herein is used as a basis for judgment, the MHF of the target marker in each sample is calculated according to the above formula, and the predicted score of the target marker is obtained by the trained model. If the value is higher than the threshold value of the target marker shown in Table 19, it is judged that the patient has liver cancer or is at risk of having liver cancer.

[0496] It should be understood that when two or more target markers are used, each sample can obtain its own MHF by calculating the methylation level of the CpG sites in each target marker detected. In the samples of the training set, the MHFs of the two or more target markers obtained from all samples are used for training to obtain the parameters of the above-mentioned prediction model formula. For the sample to be tested, the prediction model score y is obtained by bringing the calculated MHF of the sample into the formula of the prediction model determined by the training set, and the y is compared with the threshold value defined by the Youden Index obtained from the two or more target markers in the training set, wherein, if it is higher than the threshold value, it is judged to have cancer or the risk of having cancer.

[0497] In addition to the above comparison, those skilled in the art can also determine an individual's risk of having cancer based on various factors, such as age, gender, medical history, family history, symptoms, etc.

[0498] VI Compositions and Kits

[0499] The present invention provides a methylation detection or diagnostic kit and diagnostic reagent or diagnostic composition for the identification of cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer), the kit and composition comprising a reagent for detecting the methylation state or level of at least one CpG dinucleotide of one or more target markers described herein. Depending on the target marker to be detected, the kit and composition may contain primers and / or probe molecules. Preferably, the primers include primer pairs that can hybridize with the target marker to be detected or its target region under stringent conditions, moderately stringent conditions or highly stringent conditions. The primers may also include primers for detecting internal references such as ACTB.

[0500] In some embodiments, the primers are packaged in a single container or in separate containers. In some embodiments, the kit further comprises one or more blocking oligonucleotides.

[0501] In some embodiments, the kits and compositions further comprise a detection reagent. In some embodiments, the detection reagent is selected from the group consisting of a fluorescent probe, an intercalating dye, a chromophore-labeled probe, a radioisotope-labeled probe, and a biotin-labeled probe.

[0502] In some embodiments, the kit may further comprise a DNA polymerase and / or a container suitable for storing a biological sample obtained from an individual. In some embodiments, the kit further comprises instructions for use and / or explanations of the test results of the kit.

[0503] In some embodiments, the kits and compositions may also include reagents for enzymatic or non-enzymatic conversion. In a preferred embodiment, the kit further includes a bisulfite reagent or a methylation sensitive restriction enzyme (MSRE). In some embodiments, the bisulfite reagent is selected from the group consisting of ammonium bisulfite, sodium bisulfite, potassium bisulfite, calcium bisulfite, magnesium bisulfite, aluminum bisulfite, bisulfite ions, and any combination thereof. In some embodiments, the bisulfite reagent is sodium bisulfite. In some embodiments, the MSRE is selected from the group consisting of HpaII enzyme, SalI enzyme, enzyme, ScrFI enzyme, BbeI enzyme, NotI enzyme, SmaI enzyme, XmaI enzyme, MboI enzyme, BstBI enzyme, ClaI enzyme, MluI enzyme, NaeI enzyme, NarI enzyme, PvuI enzyme, SacII enzyme, HhaI enzyme, and any combination thereof.

[0504] The kits and compositions may also include a converted positive standard in which unmethylated cytosine is converted to a base that does not bind to guanine. The positive standard may be fully methylated.

[0505] The kit and composition may also include PCR reaction reagents. Preferably, the PCR reaction reagents include Taq DNA polymerase, PCR buffer (buffer), dNTPs, Mg 2+ .

[0506] In some embodiments, the kits and compositions further comprise standard reagents that can be used to perform CpG position-specific methylation analysis, wherein the analysis comprises one or more of the following techniques: MS-SNuPE, MSP, MethyLight TM , HeavyMethylTM, COBRA, and nucleic acid sequencing.

[0507] In some embodiments, the kits and compositions may comprise additional reagents selected from the group consisting of buffers (e.g., restriction enzyme, PCR, storage or wash buffers), DNA recovery reagents or kits (e.g., precipitation, ultrafiltration, affinity columns), and DNA recovery components, among others.

[0508] The kit of the present application may further comprise one or more of the following components known in the art of DNA enrichment: a protein component that selectively binds to methylated DNA; a triplex-forming nucleic acid component, one or more linkers, optionally in a suitable solution; a substance or solution for ligation, such as a ligase, a buffer; a substance or solution for column chromatography; a substance or solution for immunologically based enrichment (such as immunoprecipitation); a substance or solution for nucleic acid amplification, such as PCR; a dye or dyes, if suitable for coupling agents, if suitable in solution; a substance or solution for hybridization; and / or a substance or solution for washing steps.

[0509] In some other embodiments, the composition of the present application contains an isolated nucleic acid molecule, wherein the isolated nucleic acid molecule is selected from one or more of the following: any one of SEQ ID NOs: 1-47.

[0510] In some other embodiments, the composition of the present application contains an isolated nucleic acid molecule selected from one or more of the following: any one of SEQ ID NOs: 48-95.

[0511] In some other embodiments, the composition of the present application contains an isolated nucleic acid molecule selected from one or more of the following: any one of SEQ ID NOs: 96-138.

[0512] In some other embodiments, the composition of the present application contains an isolated nucleic acid molecule selected from one or more of the following: any one of SEQ ID NOs: 139-340.

[0513] The present application also includes a medium recording the sequence of the isolated nucleic acid molecule described herein and optionally its methylation information, and the medium is used to compare with gene methylation sequencing data to determine the presence, content and / or methylation level of the nucleic acid molecule. Preferably, the medium is a card printed with the sequence and optionally its methylation information, such as a paper, plastic, metal, or glass card. Preferably, the medium is a computer-readable medium storing the sequence and optionally its methylation information and a computer program, and when the computer program is executed by a processor, the following steps are implemented: the methylation sequencing data of the sample is compared with the sequence, thereby obtaining the presence, content and / or methylation level of the nucleic acid molecule containing the sequence in the sample.

[0514] The present application also includes a device for identifying cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer, and / or liver cancer), the device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the following steps are implemented: (1) obtaining the methylation level of one or more target markers or target regions thereof selected from the following in the sample, (2) judging whether the sample is cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer, and / or liver cancer) based on the methylation level of (1). Preferably, the obtaining step is performed using any one of the methods described in Section IV of the present application; preferably, the judging step is performed using any one of the methods described in Section V of the present application.

[0515] VII Application

[0516] The present application also provides the use of the isolated nucleic acid molecules described herein as detection targets in the diagnosis of cancer (eg, colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer).

[0517] Compared with existing molecular diagnosis technologies for cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer), the methylation markers and technical solutions provided in this application effectively solve the problem of low sensitivity of current diagnostic technologies, and contribute to early diagnosis and treatment of cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer) to improve the cure rate.

[0518] Based on the inventors' findings, the present invention provides a method for screening the risk of cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer), diagnosing cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer), and evaluating the prognosis of cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer), comprising: (1) detecting the methylation level of the cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer) related sequence (one or more markers) described in the present application in a sample of the subject, for example, by sequencing; (2) comparing the methylation level of the marker in step (1) with the corresponding reference level, and (3) screening the risk of cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer), diagnosing cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer), or evaluating the prognosis of cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer) based on the comparison result. Typically, the method further comprises, before step (1), extraction and quality control of the sample DNA, and / or conversion of unmethylated cytosine on the DNA into bases that do not bind to guanine.

[0519] The detection of step (1) can be any detection method suitable for detecting genomic DNA methylation. In a specific embodiment, step (1) includes: treating genomic DNA with a conversion reagent to convert unmethylated cytosine into a base (such as uracil) whose ability to bind to guanine is lower than that of cytosine; using primers for PCR amplification, the primers are suitable for amplifying the converted sequence of the cancer-related sequence described in this application (for example, colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer); determining the methylation level of at least one CpG by the presence or absence of the amplified product, or sequence identification (such as probe-based PCR detection identification or DNA sequencing identification). Alternatively, step (1) may also include: treating genomic DNA with a methylation-sensitive restriction endonuclease; using primers for PCR amplification, the primers are suitable for amplifying a sequence containing at least one CpG dinucleotide in the colorectal cancer-related sequence described in this application; determining the methylation level of at least one CpG by the content of the amplified product.

[0520] In some embodiments, the comparison in step (2) comprises: directly comparing the methylation level of the marker in step (1) with a reference level, or deriving a score by calculation and comparing the score of the methylation level of the marker with a corresponding reference score. Preferably, the score is calculated by a logistic regression model. In some embodiments, step (3) comprises: when the methylation level of the marker is greater than the reference level, or the score of the methylation level is greater than the reference score, the subject is at risk of developing cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer), suffers from cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer) or has a poor prognosis for cancer (e.g., colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer).

[0521] In the application, reference level or reference score are reference methylation levels or scoring that can be used as diagnosis or screening basis.Such level or scoring can be obtained by comparing between the sample based on cancer (for example, colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer) or risk object and healthy subjects, no cancer (for example, colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer) or the object of risk.In addition, reference level or reference score can also be the level or scoring of healthy subjects, no cancer (for example, colorectal cancer, gastric cancer, esophageal cancer and / or liver cancer) or the object of risk.Reference level or reference score can be derived from the group of an object or at least two objects.Those skilled in the art can select reference level according to the sensitivity and specificity of expectation.

[0522] Implementation Plan A

[0523] On the other hand, the present application provides the following embodiments:

[0524] 1. Use of a reagent for detecting the methylation state or level of at least one CpG dinucleotide of one or more target markers in the preparation of a detection reagent or diagnostic kit for diagnosing gastric cancer, and use of an apparatus for determining the methylation state or level of at least one CpG dinucleotide of one or more target markers in the preparation of a diagnostic kit for diagnosing gastric cancer; wherein the one or more target markers are selected from any 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47 or all 48 sequences in the following sequences (1)-(48):

[0525] ((1) containing chr6:166970625:166970825 (SEQ ID NO:48) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0526] (2) containing chr11:11600237:11600617 (SEQ ID NO:49) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0527] (3) containing chr17:76929754:76929954 (SEQ ID NO: 50) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0528] (4) containing chr6:391738:391938 (SEQ ID NO:51) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0529] (5) containing chr12:2282090:2282290 (SEQ ID NO:52) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0530] (6) containing chr2:177030134:177030449 (SEQ ID NO:53) and sequences within 5 kb upstream and / or 5 kb downstream;

[0531] (7) containing chr7:35301095:35301411 (SEQ ID NO:54) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0532] (8) containing chr7:8482114:8482413 (SEQ ID NO:55) and sequences within 5 kb upstream and / or 5 kb downstream;

[0533] (9) containing chr2:72371208:72371433 (SEQ ID NO:56) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0534] (10) containing chr5:134364359:134364559 (SEQ ID NO:57) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0535] (11) containing chr10:118892523:118892723 (SEQ ID NO:58) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0536] (12) containing chr12:113901298:113901498 (SEQ ID NO:59) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0537] (13) containing chr8:143613755:143613955 (SEQ ID NO:60) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0538] (14) containing chr8:20375580:20375780 (SEQ ID NO:61) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0539] (15) containing chr7:107499318:107499518 (SEQ ID NO:62) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0540] (16) containing chr6:1378941:1379141 (SEQ ID NO:63) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0541] (17) containing chr15:34786976:34787337 (SEQ ID NO: 64) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0542] (18) containing chr1:156405314:156405514 (SEQ ID NO:65) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0543] (19) containing chr8:10588811:10589173 (SEQ ID NO:66) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0544] (20) containing chr4:85418610:85418919 (SEQ ID NO:67) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0545] (21) containing chr5:140871317:140871517 (SEQ ID NO:68) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0546] (22) containing chr5:92906255:92906617 (SEQ ID NO:69) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0547] (23) containing chr14:57265398:57265598 (SEQ ID NO: 70) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0548] (24) containing chr19:19650947:19651147 (SEQ ID NO:71) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0549] (25) containing chr11:20618486:20618686 (SEQ ID NO: 72) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0550] (26) containing chr7:73407894:73408161 (SEQ ID NO:73) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0551] (27) containing chr16:82660460:82660774 (SEQ ID NO:74) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0552] (28) containing chr13:24844736:24844936 (SEQ ID NO:75) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0553] (29) containing chr20:55500358:55500677 (SEQ ID NO:76) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0554] (30) containing chr10:123923943:123924143 (SEQ ID NO:77) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0555] (31) containing chr20:59827678:59827907 (SEQ ID NO:78) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0556] (32) containing chr20:62330559:62330808 (SEQ ID NO: 79) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0557] (33) containing chr19:13209774:13209974 (SEQ ID NO:80) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0558] (34) containing chr16:2085778:2086156 (SEQ ID NO:81) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0559] (35) containing chr6:108488634:108488917 (SEQ ID NO:82) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0560] (36) containing chr12:115124911:115125191 (SEQ ID NO:83) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0561] (37) containing chr10:124896740:124897020 (SEQ ID NO:84) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0562] (38) containing chr14:55243006:55243206 (SEQ ID NO: 85) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0563] (39) containing chr13:36729096:36729334 (SEQ ID NO: 86) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0564] (40) containing chr2:10444997:10445197 (SEQ ID NO:87) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0565] (41) containing chr9:2157701:2157901 (SEQ ID NO:88) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0566] (42) containing chr12:57529619:57529819 (SEQ ID NO: 89) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0567] (43) containing chr1:119527250:119527450 (SEQ ID NO:90) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0568] (44) containing chr1:119532788:119532988 (SEQ ID NO:91) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0569] (45) containing chr15:96909441:96909641 (SEQ ID NO:92) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0570] (46) containing chr1:146551463:146551747 (SEQ ID NO:93) and sequences within 5 kb upstream and / or within 5 kb downstream;

[0571] (47) containing chr17:35293755:35293955 (SEQ ID NO: 94) or sequences within 5 kb upstream and downstream thereof; and

[0572] (48) Containing chr17:59482763:59482963 (SEQ ID NO: 95) or sequences within 5 kb upstream and downstream thereof.

[0573] 2. The use according to embodiment 1, characterized in that:

[0574] The one or more target markers include the sequences described in items (3), (8), (13), (15), (17), (19), (22), (25), (29), (31), (37), (38), (40), (41), (42), (43), (45), (47) and (48); or

[0575] The one or more target markers include the sequences described in items (2), (6), (7), (8), (12), (15), (19), (25), (28), (32), (33), (36), (37), (40), (42), (43), (44), (46) and (48); or

[0576] The one or more target markers include the sequences described in items (3), (13), (14), (20), (22), (28), (30) and (36); or

[0577] The one or more target markers include the sequences described in items (3), (13), (27), (30) and (35); or

[0578] The one or more target markers include the sequences described in items (7), (14), (22), (26), (35), (38), (40), (43), (47) and (48).

[0579] 3. The use as described in embodiment 1, characterized in that the one or more target markers are selected from the sequences described in any 1, 2, 3, 4, 5, 6, 7, 8 or 9 of items (7), (14), (22), (26), (35), (38), (40), (43), (47) and (48).

[0580] 4. The use according to embodiment 1, characterized in that

[0581] The target marker includes the sequence described in item (40), and any one or more of the sequences in items (1)-(39) and (41)-(48); or

[0582] The target marker includes the sequence described in item (47), and any one or more of the sequences in items (1) to (46) and (48); or

[0583] The target marker includes the sequence described in item (43), and any one or more of the sequences in items (1)-(42) and (44)-(48); or

[0584] The target marker includes the sequence described in item (26), and any one or more of the sequences in items (1)-(25) and (27)-(48); or

[0585] The target marker includes the sequence described in item (35), and any one or more sequences of items (1)-(34) and (36)-(48); or

[0586] The target marker includes the sequence described in item (14), and any one or more of the sequences (1)-(13) and (15)-(48); or

[0587] The target marker includes the sequence described in item (38), and any one or more of the sequences in items (1)-(37) and (39)-(48); or

[0588] The target marker includes the sequence described in item (22), and any one or more sequences of items (1)-(21) and (23)-(48); or

[0589] The target marker includes the sequence described in item (7), and any one or more sequences of items (1)-(6) and (8)-(48); or

[0590] The target marker includes the sequence described in item (48), and any one or more sequences in items (1)-(47).

[0591] 5. The use according to any one of embodiments 1 to 4, characterized in that the target marker comprises a sequence within 1 kb, preferably within 500 bp, more preferably within 300 bp, more preferably within 100 bp upstream of each start site of each sequence of SEQ ID NO: 48-95 and / or a sequence within 1 kb, preferably within 500 bp, preferably within 300 bp, preferably within 100 bp downstream of each end site; preferably, the target marker is a gene sequence containing any one of the sequences of SEQ ID NO: 48-95 and having a length of less than 400 bp.

[0592] 6. The use according to any one of embodiments 1-4, characterized in that the sequences described in items (1) to (48) are the sequences shown in SEQ ID NOs: 48-95, respectively.

[0593] 7. The use according to any one of embodiments 1 to 6, wherein the reagent comprises a primer and / or a probe molecule;

[0594] Preferably, the primer molecule is identical to, complementary to, or hybridizes to the one or more target markers under stringent conditions and comprises at least 9 consecutive nucleotides, and the probe molecule hybridizes to the amplified product of the one or more target markers under stringent conditions.

[0595] 8. The use according to any one of embodiments 1 to 6, characterized in that the reagent is a reagent required for implementing genome simplified methylation sequencing technology.

[0596] 9. A diagnostic reagent or diagnostic kit for detecting the methylation status or methylation level of at least one CpG dinucleotide of one or more target markers to diagnose gastric cancer, comprising a reagent for detecting the methylation status or level of at least one CpG dinucleotide of one or more target markers; wherein the one or more target markers are as described in any one of embodiments 1-6.

[0597] 10. The diagnostic reagent or diagnostic kit according to embodiment 9, characterized in that the diagnostic reagent or diagnostic kit comprises primers and / or probe molecules; preferably, the primer molecules are identical to, complementary to, or hybridize under stringent conditions to the one or more target markers and comprise at least 9 consecutive nucleotides, and the probe molecules hybridize under stringent conditions to the amplification products of the one or more target markers;

[0598] Optionally, the diagnostic reagent or diagnostic kit further comprises primer molecules and / or probe molecules for detecting the internal reference gene ACTB.

[0599] 11. The diagnostic reagent or diagnostic kit according to embodiment 9, wherein the diagnostic reagent or diagnostic kit further comprises one or more substances selected from the group consisting of: PCR buffer, polymerase, dNTP, restriction endonuclease, enzyme digestion buffer, fluorescent dye, fluorescence quencher, fluorescence reporter, exonuclease, alkaline phosphatase, internal standard, control, KCl, MgCl2, and (NH4)2SO4.

[0600] 12. The diagnostic reagent or diagnostic kit as described in embodiment 9 is characterized in that the reagent also includes reagents used in one or more of the following methods: PCR based on bisulfite conversion, DNA sequencing, methylation-sensitive restriction endonuclease analysis, fluorescence quantification, methylation-sensitive high-resolution melting curve method, chip-based methylation mapping and mass spectrometry.

[0601] 13. The diagnostic reagent or diagnostic kit according to embodiment 12, characterized in that the reagent is selected from one or more of the following: bisulfite and its derivatives, fluorescent dyes, fluorescence quenchers, fluorescence reporters, internal standards and controls.

[0602] 14. Use of at least one reagent or a set of reagents for distinguishing between methylated and unmethylated CpG dinucleotides in at least one target region of genomic DNA in a kit for preparing a method for detecting and / or classifying gastric cancer in an individual, wherein the method comprises contacting genomic DNA isolated from a biological sample of the individual with the at least one reagent or set of reagents, wherein the target region is identical or complementary to a sequence of at least 16 consecutive nucleotides of one or more target markers, wherein the consecutive nucleotides comprise at least one CpG dinucleotide sequence, thereby at least partially providing detection and / or classification of gastric cancer, wherein the one or more target markers are as described in any one of embodiments 1-6.

[0603] 15. Use of one or more reagents that convert unmethylated cytosine base at position 5 to uracil or other bases detectably different from cytosine in hybridization properties, an amplification enzyme, and at least one primer comprising at least 9 consecutive nucleotides in a kit for preparing a method for detecting and / or classifying gastric cancer in an individual, wherein the method comprises:

[0604] a) isolating genomic DNA from a biological sample of the individual;

[0605] b) treating the genomic DNA or fragments thereof of a) with the one or more reagents;

[0606] c) contacting the treated genomic DNA or treated fragments thereof with the amplification enzyme and the at least one primer that is identical to, complementary to, or hybridizes under stringent conditions to one or more target markers, wherein the treated genomic DNA or fragments thereof is amplified to produce at least one amplification product or is not amplified; and

[0607] d) determining the methylation status or level of at least one CpG dinucleotide of the one or more target markers, or a mean or value reflecting the average methylation status or level of a plurality of CpG dinucleotides of the one or more target markers, based on the presence or nature of the amplicon, thereby at least partially detecting and / or classifying gastric cancer in the individual;

[0608] Wherein, the one or more target markers are as described in any one of embodiments 1-6.

[0609] 16. The use according to embodiment 15, wherein in step b), the genomic DNA or fragment thereof is treated with a reagent selected from the group consisting of bisulfite, acid sulfite, metabisulfite and combinations thereof.

[0610] 17. The use according to embodiment 16, wherein in c), the nucleic acid molecules are contacted or amplified by using a thermostable DNA polymerase as the amplification enzyme, using a polymerase lacking 5'-3' exonuclease activity, using polymerase chain reaction and / or generating an amplification product with a detectable label.

[0611] 18. The use according to embodiment 15, wherein the contacting or amplifying in c) comprises using methylation-specific primers.

[0612] 19. Use of one or more methylation-sensitive restriction enzymes and amplification enzymes and at least one primer comprising at least 9 consecutive nucleotides in the preparation of a kit for a method for detecting and / or classifying gastric cancer in an individual, wherein the primers are identical to, complementary to, or hybridize under stringent conditions to one or more target markers; the method comprising:

[0613] a) isolating genomic DNA from a biological sample of the individual;

[0614] b) digesting the genomic DNA or fragment thereof described in a) with the one or more methylation-sensitive restriction enzymes, and contacting the resulting digestion product with the amplification enzyme and the at least one primer; and

[0615] c) determining the methylation status or level of at least one CpG dinucleotide of the one or more target markers based on the presence or nature of the amplification product, thereby at least in part detecting and / or classifying gastric cancer in the individual;

[0616] Wherein, the one or more target markers are as described in any one of embodiments 1-6.

[0617] 20. The use as described in embodiment 19 is characterized in that the presence or absence of the amplification product is determined by hybridizing at least one nucleic acid or peptide nucleic acid, wherein the at least one nucleic acid or peptide nucleic acid is identical to or complementary to a fragment of at least 16 bases long of a sequence selected from the one or more target markers.

[0618] 21. Use of a treated nucleic acid derived from one or more target markers in the preparation of a kit for diagnosing gastric cancer, wherein the treatment is suitable for converting at least one unmethylated cytosine base of the one or more target markers to uracil or other bases that are detectably different from cytosine in hybridization, and the one or more target markers are as described in any one of embodiments 1-6.

[0619] 22. An apparatus for detecting and diagnosing gastric cancer in an individual, the apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the following steps are implemented: (1) obtaining the methylation level or methylation status of at least one CpG dinucleotide of one or more target markers in a sample, and (2) interpreting gastric cancer based on the methylation level or methylation status of (1);

[0620] Wherein, the one or more target markers are as described in any one of embodiments 1-6.

[0621] Implementation Plan B

[0622] On the other hand, the present application provides the following embodiments:

[0623] 1. A method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of the modification state of any one or more DNA regions selected from Table 1, or their complementary regions, or fragments thereof, in a test sample.

[0624] 2. A method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 96 to 138, or a complementary region thereof, or a fragment thereof, in a test sample.

[0625] 3. A method for assessing the presence and / or progression of esophageal cancer, comprising determining the presence and / or amount of the modification state of a DNA region or fragment thereof selected from a region within 5 kbp upstream or downstream of SEQ ID NO: 105 and any one or more genes listed in Table 2 in a test sample.

[0626] 4. The method according to any one of embodiments 1 to 3, further comprising obtaining nucleic acid from the sample to be tested.

[0627] 5. The method of embodiment 4, wherein the nucleic acid comprises cell-free nucleic acid.

[0628] 6. The method according to any one of embodiments 1 to 5, wherein the sample to be tested comprises tissue, cells and / or body fluid.

[0629] 7. The method according to any one of embodiments 1 to 6, wherein the sample to be tested comprises plasma.

[0630] 8. The method of any one of embodiments 1-7, further comprising transforming the DNA region or fragment thereof.

[0631] 9. According to the method described in embodiment 8, the base having the modified state and the base not having the modified state respectively form different substances after conversion.

[0632] 10. The method according to any one of embodiments 1 to 9, wherein the base having the modified state does not substantially change after conversion, and the base not having the modified state is changed to another base different from the base after conversion, or is cleaved after conversion.

[0633] 11. The method of any one of embodiments 9-10, wherein the base comprises cytosine.

[0634] 12. The method of any one of embodiments 1-11, wherein the modification state comprises a methylation modification.

[0635] 13. The method of any one of embodiments 10-12, wherein the other base comprises uracil.

[0636] 14. The method of any one of embodiments 8-13, wherein the conversion comprises conversion by a deamination agent and / or a methylation-sensitive restriction enzyme.

[0637] 15. The method of embodiment 14, wherein the deamination reagent comprises bisulfite or an analog thereof.

[0638] 16. The method of any one of embodiments 1-15, wherein the method for determining the presence and / or amount of a modification state comprises determining the presence and / or amount of a DNA region or fragment thereof having the modification state.

[0639] 17. The method according to any one of embodiments 1 to 16, wherein the presence and / or content of the DNA region or fragment thereof having the modification state is detected by sequencing.

[0640] 18. The method of any one of embodiments 1-17, wherein the presence and / or progression of a tumor is determined by confirming the presence of a modification state of the DNA region or a fragment thereof and / or that the DNA region or a fragment thereof has a higher content of the modification state relative to a reference level.

[0641] 19. A nucleic acid comprising a sequence capable of binding to any one or more DNA regions selected from Table 1, or a complementary region thereof, or the converted region, or the fragment thereof.

[0642] 20. A nucleic acid comprising a sequence capable of binding to a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 96 to 138, or a complementary region thereof, or a converted region thereof, or a fragment thereof.

[0643] 21. A nucleic acid comprising a sequence capable of binding to a region within 5 kbp upstream or downstream of SEQ ID NO: 105 and a DNA region containing any one or more genes listed in Table 2, or a complementary region thereof, or a converted region thereof, or a fragment thereof.

[0644] 22. A kit comprising the nucleic acid of any one of embodiments 19-21.

[0645] 23. Use of the nucleic acid according to any one of embodiments 19 to 21 and / or the kit according to embodiment 22 in the preparation of a disease detection product.

[0646] 24. Use of the nucleic acid of any one of embodiments 19-21, and / or the kit of embodiment 22, in the preparation of a substance for assessing the presence and / or progression of esophageal cancer.

[0647] 25. Use of the nucleic acid of any one of embodiments 19-21 and / or the kit of embodiment 22 in the preparation of a substance for determining the modification state of the DNA region or its fragment.

[0648] 26. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to any one or more DNA regions, or their complementary regions, or the above-mentioned converted regions, or the above-mentioned fragments based on the modification state of the DNA regions, or their complementary regions, or the above-mentioned converted regions, or the above-mentioned fragments selected from Table 1.

[0649] 27. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 96 to 138, or a complementary region thereof, or a converted region thereof, or a fragment thereof, based on the modification state of the DNA region, or the complementary region thereof, or the converted region thereof, or the fragment thereof.

[0650] 28. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region, a complementary region, a converted region, or a fragment thereof selected from a region within 5 kbp upstream or downstream of SEQ ID NO: 105 and a DNA region containing any one or more genes listed in Table 2, or a region complementary thereto, or a converted region thereof, or a fragment thereof, based on the modification state of the region complementary thereto, a converted region, or a fragment thereof.

[0651] 29. Use of nucleic acids, nucleic acid panels, and / or kits for determining the modification status of DNA regions in the preparation of substances for assessing the presence and / or progression of esophageal cancer, wherein the DNA regions to be determined comprise sequences selected from any one or more DNA regions listed in Table 1, or their complementary regions, or the converted regions or fragments thereof.

[0652] 30. Use of a nucleic acid, nucleic acid set and / or kit for determining the modification status of a DNA region in the preparation of a substance for assessing the presence and / or progression of esophageal cancer, wherein the DNA region to be determined comprises a sequence selected from a DNA region within 5 kbp upstream or downstream of any one of SEQ ID NOs: 96 to 138, or a complementary region thereof, or a converted region thereof, or a fragment thereof.

[0653] 31. Use of a nucleic acid, nucleic acid set, and / or kit for determining the modification status of a DNA region in the preparation of a substance for assessing the presence and / or progression of esophageal cancer, wherein the DNA region to be determined comprises a sequence selected from a region within 5 kbp upstream or downstream of SEQ ID NO: 105 and a DNA region containing any one or more genes listed in Table 2, or a complementary region thereof, or a converted region thereof, or a fragment thereof.

[0654] 32. The use according to any one of embodiments 29 to 31, wherein the modification state comprises a methylation modification.

[0655] 33. A storage medium recording a program capable of executing the method according to any one of embodiments 1 to 18.

[0656] 34. A device comprising the storage medium of embodiment 33, and optionally further comprising a processor coupled to the storage medium, the processor being configured to execute based on a program stored in the storage medium to implement the method of any one of embodiments 1-18.

[0657] Implementation Plan C

[0658] On the other hand, the present application provides the following embodiments:

[0659] 1. A method for assessing the presence and / or progression of liver cancer, comprising determining the presence and / or amount of the modification state of any one or more DNA regions selected from Table 3, or their complementary regions, or fragments thereof, in a test sample.

[0660] 2. A method for assessing the presence and / or progression of liver cancer, comprising determining the presence and / or amount of a DNA region within 1 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340, or a complementary region thereof, or a fragment thereof in a test sample.

[0661] 3. A method for assessing the presence and / or progression of liver cancer, comprising determining the presence and / or amount of the modification state of a DNA region or fragment thereof within 1 kbp upstream or downstream of any one or more genes selected from Table 4 in a test sample.

[0662] 4. The method according to any one of embodiments 1 to 3, further comprising obtaining nucleic acid from the sample to be tested.

[0663] 5. The method of embodiment 4, wherein the nucleic acid comprises cell-free nucleic acid.

[0664] 6. The method according to any one of embodiments 1 to 5, wherein the sample to be tested comprises tissue, cells and / or body fluid.

[0665] 7. The method according to any one of embodiments 1 to 6, wherein the sample to be tested comprises plasma.

[0666] 8. The method of any one of embodiments 1-7, further comprising transforming the DNA region or fragment thereof.

[0667] 9. According to the method described in embodiment 8, the base having the modified state and the base not having the modified state respectively form different substances after conversion.

[0668] 10. The method according to any one of embodiments 1 to 9, wherein the base having the modified state does not substantially change after conversion, and the base not having the modified state is changed to another base different from the base after conversion, or is cleaved after conversion.

[0669] 11. The method of any one of embodiments 9-10, wherein the base comprises cytosine.

[0670] 12. The method of any one of embodiments 1-11, wherein the modification state comprises a methylation modification.

[0671] 13. The method of any one of embodiments 10-12, wherein the other base comprises uracil.

[0672] 14. The method of any one of embodiments 8-13, wherein the conversion comprises conversion by a deamination agent and / or a methylation-sensitive restriction enzyme.

[0673] 15. The method of embodiment 14, wherein the deamination reagent comprises bisulfite or an analog thereof.

[0674] 16. The method of any one of embodiments 1-15, wherein the method for determining the presence and / or amount of a modification state comprises determining the presence and / or amount of a DNA region or fragment thereof having the modification state.

[0675] 17. The method according to any one of embodiments 1 to 16, wherein the presence and / or content of the DNA region or fragment thereof having the modification state is detected by sequencing.

[0676] 18. The method of any one of embodiments 1-17, wherein the presence and / or progression of a tumor is determined by confirming the presence of a modification state of the DNA region or a fragment thereof and / or that the DNA region or a fragment thereof has a higher content of the modification state relative to a reference level.

[0677] 19. A nucleic acid comprising a sequence capable of binding to any one or more DNA regions selected from Table 3, or their complementary regions, or the aforementioned converted regions, or the aforementioned fragments.

[0678] 20. A nucleic acid comprising a sequence capable of binding to a DNA region within 1 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340, or a complementary region thereof, or a converted region thereof, or a fragment thereof.

[0679] 21. A nucleic acid comprising a sequence capable of binding to a DNA region within 1 kbp upstream or downstream of any one or more genes selected from Table 4, or a complementary region thereof, or the converted region or fragment thereof.

[0680] 22. A kit comprising the nucleic acid of any one of embodiments 19-21.

[0681] 23. Use of the nucleic acid according to any one of embodiments 19 to 21 and / or the kit according to embodiment 22 in the preparation of a disease detection product.

[0682] 24. Use of the nucleic acid of any one of embodiments 19-21, and / or the kit of embodiment 22, in the preparation of a substance for assessing the presence and / or progression of esophageal cancer.

[0683] 25. Use of the nucleic acid of any one of embodiments 19-21 and / or the kit of embodiment 22 in the preparation of a substance for determining the modification state of the DNA region or its fragment.

[0684] 26. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to any one or more DNA regions, or their complementary regions, or the aforementioned converted regions, or the aforementioned fragments, based on the modification state of the DNA regions, or their complementary regions, or the aforementioned converted regions, or the aforementioned fragments selected from Table 3.

[0685] 27. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region within 1 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340, or a complementary region thereof, or a converted region thereof, or a fragment thereof, based on the modification state of the DNA region, or the complementary region thereof, or the converted region thereof, or the fragment thereof.

[0686] 28. A method for preparing a nucleic acid, comprising designing a nucleic acid capable of binding to a DNA region within 1 kbp upstream or downstream of any one or more genes selected from Table 4, or its complementary region, or the above-mentioned converted region, or the above-mentioned fragment based on the modification state of the DNA region, or its complementary region, or the above-mentioned converted region, or the above-mentioned fragment.

[0687] 29. Use of nucleic acids, nucleic acid panels, and / or kits for determining the modification status of DNA regions, in the preparation of substances for assessing the presence and / or progression of liver cancer, wherein the nucleic acids for determining the DNA region within 1 kbp upstream or downstream comprise a sequence selected from any one or more DNA regions listed in Table 3, or their complementary regions, or the converted regions or fragments thereof.

[0688] 30. Use of a nucleic acid, nucleic acid set and / or kit for determining the modification status of a DNA region in the preparation of a substance for assessing the presence and / or progression of liver cancer, wherein the nucleic acid for determining the DNA region within 1 kbp upstream or downstream comprises a sequence selected from the DNA region within 1 kbp upstream or downstream of any one of SEQ ID NOs: 139 to 340, or its complementary region, or the converted region thereof, or the fragment thereof.

[0689] 31. Use of nucleic acids, nucleic acid sets, and / or kits for determining the modification status of DNA regions in the preparation of substances for assessing the presence and / or progression of liver cancer, wherein the nucleic acids used to determine the DNA region within 1 kbp upstream or downstream comprises a sequence selected from a DNA region within 1 kbp upstream or downstream of any one or more genes listed in Table 4, or a complementary region thereof, or a converted region thereof, or a fragment thereof.

[0690] 32. The use according to any one of embodiments 29 to 31, wherein the modification state comprises a methylation modification.

[0691] 33. A storage medium recording a program capable of executing the method according to any one of embodiments 1 to 18.

[0692] 34. A device comprising the storage medium of embodiment 33, and optionally further comprising a processor coupled to the storage medium, the processor being configured to execute based on a program stored in the storage medium to implement the method of any one of embodiments 1-18.

[0693] Without intending to be bound by any theory, the following examples are merely intended to illustrate the fusion protein, preparation method, and use of the present application, and are not intended to limit the scope of the present invention.

[0694] Example

[0695] Example 1 Colorectal cancer sample processing and methylation marker screening

[0696] Experimental samples

[0697] A total of 108 blood samples from patients with colorectal cancer and 108 age- and sex-matched blood samples from patients without colorectal cancer were collected. All enrolled patients signed informed consent. Sample information is shown in Table 6:

[0698] Table 6

[0699] Experimental methods

[0700] 1. Sample cfDNA Extraction

[0701] All blood samples were collected in Streck tubes. To extract plasma, the blood samples were first centrifuged at 1600 g for 10 minutes at 4°C. To prevent disruption of the buffy coat, the smooth brake mode was set. The supernatant was then transferred to a new 1.5 ml conical tube and centrifuged at 16,000 g for 10 minutes at 4°C. The supernatant was again transferred to a new 1.5 ml conical tube and stored at -80°C.

[0702] To extract circulating free DNA (cfDNA), plasma aliquots were thawed and immediately processed using the QIAamp circulating nucleic acid extraction kit (Qiagen 55114) according to the manufacturer's instructions. The concentration of extracted cfDNA was quantified using Qubit 3.0.

[0703] 2. Bisulfite Conversion and Library Preparation

[0704] Sodium bisulfite conversion of cytosine bases was performed using a bisulfite conversion kit (ThermoFisher, MECOV50). 20 ng of genomic DNA or ctDNA was converted and purified for downstream applications according to the manufacturer's instructions.

[0705] Extraction and quality control of sample DNA, and conversion of unmethylated cytosine on the DNA into bases that do not bind to guanine. In one or more embodiments, the conversion is performed using an enzymatic method, preferably deaminase treatment, or the conversion is performed using a non-enzymatic method, preferably treatment with bisulfite or bisulfate, more preferably treatment with calcium bisulfite, sodium bisulfite, potassium bisulfite, ammonium bisulfite, sodium bisulfate, potassium bisulfate, and ammonium bisulfate.

[0706] The library was constructed using the MethylTitan (CN201910515830) method. The MethylTitan method involves dephosphorylating bisulfite-converted DNA and ligating it to universal Illumina sequencing adapters with molecular markers (UMIs). Following second-strand synthesis and purification, the converted DNA undergoes semi-targeted PCR to amplify the desired target region. Following further purification, a sample-specific barcode and full-length Illumina sequencing adapters are added to the target DNA molecules via PCR. The resulting library was then quantified using Illumina's KAPA library quantification kit (KK4844) and sequenced on an Illumina sequencer. The MethylTitan library construction method can effectively enrich the required target fragments when using less DNA, especially cfDNA. At the same time, this method can well preserve the methylation status of the original DNA. Ultimately, by analyzing adjacent CpG methylated cytosines (a given target may have several to dozens of CpGs, depending on the given region), the entire methylation pattern of that specific region can be used as a unique marker, rather than comparing the status of a single base.

[0707] 3. Sequencing and Data Preprocessing

[0708] 1) Paired-end sequencing was performed on an Illumina Hiseq 2500 sequencer, with a sequencing load of 25–35 megabytes per sample. Adapter removal was performed on the paired-end 150bp sequencing data from the Illumina Hiseq 2500 sequencer using Trim_galore v 0.6.0 and cutadapt v2.1 software. The adapter sequence "AGATCGGAAGAGCACACGTCTGAACTCCAGTC (SEQ ID NO: 341)" was removed from the 3' end of Read 1, and the adapter sequence "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT (SEQ ID NO: 342)" was removed from the 3' end of Read 2. Bases with sequencing quality values ​​below 20 at both ends were removed. If a 3bp adapter sequence was present at the 5' end, the entire read was removed. Reads shorter than 30 bases after adapter removal were also removed.

[0709] 2) Paired-end reads were merged into single-end reads using Pear v0.9.6 software. Reads with at least 20 bases of overlap were merged, and reads shorter than 30 bases after merging were discarded.

[0710] 4. Sequencing Data Alignment

[0711] The reference genome data used in this example is from the UCSC database (UCSC: hg19, http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / hg19.fa.gz).

[0712] 1) First, hg19 was converted from cytosine to thymine (CT) and adenine to guanine (GA) using Bismark software, and the converted genomes were indexed using Bowtie2 software.

[0713] 2) The preprocessed data is also transformed into CT and GA.

[0714] 3) Use Bowtie2 software to align the transformed sequences to the transformed HG19 reference genome. The minimum seed sequence length is 20, and seed sequence mismatches are not allowed.

[0715] 5. Extracting Methylation Information

[0716] For each CpG site in the target region hg19, the methylation level corresponding to each site is obtained based on the above comparison results. The nucleotide numbers of the sites involved in the present invention correspond to the nucleotide position numbers of hg19.

[0717] 1) Calculation of methylation haplotype ratio (MHF). For each CpG site in the target region hg19, based on the above alignment results, the base sequence corresponding to each site in the reads is obtained. C indicates that the site is methylated, and T indicates that the site is unmethylated. The nucleotide numbering of the site in this application corresponds to the nucleotide position numbering of HG19. A target methylation region may have multiple methylation haplotypes. This value needs to be calculated for each methylation haplotype in the target region. The calculation formula of MHF is as follows:

[0718] MHFi,h=(Ni,h) / Ni

[0719] Where i represents the target methylation interval, h represents the target methylation haplotype, Ni represents the number of reads located in the target methylation interval, and Ni,h represents the number of reads containing the target methylation haplotype.

[0720] 2) Calculation of average methylation level (AMF): For each target region, the average methylation level in the region is calculated. The formula is as follows:

[0721] Where m is the total number of CpG sites in the target, i is each CpG site in the interval, and N C,i is the number of reads with C as the base of the CpG site (i.e., the number of reads with methylation at the site), N T,i is the number of reads with a T base at the CpG site (i.e., the number of sequencing reads with unmethylated residues at the site)

[0722] 6. Methylation haplotype data matrix

[0723] 1) The methylation haplotype ratio (MHF) and average methylation level (AMF) data of each sample in the training set and test set were merged into a data matrix, and missing values ​​were processed for each site with a depth lower than 200.

[0724] 2) Remove sites with missing value ratios higher than 10%.

[0725] 3) For missing values ​​in the data matrix, the KNN algorithm is used to interpolate missing data. First, the interpolator is trained using the KNN algorithm using the training set, and then the training set matrix and the test set matrix are interpolated separately.

[0726] 7. Screening methylation markers based on the feature matrix (Figure 1)

[0727] 1) The training set was randomly divided into three folds. Two of the folds were used as training sets to build the logistic regression model, and one of the folds was used as validation data for prediction. After repeating this process five times, the average validation set area under the curve (AUC) was calculated for each target region. For each target region, the feature with the highest AUC was selected as the representative feature for that region, and the features were sorted from highest to lowest AUC.

[0728] 2) Randomly split the training set into five parts and perform 5-fold cross-validation, repeating this 10 times, for incremental feature screening. Specifically, set aside one part of the training set as validation data, and use the remaining training set data as training data. Following the above sequence, the representative features of each region were added to the feature set. A logistic regression model was constructed using the four training data parts, and predictions were made on the validation data. After 10 iterations, the average AUC for the validation data was calculated.

[0729] 3) If the AUC of the training data increases, the methylation marker is retained; otherwise, it is removed. After the cycle, the resulting feature combination is used as the methylation marker combination. A new model is trained using all the training set data and validated using the test set data.

[0730] Example 2: Screening of colorectal cancer-specific methylation sites by methylation-targeted sequencing

[0731] The inventors screened 47 methylation markers from a large number of candidate regions. Their genomic locations and associated genes are shown in Table 1. The genomic location of a methylation marker refers to the genomic location of the methylation marker in the UCSC HG19 genome (https: / / genome.ucsc.edu / cgi-bin / hgTracks?db=hg19). The gene associated with a methylation marker refers to the gene with the closest TSS within 100 kb of the methylation marker.

[0732] The sequences shown in SEQ ID NOs: 1-47 were selected as methylation markers used in the examples. The methylation levels of all CpG sites in each methylation marker can be obtained using MethylTitan methylation sequencing. The mean methylation level of all CpG sites in each region, the methylation level of individual CpG sites, and the methylation haplotype combinations of CpG sites within a region can all be used as markers for colorectal cancer.

[0733] Figure 2 shows a boxplot of the methylation levels of 47 methylation markers in the training set for colorectal cancer and non-colorectal cancer samples. Figure 3 shows a boxplot of the methylation levels of 47 methylation markers in the test set for colorectal cancer and non-colorectal cancer samples. As can be seen from the boxplots in Figures 2 and 3, the average methylation levels within the methylation marker regions differ significantly between colorectal cancer and non-colorectal cancer cfDNA samples, demonstrating good discriminatory power.

[0734] The P values ​​in Table 7 are Mann Whitney U Test P values, and the methylation levels represent the median methylation levels of the cfDNA samples in that group. The statistical results in Table 7 also show that the methylation levels of the 47 methylation markers in this application are significantly different between colorectal cancer and non-colorectal cancer samples (P < 0.001), indicating that they are good colorectal cancer methylation markers.

[0735] Table 7 Methylation levels of methylation markers in colorectal cancer in the training set and test set

[0736] Example 3 Performance of a single methylation marker in determining the presence of colorectal cancer

[0737] In order to verify the performance of a single methylation marker in distinguishing colorectal cancer from non-colorectal cancer, the methylation level data of a single marker was used to train the model in the training set data of Example 1, and the performance of the model was verified using the test set samples.

[0738] The logistic regression model in the sklearn (V1.0.1) package in Python (V3.9.7) was used: model = LogisticRegression(). The model formula is as follows, where x is the methylation level of the sample target marker, w is the coefficient of different markers, b is the intercept value, and y is the model prediction score:

[0739] Use the samples in the training set for training: model.fit(Traindata, TrainPheno), where TrainData is the data of the target methylation site in the training set samples, TrainPheno is the trait of the training set samples (1 for colorectal cancer and 0 for no colorectal cancer), and the relevant threshold of the model is determined based on the samples in the training set.

[0740] Use the test set samples for testing: TestPred = model.predict_proba(TestData), where TestData is the data of the target methylation site in the test set samples, and TestPred is the model prediction score. Use this prediction score and the above threshold to determine whether the sample is colorectal cancer.

[0741] The effects of the logistic regression model of a single methylation marker in this example are shown in Table 8. As can be seen from the table, all methylation markers can achieve an AUC of more than 0.75 in both the test set and the training set, and are all good markers for colorectal cancer.

[0742] In this application, each single methylation marker can be used as a marker for colorectal cancer. Logistic regression modeling is used, and a threshold is set according to the training set. If the value is greater than the threshold, it is predicted to be colorectal cancer, otherwise it is predicted to be non-colorectal cancer. Both the training set and the test set can achieve good accuracy, specificity and sensitivity. Similar effects can be achieved using other machine learning models.

[0743] Table 8 Performance of logistic regression models for individual methylation markers

[0744] Example 4 Prediction results of all target methylation markers for colorectal cancer

[0745] In this example, the methylation levels of all 47 methylation markers were used to construct a logistic regression machine learning model, ALLMODEL, which accurately distinguished colorectal cancer from non-colorectal cancer samples. The specific steps were essentially the same as in Example 2, except that the model was fed with data from all 47 target methylation marker combinations (SEQ ID NOs: 1-47).

[0746] The distribution of model prediction scores in the training and test sets is shown in Figure 4. The ROC curve is shown in Figure 5. In the training set, the AUC for distinguishing between colorectal cancer and non-colorectal cancer samples reached 0.965. In the test set, the AUC for distinguishing between colorectal cancer and non-colorectal cancer samples reached 0.965. The threshold was set to 0.441. A value greater than this value was predicted as colorectal cancer, and vice versa. At this threshold, the training set accuracy was 0.894, the training set specificity was 0.932, and the training set sensitivity was 0.859. The test set accuracy was 0.892, the test set specificity was 0.914, and the test set sensitivity was 0.867. The model can effectively distinguish colorectal cancer from non-colorectal cancer samples.

[0747] Example 5 Prediction results of 9 methylation markers for colorectal cancer

[0748] In order to verify the effect of the relevant marker combination, this example selected 9 methylation markers, including SEQ ID NO:4, SEQ ID NO:11, SEQ ID NO:15, SEQ ID NO:18, SEQ ID NO:19, SEQ ID NO:30, SEQ ID NO:34, SEQ ID NO:37, and SEQ ID NO:41, from the methylation levels of all 47 methylation markers to construct a logistic regression machine learning model SUBMODEL1.

[0749] The method for constructing the machine learning model was also consistent with that in Example 3, but only the data of the above 9 markers in this example were used for the relevant samples. The model scores of the model in the training set and the test set are shown in Figure 6, and the ROC curve of the model is shown in Figure 7. It can be seen that the scores of the colorectal cancer and non-colorectal cancer samples in the training set and the test set are significantly different from the scores of other cancer types. The AUC for distinguishing adenocarcinoma from non-colorectal cancer samples in the training set of the model reached 0.921, and the AUC for distinguishing colorectal cancer from non-colorectal cancer samples in the test set reached 0.917. The threshold was set to 0.502. If the value was greater than this, the prediction was colorectal cancer, otherwise it was predicted as non-colorectal cancer. At this threshold, the training set accuracy was 0.854, the training set specificity was 0.822, and the training set sensitivity was 0.885. The test set accuracy was 0.800, the test set specificity was 0.800, and the test set sensitivity was 0.800, indicating the good performance of the combined model.

[0750] Example 6 Prediction results of six methylation markers for colorectal cancer

[0751] In order to verify the effect of the relevant marker combination, this example selected 6 methylation markers, SEQ ID NO: 1, SEQ ID NO: 21, SEQ ID NO: 29, SEQ ID NO: 36, SEQ ID NO: 44, and SEQ ID NO: 47, from the methylation levels of all 47 methylation markers to construct a logistic regression machine learning model SUBMODEL2.

[0752] The method for constructing the machine learning model is also consistent with that in Example 3, but only the data of the above 6 markers in this example are used for the relevant samples. The model scores of the model in the training set and the test set are shown in Figure 8, and the ROC curve of the model is shown in Figure 9. It can be seen that the scores of the colorectal cancer and non-colorectal cancer samples in the training set and the test set are significantly different. The AUC for distinguishing colorectal cancer and non-colorectal cancer samples in the training set of the model reached 0.916, and the AUC for distinguishing colorectal cancer and non-colorectal cancer samples in the test set reached 0.879. The threshold was set to 0.392. A value greater than this value was predicted as colorectal cancer, and vice versa, it was predicted as non-colorectal cancer. At this threshold, the training set accuracy was 0.841, the training set specificity was 0.877, and the training set sensitivity was 0.822. The test set accuracy was 0.785, the test set specificity was 0.714, and the test set sensitivity was 0.867, indicating the good performance of the combined model.

[0753] Example 7 Prediction results of 7 methylation markers for colorectal cancer

[0754] In order to verify the effect of the relevant marker combination, this example selected 7 methylation markers, including SEQ ID NO:6, SEQ ID NO:10, SEQ ID NO:13, SEQ ID NO:14, SEQ ID NO:22, SEQ ID NO:28, and SEQ ID NO:43, from the methylation levels of all 47 methylation markers to construct a logistic regression machine learning model SUBMODEL3.

[0755] The method for constructing the machine learning model was also consistent with that in Example 3, but only the data of the above 7 markers in this example were used for the relevant samples. The model scores of the model in the training set and the test set are shown in Figure 10, and the ROC curve of the model is shown in Figure 11. It can be seen that the scores of colorectal cancer and non-colorectal cancer samples in the training set and the test set are significantly different from those of other cancer types. The AUC for distinguishing adenocarcinoma from non-colorectal cancer samples in the training set of the model reached 0.911, and the AUC for distinguishing colorectal cancer from non-colorectal cancer samples in the test set reached 0.932. The threshold was set to 0.507. A value greater than this value was predicted as colorectal cancer, and vice versa, it was predicted as non-colorectal cancer. At this threshold, the training set accuracy was 0.848, the training set specificity was 0.973, and the training set sensitivity was 0.731. The test set accuracy was 0.815, the test set specificity was 0.971, and the test set sensitivity was 0.633, indicating the good performance of the combined model.

[0756] Example 8 Gastric Cancer Sample Processing and Methylation Marker Screening

[0757] A total of 206 gastric cancer patients and 393 healthy controls were collected, and all enrolled patients signed informed consent. These samples were divided into training and test sets according to a specific ratio. The training set was used to build the machine learning model described below, and the test set was used to test the model's performance. Sample information is shown in Table 9 below.

[0758] Table 9

[0759] The sample processing, strategy and data preprocessing process are the same as in Example 1. After calculating MHF to extract methylation information, the methylation haplotype data matrix is ​​generated:

[0760] 1) The methylation haplotype data of each sample in the training set and the test set were merged into a data matrix, and missing values ​​were processed for each site with a depth lower than 100.

[0761] 2) Remove sites with missing value ratios higher than 10%.

[0762] 3) For missing values ​​in the data matrix, the KNN algorithm is used to interpolate missing data.

[0763] Then, characteristic methylation haplotypes were found based on the grouping of training set samples:

[0764] 1) The dataset was randomly divided into three parts according to age matching.

[0765] 2) Set aside a portion of the data set as test data, and the rest of the data as training data.

[0766] 3) The training set was further divided into three parts and three-fold cross-validation was performed. Markers were selected based on the average AUC of the three-fold cross-validation.

[0767] 4) The marker obtained in step 3 is trained based on the Logistic Regression model using the training data, and the model effect is verified in the test data.

[0768] 5) The obtained methylation markers were annotated using Great.

[0769] The screened gastric cancer-specific methylation markers are as follows: SEQ ID NO: 48 located in or upstream and downstream of MPC1; SEQ ID NO: 49 located in or upstream and downstream of GALNT18; SEQ ID NO: 50 located in or upstream and downstream of TIMP2; SEQ ID NO: 51 located in or upstream and downstream of IRF4; SEQ ID NO: 52 located in or upstream and downstream of CACNA1C; SEQ ID NO: 53 located in or upstream and downstream of HOXD4; SEQ ID NO: 54 located in or upstream and downstream of TBX20; SEQ ID NO: 55 located in or upstream and downstream of NXPH1; SEQ ID NO: 56 located in or upstream and downstream of CYP26B1; SEQ ID NO: 57 located in or upstream and downstream of PITX1; SEQ ID NO: 58 located in or upstream and downstream of VAX1; SEQ ID NO: 59 located in or upstream and downstream of LHX5. NO:59; SEQ ID NO:60 located in ARC or upstream and downstream of the gene; SEQ ID NO:61 located in LZTS1 or upstream and downstream of the gene; SEQ ID NO:62 located in DLD or upstream and downstream of the gene; SEQ ID NO:63 located in FOXF2 or upstream and downstream of the gene; SEQ ID NO:64 located in GOLGA8A or upstream and downstream of the gene; SEQ ID NO:65 located in C1orf61 or upstream and downstream of the gene; SEQ ID NO:66 located in SOX7 or upstream and downstream of the gene; SEQ ID NO:67 located in NKX6-1 or upstream and downstream of the gene; SEQ ID NO:68 located in PCDHGC5 or upstream and downstream of the gene; SEQ ID NO:69 located in NR2F1 or upstream and downstream of the gene; SEQ ID NO:70 located in OTX2 or upstream and downstream of the gene; SEQ ID NO:71 located in CILP2 or upstream and downstream of the gene; SEQ ID NO:72 located in SLC6A5 or upstream and downstream of the gene; NO:72; SEQ ID NO:73 located within ELN or upstream and downstream of the gene; SEQ ID NO:74 located within CDH13 or upstream and downstream of the gene; SEQ ID NO:75 located within C1QTNF9 or upstream and downstream of the gene; SEQ ID NO:76 located within TFAP2C or upstream and downstream of the gene; SEQ ID NO:77 located within TACC2 or upstream and downstream of the gene; SEQ ID NO:78 located within CDH4 or upstream and downstream of the gene; SEQ ID NO:79 located within TNFRSF6B or upstream and downstream of the gene;SEQ ID NO: 80 located in LYL1 or upstream and downstream of the gene; SEQ ID NO: 81 located in SLC9A3R2 or upstream and downstream of the gene; SEQ ID NO: 82 located in NR2E1 or upstream and downstream of the gene; SEQ ID NO: 83 located in TBX3 or upstream and downstream of the gene; SEQ ID NO: 84 located in HMX3 or upstream and downstream of the gene; SEQ ID NO: 85 located in GCH1 or upstream and downstream of the gene; SEQ ID NO: 86 located in DCLK1 or upstream and downstream of the gene; SEQ ID NO: 87 located in HPCAL1 or upstream and downstream of the gene; SEQ ID NO: 88 located in SMARCA2 or upstream and downstream of the gene; SEQ ID NO: 89 located in LRP1 or upstream and downstream of the gene; SEQ ID NO: 90 located in TBX15 or upstream and downstream of the gene; SEQ ID NO: 91 located in TBX15 or upstream and downstream of the gene; SEQ ID NO: 92 located in NR2F2 or upstream and downstream of the gene. NO:92; SEQ ID NO:95 located within PRKAB2 or upstream or downstream of the gene; SEQ ID NO:94 located within LHX1 or upstream or downstream of the gene; SEQ ID NO:95 located within TBX2 or upstream or downstream of the gene.

[0770] The methylation levels of the methylation marker regions increased or decreased in cfDNA of gastric cancer patients (as shown in Table 10). The sequences of the 48 methylation markers obtained are as shown in SEQ ID NO: 48-95. The methylation levels of all CpG sites of each methylation marker can be obtained by the MethylTitan methylation sequencing method. The methylation levels calculated by MHF in each region can be used as a marker for gastric cancer.

[0771] Table 10 Methylation levels of methylation markers in gastric cancer in the training set and test set

[0772] The methylation levels within the methylation marker region for gastric cancer and non-gastric cancer populations in the test set are shown in Table 10. As can be seen from Table 10, the distribution of methylation levels within the methylation marker region is significantly different between gastric cancer and non-gastric cancer populations, with good discriminatory effect and a significant difference (P < 0.01), indicating that it is a good gastric cancer methylation marker.

[0773] Example 9 Performance of a single methylation marker in determining the presence of gastric cancer

[0774] To verify the performance of a single methylation marker in distinguishing whether a subject has gastric cancer, the methylation level data of a single marker was used to train a model in the training set data of Example 8, and the performance of the model was verified using the test set samples. The specific steps are as follows (Figure 12):

[0775] 1. Sequence preprocessing: For each target region, calculate the methylation haplotype ratio value of each MHF (Methylated Haplotype Fraction) in the region.

[0776] 2. Use the logistic regression model in the sklearn (V1.0.1) package in Python (V3.9.7): model = LogisticRegression(). The model formula is as follows, where x is the methylation level of the sample target marker, w is the coefficient of different markers, b is the intercept value, and y is the model prediction score:

[0777] 3. Use the samples in the training set for training: model.fit(Traindata, TrainPheno), where TrainData is the data of the target methylation site in the training set samples, TrainPheno is the trait of the training set samples (1 for gastric cancer and 0 for non-gastric cancer), and the relevant threshold of the model is determined based on the samples in the training set.

[0778] 4. Use the test set samples for testing: TestPred = model.predict_proba(TestData), where TestData is the data of the target methylation site in the test set samples, and TestPred is the model prediction score. Use this prediction score and the above threshold to determine whether the sample is gastric cancer.

[0779] 5. AUC indicator of statistical model.

[0780] The effects of the logistic regression model for a single target marker in this example are shown in Table 11. As can be seen from Table 11, all target markers can achieve an AUC of more than 0.5 in both the test set and the training set, and are all good gastric cancer markers.

[0781] Table 11 Performance of single marker logistic regression model

[0782] Example 10 Prediction results of all methylation markers for gastric cancer

[0783] This example uses the methylation levels of all 48 target gastric cancer markers to construct a logistic regression machine learning model, accurately distinguishing whether a subject has gastric cancer from the data. The specific steps are basically the same as those in Example 2, except that the data of all 48 target marker combinations (SEQ ID NOs: 48-95) are used as input to the model.

[0784] The distribution of model prediction scores in the training and test sets is shown in Figure 13. The ROC curve is shown in Figure 14. In the test set, the AUC for distinguishing between gastric cancer and non-cancer samples reached 0.922, demonstrating good discrimination between gastric cancer and non-cancer samples. When the threshold was set to 0.53, scores above this threshold were predicted as gastric cancer, while scores below this threshold were predicted as non-cancer. The specificity in the training set was 95%, and the sensitivity in the test set reached 73%, demonstrating the excellent performance of this combined model.

[0785] Example 11 Prediction results of 19 methylation markers for gastric cancer

[0786] To verify the effect of the relevant marker combination, this example selected 19 target markers, including SEQ ID NO: 50, 55, 60, 62, 64, 66, 69, 72, 76, 78, 84, 85, 87, 88, 89, 90, 92, 94, and 95, from the methylation levels of all 48 methylation markers to construct a logistic regression machine learning model.

[0787] The method for constructing the machine learning model was consistent with that in Example 9, but only the data for the aforementioned 19 target markers were used for the relevant samples. The model scores for the training and test sets are shown in Figure 15 , and the receiver operating characteristic (ROC) curve for the model is shown in Figure 16 . It can be seen that the model scores for gastric cancer and non-gastric cancer samples in the training and test sets showed significant differences. The AUC for the test set reached 0.919, demonstrating the good performance of this combined model. When the threshold was set to 0.54, gastric cancer was predicted for samples above this value, and no gastric cancer was predicted for samples below this value. The specificity was 95% in the training set, and the sensitivity reached 78% in the test set, demonstrating the good performance of this combined model.

[0788] Example 12 Prediction results of 19 methylation markers for gastric cancer

[0789] To verify the effect of the relevant marker combination, this example selected 19 target markers, including SEQ ID NOs: 49, 53, 54, 55, 59, 62, 66, 72, 75, 79, 80, 83, 84, 87, 89, 90, 91, 93, and 95, from the methylation levels of all 48 methylation markers to construct a logistic regression machine learning model.

[0790] The method for constructing the machine learning model was consistent with that in Example 2, but only the data for the aforementioned 19 target markers were used for the relevant samples. The model scores for the training and test sets are shown in Figure 17, and the receiver operating characteristic (ROC) curve for the model is shown in Figure 18. It can be seen that the model scores for gastric cancer and non-gastric cancer samples in the training and test sets showed significant differences. The AUC for the test set reached 0.913, demonstrating the good performance of this combined model. When the threshold was set to 0.49, gastric cancer was predicted for samples above this value, and no gastric cancer was predicted for samples below this value. The specificity was 95% in the training set, and the sensitivity reached 65% in the test set, demonstrating the good performance of this combined model.

[0791] Example 13 Prediction results of 8 methylation markers for gastric cancer

[0792] In order to verify the effect of the relevant marker combination, this example selected 8 target markers, including SEQ ID NO:50, SEQ ID NO:60, SEQ ID NO:61, SEQ ID NO:67, SEQ ID NO:69, SEQ ID NO:75, SEQ ID NO:77, and SEQ ID NO:84, from the methylation levels of all 48 methylation markers to construct a logistic regression machine learning model.

[0793] The method for constructing the machine learning model was consistent with that in Example 9, but only the data for the eight target markers described above were used for the relevant samples. The model scores for the training and test sets are shown in Figure 19, and the receiver operating characteristic (ROC) curve for the model is shown in Figure 20. It can be seen that the model scores for gastric cancer and non-gastric cancer samples in the training and test sets showed significant differences. The AUC for the test set reached 0.872, demonstrating the good performance of this combined model. When the threshold was set to 0.46, gastric cancer was predicted for samples above this value, and no gastric cancer was predicted for samples below this value. The specificity was 95% in the training set, and the sensitivity reached 56% in the test set, demonstrating the good performance of this combined model.

[0794] Example 14 Prediction results of five methylation markers for gastric cancer

[0795] In order to verify the effect of the relevant marker combination, this example selected 5 target markers, SEQ ID NO: 50, SEQ ID NO: 60, SEQ ID NO: 74, SEQ ID NO: 77, and SEQ ID NO: 82, from the methylation levels of all 48 methylation markers to construct a logistic regression machine learning model.

[0796] The method for constructing the machine learning model was consistent with that in Example 9, but only the data for the five target markers described above were used for the relevant samples. The model scores for the training and test sets are shown in Figure 21, and the model ROC curve is shown in Figure 22. It can be seen that the model scores for gastric cancer and non-gastric cancer samples in the training and test sets showed significant differences. The model achieved an AUC of 0.856 for the test set, demonstrating the good performance of this combined model. A threshold of 0.52 was set, with gastric cancer predicted for samples above this value and the absence of gastric cancer predicted for samples below this value. The specificity was 95% in the training set, and the sensitivity reached 48% in the test set, demonstrating the good performance of this combined model.

[0797] Example 15 Esophageal cancer sample processing and methylation marker screening

[0798] A total of 162 blood samples from patients with esophageal cancer and 393 blood samples from patients without esophageal cancer were collected. All enrolled patients signed informed consent forms. The sample information is shown in Table 12.

[0799] Table 12

[0800] The sample processing, strategy and data preprocessing process are the same as in Example 1. After the methylation haplotype data matrix is ​​formed, the characteristic methylation haplotype is found according to the grouping of the training set samples:

[0801] 1) Perform logistic regression analysis on the phenotype of each methylation haplotype and construct a logistic regression model. Specifically, for each target region, calculate the methylation haplotype ratio of each MHF (Methylated Haplotype Fraction) within the region. Use the statmodels package (0.12.0) in Python (v3.6.9) to construct a logistic regression model and calculate the logistic regression coefficient. The command line is:

[0802] import statsmodels.api as sm

[0803] logist_model=sm.Logit(Y,sm.add_constant(X)).fit

[0804] pvlaue = logist_model.pvalues. X represents the methylation haplotype value corresponding to each sample, Y represents the classification label corresponding to each sample, and pvalue represents the significance test value of the logistic regression. For each amplified target region, the methylation marker corresponding to the MHF with the smallest regression coefficient is screened to form the candidate methylation haplotype.

[0805] 2) Randomly divide the training set into ten parts and perform ten-fold cross-validation incremental feature screening.

[0806] 3) Set aside one data set from the training set as test data, and use the remaining training set data as training data. For each region, candidate methylation haplotypes were ranked from highest to lowest based on regression coefficient significance. Each methylation haplotype was added one at a time, and a polynomial kernel SVM model was constructed using the nine training data sets to predict the test data.

[0807] 4) Repeat step 3 10 times for all data sets, calculating the AUC for the test data each time. After 10 repetitions, calculate the average AUC for all 10 iterations. If the AUC for the training data increases, retain the candidate methylation haplotype as a characteristic methylation marker; otherwise, discard it. The resulting methylation markers were annotated using the GREAT tool (great.stanford.edu / great / public-3.0.0 / html / index.php) (see Table 13).

[0808] The target genes in the methylation markers were annotated using the GREAT tool (great.stanford.edu / great / public-3.0.0 / html / 3.0.0 / html / index.php). During the GREAT analysis, the marker region was associated with the adjacent genes and the region was annotated with the adjacent genes. The association was divided into two processes. First, the regulatory domain of each gene was found, and then the genes covering the regulatory domain of the region were associated with the region. For example, SKI (+2024) can represent a marker at 93 bp downstream of the transcription start site (TSS) of the SKI gene, and EPS8L3 (-28150) can represent a marker at 28150 bp upstream of the transcription start site (TSS) of the EPS8L3 gene.

[0809] 5) The feature combination corresponding to the median of the average AUC under different numbers of features in the training set was taken as the final methylation marker (Table 13).

[0810] Table 13

[0811] The methylation levels of the methylation marker regions increased or decreased in cfDNA of patients with esophageal cancer (as shown in Table 14). The sequences of the 43 methylation markers obtained are as shown in SEQ ID NO: 96-138. The methylation levels of all CpG sites of each methylation marker can be obtained by the MethylTitan methylation sequencing method. The mean of the methylation levels of all CpG sites in each region and the methylation level of a single CpG site can be used as a marker for esophageal cancer.

[0812] Table 14 Methylation levels of methylation markers in esophageal cancer in the training set and test set

[0813] The average methylation levels in the methylation marker region of the test set for esophageal cancer and non-esophageal cancer populations are shown in Table 14. As can be seen from Table 14, the distribution of the average methylation levels in the methylation marker region is significantly different between esophageal cancer and non-esophageal cancer populations, with good discriminatory effect and a significant difference (P < 0.01), indicating that it is a good methylation marker for esophageal cancer.

[0814] Example 16 Performance of a single methylation marker in determining the presence of esophageal cancer

[0815] To verify the performance of a single methylation marker in distinguishing between esophageal cancer and non-esophageal cancer, the model was trained using the methylation level data of a single marker in the training set data of Example 1, and the performance of the model was verified using the test set samples. The specific steps are as follows (Figure 12):

[0816] 1. Sequence preprocessing: For each target region, calculate the methylation haplotype ratio value of each MHF (Methylated Haplotype Fraction) in the region.

[0817] 2. Use the logistic regression model in the sklearn (V1.0.1) package in Python (V3.9.7): model = LogisticRegression(). The model formula is as follows, where x is the methylation level of the sample target marker, w is the coefficient of different markers, b is the intercept value, and y is the model prediction score:

[0818] 3. Use the samples in the training set for training: model.fit(Traindata, TrainPheno), where TrainData is the data of the target methylation site in the training set samples, TrainPheno is the trait of the training set samples (esophageal cancer is 1, no esophageal cancer is 0), and the relevant threshold of the model is determined based on the samples in the training set.

[0819] 4. Use the test set samples for testing: TestPred = model.predict_proba(TestData)[:,1], where TestData is the data of the target methylation site in the test set samples, and TestPred is the model prediction score. Use this prediction score and the above threshold to determine whether the sample is esophageal cancer.

[0820] 5. AUC indicator of statistical model.

[0821] The effects of the single marker logistic regression model in this embodiment are shown in Table 15. As can be seen from the table, all markers can achieve an AUC of more than 0.55 in both the test set and the training set, and are all good esophageal cancer markers.

[0822] Table 15 Performance of single marker logistic regression model

[0823] Example 17 Prediction results of all methylation markers for esophageal cancer

[0824] In this example, a logistic regression machine learning model was constructed using the methylation levels of all 43 methylation markers to accurately distinguish between esophageal cancer and non-esophageal cancer samples. The specific steps were essentially the same as those in Example 16, except that the model was fed with data from all 43 target methylation marker combinations (SEQ ID Nos: 96-138).

[0825] The distribution of model prediction scores in the training and test sets is shown in Figure 23. The ROC curve is shown in Figure 24. In the test set, the AUC for distinguishing between esophageal cancer and non-esophageal cancer samples reached 0.935. When the training set specificity was 95%, the test set sensitivity reached 84.3%. The threshold was set to 0.383. If the value was greater than this, esophageal cancer was predicted, and vice versa, esophageal cancer was predicted. This shows that the methylation markers of this application can effectively distinguish esophageal cancer from non-esophageal cancer samples.

[0826] Example 18 Prediction results of 16 methylation markers for esophageal cancer

[0827] To verify the effect of the relevant marker combination, this example selected 16 methylation markers, including SEQ ID Nos: 100, 103, 109, 110, 113, 120, 121, 125, 128, 130, 132, 133, 134, 135, 137, and 138, from the methylation levels of all 43 methylation markers to construct a logistic regression machine learning model.

[0828] The method for constructing the machine learning model was also consistent with that in Example 16, but only the data of the above 16 markers in that example were used for the relevant samples. The model scores for the training and test sets are shown in Figure 25, and the ROC curve for the model is shown in Figure 26. It can be seen that in the training and test sets, the scores of esophageal cancer and non-esophageal cancer samples were significantly different from those of other cancer types. The AUC of the model test set reached 0.920. When the threshold was set to 0.431, samples above this value were predicted to be esophageal cancer, and samples below this value were predicted to be non-esophageal cancer. The specificity in the training set was 95%, and the sensitivity in the test set reached 75.8%, indicating that the model has good performance when composed of a combination of multiple markers selected from the methylation markers of this application.

[0829] Example 19 Prediction results of 17 methylation markers for esophageal cancer

[0830] To verify the effect of the relevant marker combination, this example selected 17 methylation markers, including SEQ ID Nos: 102, 107, 108, 110, 112, 120, 121, 123, 124, 125, 130, 131, 132, 133, 134, 135, and 137, from the methylation levels of all 43 methylation markers to construct a logistic regression machine learning model.

[0831] The method for constructing the machine learning model was also consistent with that in Example 16, but only the data for the 16 markers described in that example were used for the relevant samples. The model scores for the training and test sets are shown in Figure 27, and the ROC curve for the model is shown in Figure 28. It can be seen that in both the training and test sets, the scores for esophageal cancer and non-esophageal cancer samples were significantly different from those for other cancer types. The AUC for the model test set reached 0.916. When the threshold was set to 0.431, samples above this value were predicted to be esophageal cancer, while samples below this value were predicted to be non-esophageal cancer. The specificity in the training set was 95%, and the sensitivity in the test set reached 59.4%, demonstrating that the model has good performance when composed of a combination of multiple markers selected from the methylation markers of this application.

[0832] This application uses the methylation levels of relevant genes in plasma cfDNA to obtain 43 methylated nucleic acid fragments with significant differences. Based on the above single methylated nucleic acid fragment markers, or a marker group composed of multiple methylated nucleic acid fragments, an esophageal cancer risk prediction model is established. It can effectively identify esophageal cancer with high sensitivity and specificity, and is suitable for the screening and diagnosis of esophageal cancer.

[0833] Example 20 Prediction results of 27 methylation markers for esophageal cancer

[0834] This example uses the methylation levels of 27 methylation markers to construct a logistic regression machine learning model, which accurately distinguishes esophageal cancer from non-esophageal cancer samples in the data. The specific steps are basically the same as those in Example 16, except that the data input model is a combination of 27 target methylation markers (SEQ ID No: 98, 100, 102, 103, 107, 108, 109, 111, 112, 113, 114, 116, 117, 121, 123, 124, 125, 127, 128, 130, 131, 133, 134, 135, 136, 137 and 138).

[0835] The distribution of model prediction scores in the training and test sets is shown in Figure 29. The ROC curve is shown in Figure 30. In the test set, the AUC for distinguishing between esophageal cancer and non-esophageal cancer samples reached 0.930. When the training set specificity was 95%, the test set sensitivity reached 57.6%. The threshold was set to 0.425. If the value was greater than this, esophageal cancer was predicted, otherwise it was predicted as non-esophageal cancer. This shows that the methylation markers of this application can effectively distinguish esophageal cancer from non-esophageal cancer samples.

[0836] Example 21 Prediction results of 7 methylation markers for esophageal cancer

[0837] In order to verify the effect of the relevant marker combination, this example selected 7 methylation markers, SEQ ID No: 102, 109, 116, 117, 127, 134 and 135, from the methylation levels of the 27 methylation markers in Example 20 to construct a logistic regression machine learning model.

[0838] The method for constructing the machine learning model was also consistent with that in Example 16, but only the data of the above seven markers in that example were used for the relevant samples. The model scores for the training and test sets are shown in Figure 31, and the ROC curve for the model is shown in Figure 32. It can be seen that in the training and test sets, the scores of esophageal cancer and non-esophageal cancer samples were significantly different from those of other cancer types. The AUC of the model test set reached 0.900. When the threshold was set to 0.50, samples above this value were predicted to be esophageal cancer, and samples below this value were predicted to be non-esophageal cancer. The specificity in the training set was 95%, and the sensitivity in the test set reached 57.6%, indicating that the model has good performance when composed of a combination of multiple markers selected from the methylation markers of this application.

[0839] Example 22 Prediction results of 7 methylation markers for esophageal cancer

[0840] In order to verify the effect of the relevant marker combination, this example selected 7 methylation markers, SEQ ID No: 121, 125, 130, 133, 134, 135 and 136, from the methylation levels of the 27 methylation markers in Example 20 to construct a logistic regression machine learning model.

[0841] The method for constructing the machine learning model was also consistent with that in Example 16, but only the data for the seven markers described in that example were used for the relevant samples. The model scores for the training and test sets are shown in Figure 33, and the ROC curve for the model is shown in Figure 34. It can be seen that in both the training and test sets, the scores for esophageal cancer and non-esophageal cancer samples differed significantly from those for other cancer types. The model achieved an AUC of 0.890 for the test set. When the threshold was set to 0.594, samples above this value were predicted to be esophageal cancer, while samples below this value were predicted to be non-esophageal cancer. The specificity was 95% in the training set, and the sensitivity reached 65.6% in the test set, demonstrating that the model exhibits good performance when composed of a combination of multiple markers selected from the methylation markers of this application.

[0842] Example 23 Prediction results of 23 methylation markers for esophageal cancer

[0843] The methylation levels of the 23 methylation markers used in this example were used to construct a logistic regression machine learning model, which accurately distinguished esophageal cancer from non-esophageal cancer samples in the data. The specific steps were basically the same as those in Example 16, except that the data of all 23 target methylation marker combinations (SEQ ID No: 96, 97, 99, 101, 104, 105, 106, 110, 115, 118, 119, 120, 121, 122, 125, 126, 129, 130, 132, 133, 134, 135 and 137) were used as input to the model.

[0844] The distribution of model prediction scores in the training and test sets is shown in Figure 35. The ROC curve is shown in Figure 36. In the test set, the AUC for distinguishing between esophageal cancer and non-esophageal cancer samples reached 0.934. When the training set specificity was 95%, the test set sensitivity reached 64%. The threshold was set to 0.41. If the value was greater than this, esophageal cancer was predicted, otherwise it was predicted as non-esophageal cancer. This shows that the methylation markers of this application can effectively distinguish esophageal cancer from non-esophageal cancer samples.

[0845] Example 24 Prediction results of 17 methylation markers for esophageal cancer

[0846] In order to verify the effect of the relevant marker combination, this example selected 17 methylation markers, including SEQ ID No: 96, 97, 99, 104, 105, 106, 110, 118, 120, 122, 125, 126, 129, 130, 132, 133 and 135, from the methylation levels of the methylation markers in Example 23 to construct a logistic regression machine learning model.

[0847] The method for constructing the machine learning model is also consistent with that in Example 16, but the relevant samples only used the data of the above 17 markers in this example. The model scores of the model in the training set and the test set are shown in Figure 37, and the ROC curve of the model is shown in Figure 38. It can be seen that the scores of esophageal cancer and non-esophageal cancer samples in the training set and the test set of the model are significantly different from the scores of other cancer types. The AUC of the model test set reached 0.900. When the threshold was set to 0.508, it was predicted to be esophageal cancer if it was greater than this value, and it was predicted to be non-esophageal cancer if it was less than this value. The specificity in the training set was 95%, and the sensitivity in the test set reached 56.3%, indicating that the model has good performance when a combination of multiple markers is selected from the methylation markers of this application.

[0848] Example 25 Prediction results of 15 methylation markers for esophageal cancer

[0849] In order to verify the effect of the relevant marker combination, this example selected 15 methylation markers, including SEQ ID No: 96, 97, 99, 105, 110, 118, 119, 120, 121, 122, 129, 130, 134, 135 and 137, from the methylation levels of Example 23 to construct a logistic regression machine learning model.

[0850] The method for constructing the machine learning model was also consistent with that in Example 16, but only the data for the 15 markers described in that example were used for the relevant samples. The model scores for the training and test sets are shown in Figure 39, and the ROC curve for the model is shown in Figure 40. It can be seen that in both the training and test sets, the scores for esophageal cancer and non-esophageal cancer samples were significantly different from those for other cancer types. The AUC for the model test set reached 0.906. When the threshold was set to 0.511, samples above this value were predicted to be esophageal cancer, while samples below this value were predicted to be non-esophageal cancer. The specificity in the training set was 95%, and the sensitivity in the test set reached 59.4%, demonstrating that the model has good performance when composed of a combination of multiple markers selected from the methylation markers of this application.

[0851] Example 26 Liver cancer sample processing and methylation marker screening

[0852] A total of 276 HCC blood samples and 393 HCC-free blood samples were collected. All enrolled patients signed informed consent forms. Sample information is shown in Table 16.

[0853] Table 16

[0854] The sample processing, strategy and data preprocessing process are the same as in Example 1. After the methylation haplotype data matrix is ​​formed, the characteristic methylation haplotype is found according to the grouping of the training set samples:

[0855] 1) Perform logistic regression analysis on the phenotype of each methylation haplotype and construct a logistic regression model. Specifically, for each target region, calculate the methylation haplotype ratio of each MHF (Methylated Haplotype Fraction) within the region. Use the statmodels package (0.12.0) in Python (v3.6.9) to construct a logistic regression model and calculate the logistic regression coefficient. The command line is:

[0856] import statsmodels.api as sm

[0857] logist_model=sm.Logit(Y,sm.add_constant(X)).fit

[0858] pvlaue = logist_model.pvalues. X represents the methylation haplotype value corresponding to each sample, Y represents the classification label corresponding to each sample, and pvalue represents the significance test value of the logistic regression. For each amplified target region, the methylation marker corresponding to the MHF with the smallest regression coefficient is screened to form the candidate methylation haplotype.

[0859] 2) Randomly divide the training set into ten parts and perform ten-fold cross-validation incremental feature screening.

[0860] 3) Set aside one data set from the training set as test data, and use the remaining training set data as training data. For each region, candidate methylation haplotypes were ranked from highest to lowest based on regression coefficient significance. Each methylation haplotype was added one at a time, and a polynomial kernel SVM model was constructed using the nine training data sets to predict the test data.

[0861] 4) Repeat step 3 10 times for all data sets, calculating the AUC for the test data each time. After 10 repetitions, calculate the average AUC for all 10 iterations. If the AUC for the training data increases, retain the candidate methylation haplotype as a characteristic methylation marker; otherwise, discard it. The resulting methylation markers were annotated using the GREAT tool (great.stanford.edu / great / public-3.0.0 / html / index.php) (see Table 17).

[0862] The target genes in the methylation markers were annotated using the GREAT tool (great.stanford.edu / great / public-3.0.0 / html / 3.0.0 / html / index.php). During the GREAT analysis, the marker region was associated with the adjacent genes and the region was annotated with the adjacent genes. The association was divided into two processes. First, the regulatory domain of each gene was found, and then the genes covering the regulatory domain of the region were associated with the region. For example, SKI (+2024) can represent a marker at 93 bp downstream of the transcription start site (TSS) of the SKI gene, and EPS8L3 (-28150) can represent a marker at 28150 bp upstream of the transcription start site (TSS) of the EPS8L3 gene.

[0863] 5) The feature combination corresponding to the median of the average AUC under different numbers of features in the training set was taken as the final methylation marker (Table 17).

[0864] Table 17

[0865] The methylation levels of the methylation marker regions increased or decreased in cfDNA of patients with liver cancer (as shown in Table 18). The sequences of the 202 methylation markers obtained are as shown in SEQ ID NO: 139-340. The methylation levels of all CpG sites of each methylation marker can be obtained by the MethylTitan methylation sequencing method. The mean of the methylation levels of all CpG sites in each region, as well as the methylation levels of individual CpG sites, can be used as markers for liver cancer.

[0866] Table 18 Methylation levels of methylation markers in liver cancer in the training set and test set

[0867] The average methylation levels within the methylation marker region for the test set of liver cancer and non-liver cancer subjects are shown in Table 18. As can be seen from Table 18, the distribution of the average methylation levels within the methylation marker region is significantly different between liver cancer and non-liver cancer subjects, demonstrating good discriminatory effects with significant differences (P < 0.01), indicating that it is a good liver cancer methylation marker.

[0868] Example 27 Performance of a Single Methylation Marker in Determining the Presence of Liver Cancer

[0869] To verify the performance of a single methylation marker in distinguishing between liver cancer and non-liver cancer, the model was trained using the methylation level data of a single marker in the training set data of Example 26, and the performance of the model was verified using the test set samples. The specific steps are as follows (Figure 12):

[0870] 1. Sequence preprocessing: For each target region, calculate the methylation haplotype ratio value of each MHF (Methylated Haplotype Fraction) in the region.

[0871] 2. Use the logistic regression model...

Claims

1. A colorectal cancer methylation marker, which is an isolated mammalian nucleic acid molecule, the sequence of which comprises: (1) any one or more or all of SEQ ID NOs: 1-47 or their complementary sequences or variants thereof, wherein the variants have at least 70% sequence identity with the corresponding sequences and the methylation sites in the variants have not been mutated, or (2) a sequence of (1) that has been treated to convert unmethylated cytosine into a cytosine residue that is identical to a cytosine residue in the guanine residue. Purines have lower binding capacity than cytosine bases. Preferably, item (1) is selected from any one of the following groups: (1.1) any one or more or all of the following sequences: SEQ ID NO: 4, SEQ ID NO: 11, SEQ ID NO: 15, SEQ ID NO: 18, SEQ ID NO: 19, SEQ ID NO: 30, SEQ ID NO: 34, SEQ ID NO: 37, SEQ ID NO: 41, or complementary sequences or variants thereof, optionally further comprising any one or more of the remaining sequences in SEQ ID NO: 1-47 or complementary sequences or variants thereof, (1.2) any one or more or all of the following sequences: SEQ ID NO: 1, SEQ ID NO: 21, SEQ ID NO: 29, SEQ ID NO: 36, SEQ ID NO: 44, SEQ ID NO: 47, or complementary sequences or variants thereof, optionally including any one or more of the remaining sequences in SEQ ID NO: 1-47, (1.3) any one or more or all of the following sequences: SEQ ID NO: 6, SEQ ID NO: 10, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 22, SEQ ID NO: 28, SEQ ID NO: 43, or complementary sequences or variants thereof, and optionally further comprising any one or more of the remaining sequences in SEQ ID NO: 1-47 or complementary sequences or variants thereof.

2. A reagent for screening the risk of colorectal cancer, diagnosing colorectal cancer, and evaluating the prognosis of colorectal cancer by detecting DNA methylation, the reagent comprising a reagent for detecting the methylation level of a marker in a sample to be tested, the marker being a DNA sequence and the upstream 5kb and downstream 5kb of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, the DNA sequence comprising one or more or all of the following gene sequences: (p) TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1 , PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3 , VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2, Preferably, The DNA sequence comprises one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally further comprises one or more or all of the other gene sequences in (p); or The DNA sequence comprises one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally further comprises one or more or all of the other gene sequences in (p); or The DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p). More preferably, the agent has one or more characteristics selected from the group consisting of: The marker comprises at least 3 CpG dinucleotides, The fragment length is 1-1000 bp, preferably 1-700 bp, The fragment is the promoter region of the gene sequence or a portion thereof, The reagent comprises a primer molecule that hybridizes to the marker or its converted sequence, The reagent comprises a probe molecule that hybridizes to the marker or a converted sequence thereof, The sample is from a mammal.

3. A medium recording a DNA sequence or a fragment thereof and / or methylation information thereof, wherein the DNA sequence comprises: (i) one or more or all of the following gene sequences: (p) TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2, or (ii) a sequence treated according to (i), wherein the treatment converts unmethylated cytosine into a base having a lower binding ability with guanine than with cytosine, Preferably, The DNA sequence comprises one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally further comprises one or more or all of the other gene sequences in (p); or The DNA sequence comprises one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally further comprises one or more or all of the other gene sequences in (p); or The DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p). More preferably, the medium has one or more characteristics selected from the following: The marker comprises at least 3 CpG dinucleotides, The fragment length is 1-1000 bp, preferably 1-700 bp, The fragment is the promoter region of the gene sequence or a portion thereof, The medium is a carrier printed with the DNA sequence or its fragment and / or its methylation information, including a card, such as a paper, plastic, metal, or glass card. The medium is a computer-readable medium storing the sequence and / or its methylation information and a computer program. When the computer program is executed by a processor, the following steps are implemented: comparing the methylation sequencing data of the sample with the sequence or information, thereby obtaining the presence, content and / or methylation level of the nucleic acid molecule containing the sequence in the sample, and based on this, screening for colorectal cancer risk, diagnosing colorectal cancer, and evaluating the prognosis of colorectal cancer.

4. Use of the following (a) and optionally (b) in the preparation of a kit for screening colorectal cancer risk, diagnosing colorectal cancer, and assessing the prognosis of colorectal cancer, (a) a reagent or device for determining the methylation level of a marker in a sample of a subject, wherein the marker is a DNA sequence and 5 kb upstream and 5 kb downstream of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, (b) the marker or a nucleic acid molecule thereof treated so as to convert unmethylated cytosine into a base having a lower binding ability with guanine than with cytosine, in, The DNA sequence includes one or more or all of the following gene sequences: (p)TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2.

5. The use according to claim 4, characterized in that The DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p), or The DNA sequence comprises one or more or all of TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally further comprises one or more or all of the other gene sequences in (p), or The DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p).

6. The use according to claim 4 or 5, characterized in that The reagent comprises a primer molecule that hybridizes to the marker or a converted sequence thereof, and / or the reagent comprises a probe molecule that hybridizes to the marker or a converted sequence thereof, Preferably, the use further has one or more features selected from the following: The marker comprises at least 3 CpG dinucleotides, The fragment length is 1-1000 bp, preferably 1-700 bp, The fragment is the promoter region of the gene sequence or a portion thereof, The device comprises the medium according to claim 3, The subject is a mammal, The sample is from mammalian tissue, cells or body fluid, preferably blood, The DNA sequence is: a sequence of a corresponding marker in the genome, or a converted sequence thereof, or a sequence thereof treated with a methylation-sensitive restriction endonuclease, wherein the conversion converts unmethylated cytosine into a base having a lower binding ability with guanine than cytosine. The kit also includes PCR reaction reagents, The kit further comprises other reagents for detecting DNA methylation, wherein the other reagents are selected from one or more of the following methods: PCR based on bisulfite conversion, DNA sequencing, methylation-sensitive restriction endonuclease analysis, fluorescence quantitative method, methylation-sensitive high-resolution melting curve method, chip-based methylation profiling, mass spectrometry; preferably, the other reagents are selected from one or more of the following: bisulfite, bisulfite, acid sulfite or pyrosulfite or their derivatives, methylation-sensitive or insensitive restriction endonuclease, enzyme digestion buffer, fluorescent dye, fluorescence quencher, fluorescent reporter, exonuclease, alkaline phosphatase, internal standard and control; Preferably, the screening of colorectal cancer risk, diagnosis of colorectal cancer, and assessment of colorectal cancer prognosis include: comparing the methylation level of the marker with the corresponding reference level, and screening the risk of colorectal cancer, diagnosing colorectal cancer, and assessing the prognosis of colorectal cancer based on the score.

7. A method for screening the risk of colorectal cancer, diagnosing colorectal cancer, or assessing the prognosis of colorectal cancer, comprising: (1) Detecting the methylation level of a marker in a sample of a subject, wherein the marker is a DNA sequence and the upstream 5 kb and downstream 5 kb of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, wherein the DNA sequence includes one or more or all of the following gene sequences: (p) TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IR X1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYS L4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2, (2) comparing the methylation level of the marker in step (1) with the corresponding reference level, (3) Screening for colorectal cancer risk, diagnosing colorectal cancer, or evaluating the prognosis of colorectal cancer based on the comparison results; Preferably, The DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p), or The DNA sequence comprises one selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2 or more or all, optionally further comprising one or more or all of the other gene sequences in (p), or The DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p). More preferably, the method has one or more features selected from the group consisting of: The marker comprises at least 3 CpG dinucleotides, The fragment length is 1-1000 bp, preferably 1-700 bp, The fragment is the promoter region of the gene sequence or a portion thereof, Preferably, the method further comprises, before step (1), a step of obtaining a biological sample containing DNA from a subject; preferably, step (1) comprises performing the detection using primer molecules, probe molecules and / or media, The comparison in step (2) comprises: directly comparing the methylation level of the marker in step (1) with the reference level, or calculating a score and comparing the score of the methylation level of the marker with the corresponding reference score; preferably, the score is calculated by a logistic regression model, Step (3) comprises: when the methylation level of the marker is greater than the reference level, or the methylation level score is greater than the reference score, the subject is at risk of developing colorectal cancer, has colorectal cancer, or has a poor prognosis for colorectal cancer, Preferably, the detection includes: PCR based on bisulfite conversion, DNA sequencing, methylation-sensitive restriction endonuclease analysis, fluorescence quantitative method, methylation-sensitive high-resolution melting curve method, chip-based methylation profiling, mass spectrometry, The sample is from mammalian tissue, cells or body fluid, preferably blood, The DNA sequence is: a sequence of a corresponding marker in a genome, or a converted sequence thereof, or a sequence thereof treated with a methylation-sensitive restriction endonuclease, wherein the conversion converts unmethylated cytosine therein into a base that does not bind to guanine.

8. A kit for screening the risk of colorectal cancer, diagnosing colorectal cancer, or assessing the prognosis of colorectal cancer, comprising: (a) a reagent or device for determining the methylation level of a marker in a sample of a subject, wherein the marker is a DNA sequence and 5 kb upstream and 5 kb downstream of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, and Optionally, (b) the marker or a treated nucleic acid molecule thereof, wherein the treatment converts unmethylated cytosine into a base having a lower binding ability with guanine than with cytosine, in, The DNA sequence includes one or more or all of the following gene sequences: (p)TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IRX1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYSL4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2, Preferably, The DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p), or The DNA sequence comprises one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally further comprises one or more or all of the other gene sequences in (p), or The DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p). More preferably, the method has one or more features selected from the group consisting of: The marker comprises at least 3 CpG dinucleotides, The fragment length is 1-1000 bp, preferably 1-700 bp, The fragment is the promoter region of the gene sequence or a portion thereof, The reagent comprises a primer molecule that hybridizes to the marker or its converted sequence, The reagent comprises a probe molecule that hybridizes to the marker or a converted sequence thereof, The device comprises the medium according to claim 3, The subject is a mammal, The sample is from mammalian tissue, cells or body fluid, preferably blood, The DNA sequence is: a sequence of a corresponding marker in the genome, or a converted sequence thereof, or a sequence thereof treated with a methylation-sensitive restriction endonuclease, wherein the conversion converts unmethylated cytosine into a base having a lower ability to bind to guanine than cytosine.

9. The kit according to claim 8, wherein The kit also includes PCR reaction reagents, or The kit further comprises other reagents for detecting DNA methylation, wherein the other reagents are selected from one or more reagents used in the following methods: PCR based on bisulfite conversion, DNA sequencing, methylation-sensitive restriction endonuclease analysis, fluorescence quantitative method, methylation-sensitive high-resolution melting curve method, chip-based methylation profiling, mass spectrometry, Preferably, the other reagents for detecting DNA methylation are selected from one or more of the following: bisulfite, bisulfite, acid sulfite or pyrosulfite or their derivatives, methylation-sensitive or insensitive restriction endonucleases, enzyme digestion buffers, fluorescent dyes, fluorescence quenchers, fluorescent reporters, exonucleases, alkaline phosphatase, internal standards and controls.

10. A device for screening colorectal cancer risk, diagnosing colorectal cancer, or evaluating the prognosis of colorectal cancer, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the following steps are implemented: (1) Obtaining the methylation level of a marker in a sample of a subject, wherein the marker is a DNA sequence and 5 kb upstream and 5 kb downstream of the DNA sequence, or a fragment thereof, or one or more CpG dinucleotides therein, wherein the DNA sequence includes one or more or all of the following gene sequences: (p) TTLL10, ST6GALNAC5, KCNA3, CACNA1E, TRAPPC12, UBE2F, ZIC4, ZNF595, EVC2, HMX1, PITX2, POU4F2, IRX4, IR X1, CRHBP, KCNMB1, KCNQ5, TBX20, ACTR3C, ACTR3B, VIPR2, SOX17, MOS, PREX2, GDF6, OSR2, BARX1, SORCS3, VAX1, DPYS L4, UTF1, B3GAT1, HOXC13, CUX2, GLT1D1, ITGBL1, SKOR1, TM6SF1, LRRK1, FOXL1, MYO15B, DNM2, ZNF536, YTHDF1, SIM2, (2) comparing the methylation level of the marker in step (1) with the corresponding reference level, (3) Screening for colorectal cancer risk, diagnosing colorectal cancer, or evaluating the prognosis of colorectal cancer based on the comparison results; Preferably, The DNA sequence includes one or more or all of the genes selected from CACNA1E, PITX2, CRHBP, TBX20, SORCS3, B3GAT1, GLT1D1 and LRRK1, and optionally includes one or more or all of the other gene sequences in (p), or The DNA sequence comprises one or more or all of the genes selected from TTLL10, ACTR3B, BARX1, CUX2, DNM2 and SIM2, and optionally further comprises one or more or all of the other gene sequences in (p), or The DNA sequence includes one or more or all of the genes selected from UBE2F, HMX1, IRX4, IRX1, VIPR2, OSR2 and MYO15B, and optionally includes one or more or all of the other gene sequences in (p). More preferably, the method has one or more features selected from the group consisting of: The marker comprises at least 3 CpG dinucleotides, The fragment length is 1-1000 bp, preferably 1-700 bp, The fragment is the promoter region of the gene sequence or a portion thereof, The reagent comprises a primer molecule that hybridizes to the marker or its converted sequence, The reagent comprises a probe molecule that hybridizes to the marker or a converted sequence thereof, The device comprises the medium according to claim 3, The subject is a mammal, The sample is from mammalian tissue, cells or body fluid, preferably blood, The DNA sequence is: a sequence of a corresponding marker in a genome, or a sequence thereof after transformation, or a sequence thereof treated with a methylation-sensitive restriction endonuclease, wherein the transformation converts unmethylated cytosine into a cytosine with a binding ability lower than that of guanine. The base cytosine, Preferably, the method further comprises, before step (1), a step of obtaining a biological sample containing DNA from a subject; preferably, step (1) comprises performing the detection using the primer molecules, probe molecules and / or media, The comparison in step (2) comprises: directly comparing the methylation level of the marker in step (1) with the reference level, or calculating a score and comparing the score of the methylation level of the marker with the corresponding reference score; preferably, the score is calculated by a logistic regression model, Step (3) comprises: when the methylation level of the marker is greater than the reference level, or the methylation level score is greater than the reference score, the subject is at risk of developing colorectal cancer, has colorectal cancer, or has a poor prognosis for colorectal cancer, The detection includes: PCR based on bisulfite conversion, DNA sequencing, methylation-sensitive restriction endonuclease analysis, fluorescence quantitative method, methylation-sensitive high-resolution melting curve method, chip-based methylation mapping analysis, and mass spectrometry.

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