Methylation marker for detecting gastric cancer and application thereof

Abstract

This invention provides a methylation biomarker for detecting gastric cancer and its application, relating to the field of molecular biomedical technology. This invention provides a combination of methylation biomarkers for early, non-invasive identification of gastric cancer and their application. Based on the measured expression levels of methylation biomarkers in blood samples from patients with and without gastric cancer, biomarkers for identifying gastric cancer are provided. The biomarker combination of this invention can not only assist clinicians in predicting and diagnosing the risk of patients developing gastric cancer, but also serve as a tool for screening high-risk populations for gastric cancer, helping patients to detect and treat the disease early. Moreover, compared to current gastric cancer early screening products and existing technologies, the detection process of this invention is non-invasive, has higher sensitivity and specificity, the detection method is mature and rapid, the cost is low, the safety is high, and it is convenient for large-scale clinical application.

Description

Technical Field

[0001] This application relates to the field of molecular biomedical technology, and in particular to a methylation marker for detecting gastric cancer and its application. Background Technology

[0002] Gastrointestinal endoscopy is currently the gold standard for gastric cancer screening, but its invasiveness causes stress for patients, and the results depend on the doctor's experience. Furthermore, common serum tumor markers (CEA, CA19-9, CA72-4, CA125, etc.) have a positive rate of less than 10% in early-stage gastric cancer, limiting their screening value. Therefore, developing a non-invasive, convenient, and effective method for early gastric cancer diagnosis is crucial for improving the overall survival of gastric cancer patients.

[0003] Liquid biopsy technology can detect specific molecular information of solid tumors from body fluids, providing direction for cancer diagnosis and treatment. In studies on methylation biomarkers for gastric cancer screening, most candidate tumor markers are expressed in late-stage cancer and are not suitable for early detection.

[0004] DNA methylation is an epigenetic mechanism that leads to tumorigenesis by activating oncogenes or silencing tumor suppressor genes. DNA methylation levels are influenced by environmental factors; studies have shown that aging and smoking are associated with the accumulation of abnormal CpG island methylation. Furthermore, chronic inflammation is known to accelerate the accumulation of abnormal methylation in non-cancerous tissues. For example, ulcerative colitis, chronic hepatitis, and reflux esophagitis cause inflammation, leading to abnormal CpG island methylation. This abnormal CpG island methylation in non-cancerous tissues is considered to be associated with carcinogenesis. Chronic inflammation caused by Helicobacter pylori may be associated with the development of gastric cancer. Helicobacter pylori infection leads to abnormal promoter methylation, inhibiting the expression of tumor suppressor genes such as RUNX3, LOX, and CDH1. Some studies suggest that chronic inflammation caused by Helicobacter pylori infection may induce abnormal DNA methylation, rather than being a factor of Helicobacter pylori itself. Another study using a Mongolian gerbil model confirmed that both Helicobacter pylori infection itself and abnormal methylation in the gastric mucosa contribute to gastric cancer. Summary of the Invention

[0005] The purpose of this invention is to screen methylation biomarkers for detecting early gastric cancer, specifically high-correlation intervals (differential methylation intervals) where there are differences in methylation levels between cancer patient samples and non-cancer control samples. These include partial intervals of one or more of the six high-correlation intervals screened in this application. Furthermore, using one or more of these screened methylation biomarkers to construct and validate machine learning models can achieve the purpose of auxiliary diagnosis of gastric cancer, offering advantages such as high throughput, high detection specificity, and high sensitivity, thus better distinguishing between cancer patients and healthy individuals.

[0006] On the one hand, this application provides a methylation biomarker for detecting gastric cancer, said methylation biomarker comprising one or more of the following (a1)-(a6):

[0007] One or more sequences from (a1), ZNF671, TJP2, DLX4, SFMBT2, PRDM14, and VWC2;

[0008] (a2), one or more subregion sequences from ZNF671, TJP2, DLX4, SFMBT2, PRDM14, and VWC2;

[0009] (a3), the region of the sequence shown in (a1) or (a2) after treatment with bisulfite, bisulfite and / or deaminase;

[0010] (a4), variants that have at least 90% sequence identity with (a1) or (a2) or (a3) ​​and have the same methylation sites as (a1) or (a2) or (a3);

[0011] DNA methylation sites in the regions of sequences shown in (a5), (a1), (a2), (a3), or (a4);

[0012] DNA methylation abundance covered in the regions of sequences shown in (a6), (a1), (a2), (a3), or (a4).

[0013] Preferably, the methylation marker is a methylation marker used to detect early gastric cancer.

[0014] Preferably, (a1) includes ZNF671 and TJP2;

[0015] More preferably, (a1) includes ZNF671, TJP2 and DLX4;

[0016] More preferably, (a1) includes ZNF671, TJP2 and SFMBT2;

[0017] More preferably, (a1) includes ZNF671, TJP2 and PRDM14;

[0018] More preferably, (a1) includes ZNF671, TJP2 and VWC2;

[0019] More preferably, (a1) includes ZNF671, TJP2, DLX4, SFMBT2, PRDM14 and VWC2.

[0020] The role of bisulfite, sulfite, and / or deaminase treatment is to convert unmethylated cytosine on DNA into bases that do not bind to guanine.

[0021] In this invention, the methylation biomarker uses the methylation status / level of the CpG island-containing regions / fragments / methylation sites involved in this invention as the basis for distinguishing between patients with gastric cancer and those without. The methylation level of the methylation biomarker can be obtained by various detection methods known in the art, including but not limited to obtaining the methylation level through next-generation sequencing.

[0022] In this invention, the sequence number in the genome corresponds to its position on the HG19 genome of UCSC (http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / hg19.fa.gz).

[0023] The methylation markers described in this invention can be used alone or in combination as gastric cancer-related methylation molecular markers for the detection or auxiliary detection or identification of gastric cancer or gastric cancer-related diseases.

[0024] This application is the first to use ZNF671, TJP2, DLX4, SFMBT2, PRDM14, and VWC2 as a combination of methylation biomarkers for the detection of gastric cancer risk. Experimental results have verified that ZNF671, TJP2, DLX4, SFMBT2, PRDM14, VWC2, and some of their sub-regions exhibit high sensitivity and specificity, enabling rapid differentiation between gastric cancer and non-gastric cancer samples, thus providing a novel biomarker combination for gastric cancer detection.

[0025] Further, the Hg19 coordinates of the methylation marker ZNF671 include chr19:58238280-58239295; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker ZNF671 include chr19:58238811-58238888, chr19:58238286-58238371, chr19:58239189-58239291; and / or,

[0026] The Hg19 coordinates of the methylation marker TJP2 include chr19:71788710-71789520; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker TJP2 include Chr9:71789442-71789513, Chr9:71788716-71788835, Chr9:71788985-71789075; and / or,

[0027] The Hg19 coordinates of the methylation marker DLX4 include chr17:48042090-48043030; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker DLX4 include chr17:48042492-48042581, chr17:48042093-48042182, and chr17:48042903-48043020; and / or,

[0028] The Hg19 coordinates of the methylation marker SMFBT2 include Chr10:7451800-7453780; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker SMFBT2 include Chr10:7452340-7452405, Chr10:7451805-7451890, and Chr10:7453669-7453770; and / or,

[0029] The Hg19 coordinates of the methylation marker PRDM14 include chr8:70981860-70982995; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker PRDM14 include chr8:70982125-70982184, chr8:70981864-70981943, chr8:70982863-70982990; and / or,

[0030] The Hg19 coordinates of the methylation marker VWC2 include Chr7:49813040-49814275; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker VWC2 include Chr7:49813254-49813323, Chr7:49813044-49813165, and Chr7:49814169-49814271.

[0031] More preferably, the Hg19 coordinates of the ZNF671 subregion are chr19:58238811-58238888, and the nucleotide sequence includes the sequence shown in SEQ ID No. 1 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 1; more preferably, the Hg19 coordinates of the ZNF671 subregion are chr19:58238286-58238371, and the nucleotide sequence includes the sequence shown in SEQ ID No. 2 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 2; more preferably, the Hg19 coordinates of the ZNF671 subregion are chr19:58239189-58239291, and the nucleotide sequence includes the sequence shown in SEQ ID No. 3 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 3.

[0032] More preferably, the Hg19 coordinates of the TJP2 subregion are Chr 9:71789442-71789513, and the nucleotide sequence includes the sequence shown in SEQ ID No. 4 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 4; more preferably, the Hg19 coordinates of the TJP2 subregion are Chr 9:71788716-71788835, and the nucleotide sequence includes the sequence shown in SEQ ID No. 5 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 5; more preferably, the Hg19 coordinates of the TJP2 subregion are Chr 9:71788985-71789075, and the nucleotide sequence includes the sequence shown in SEQ ID No. 6 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 6.

[0033] More preferably, the Hg19 coordinates of the DLX4 subregion are chr17:48042492-48042581, and the nucleotide sequence includes the sequence shown in SEQ ID No. 7 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 7; more preferably, the Hg19 coordinates of the DLX4 subregion are chr17:48042093-48042182, and the nucleotide sequence includes the sequence shown in SEQ ID No. 8 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 8; more preferably, the Hg19 coordinates of the DLX4 subregion are chr17:48042903-48043020, and the nucleotide sequence includes the sequence shown in SEQ ID No. 9 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 9.

[0034] More preferably, the Hg19 coordinates of the SFMBT2 subregion are Chr 10:7452340-7452405, and the nucleotide sequence includes the sequence shown in SEQ ID No. 10 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 10; more preferably, the Hg19 coordinates of the SFMBT2 subregion are Chr 10:7451805-7451890, and the nucleotide sequence includes the sequence shown in SEQ ID No. 11 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 11; more preferably, the Hg19 coordinates of the SFMBT2 subregion are Chr 10:7453669-7453770, and the nucleotide sequence includes the sequence shown in SEQ ID No. 12 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 12.

[0035] More preferably, the Hg19 coordinates of the PRDM14 subregion are chr 8:70982125-70982184, and the nucleotide sequence includes the sequence shown in SEQ ID No. 13 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 13; more preferably, the Hg19 coordinates of the PRDM14 subregion are chr 8:70981864-70981943, and the nucleotide sequence includes the sequence shown in SEQ ID No. 14 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 14; more preferably, the Hg19 coordinates of the PRDM14 subregion are chr 8:70982863-70982990, and the nucleotide sequence includes the sequence shown in SEQ ID No. 15 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 15.

[0036] More preferably, the Hg19 coordinates of the VWC2 subregion are Chr 7:49813254-49813323, and the nucleotide sequence includes the sequence shown in SEQ ID No. 16 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 16; more preferably, the Hg19 coordinates of the VWC2 subregion are Chr 7:49813044-49813165, and the nucleotide sequence includes the sequence shown in SEQ ID No. 17 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 17; more preferably, the Hg19 coordinates of the VWC2 subregion are Chr 7:49814169-49814271, and the nucleotide sequence includes the sequence shown in SEQ ID No. 18 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 18.

[0037] Preferably, the methylation marker is a methylation marker that has undergone conversion treatment with bisulfite, sulfite, and / or deaminase.

[0038] More preferably, the nucleotide sequence of the transformed ZNF671 subregion includes the sequence shown in SEQ ID No. 19 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 19, the sequence shown in SEQ ID No. 20 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 20, and the sequence shown in SEQ ID No. 21 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 21.

[0039] More preferably, the nucleotide sequence of the transformed TJP2 subregion includes the sequence shown in SEQ ID No. 22 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 22, the sequence shown in SEQ ID No. 23 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 23, and the sequence shown in SEQ ID No. 24 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 24.

[0040] More preferably, the nucleotide sequence of the transformed DLX4 subregion includes the sequence shown in SEQ ID No. 25 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 25, the sequence shown in SEQ ID No. 26 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 26, and the sequence shown in SEQ ID No. 27 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 27.

[0041] More preferably, the nucleotide sequence of the transformed SMFBT2 subregion includes the sequence shown in SEQ ID No. 28 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 28, the sequence shown in SEQ ID No. 29 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 29, and the sequence shown in SEQ ID No. 30 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 30.

[0042] More preferably, the nucleotide sequence of the transformed PRDM14 subregion includes the sequence shown in SEQ ID No. 31 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 31, the sequence shown in SEQ ID No. 32 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 32, and the sequence shown in SEQ ID No. 33 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 33.

[0043] More preferably, the nucleotide sequence of the transformed VWC2 subregion includes the sequence shown in SEQ ID No. 34 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 34, the sequence shown in SEQ ID No. 35 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 35, and the sequence shown in SEQ ID No. 36 or a nucleotide sequence having at least 90% sequence identity with SEQ ID No. 36.

[0044] This application is the first to restrict the detection region for ZNF671, TJP2, DLX4, SMBT2, PRDM14, and VWC2, obtaining functional subregions in methylation markers with good specificity and high sensitivity, which greatly shortens the detection time and saves detection costs.

[0045] On the other hand, this application also provides primers and / or probes for detecting the methylation markers, wherein the primers target the nucleotide sequence containing the methylation markers for specific amplification of the target sequence; and the probes specifically capture the nucleotide sequence containing the methylation markers.

[0046] The ZNF671 methylation detection primer pair includes a forward primer with the nucleotide sequence shown in SEQ ID NO. 37, a reverse primer with the nucleotide sequence shown in SEQ ID NO. 38, and a probe with the nucleotide sequence shown in SEQ ID NO. 39; and / or,

[0047] The ZNF671 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO.40, a reverse primer with a nucleotide sequence as shown in SEQ ID NO.41, and a probe with a nucleotide sequence as shown in SEQ ID NO.42;

[0048] The ZNF671 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO.43, a reverse primer with a nucleotide sequence as shown in SEQ ID NO.44, and a probe with a nucleotide sequence as shown in SEQ ID NO.45;

[0049] The TJP2 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO.46, a reverse primer with a nucleotide sequence as shown in SEQ ID NO.47, and a probe with a nucleotide sequence as shown in SEQ ID NO.48;

[0050] The TJP2 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO.49, a reverse primer with a nucleotide sequence as shown in SEQ ID NO.50, and a probe with a nucleotide sequence as shown in SEQ ID NO.51;

[0051] The TJP2 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO. 52, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 53, and a probe with a nucleotide sequence as shown in SEQ ID NO. 54;

[0052] The DLX4 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO. 55, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 56, and a probe with a nucleotide sequence as shown in SEQ ID NO. 57;

[0053] The DLX4 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO. 58, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 59, and a probe with a nucleotide sequence as shown in SEQ ID NO. 60;

[0054] The DLX4 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO. 61, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 62, and a probe with a nucleotide sequence as shown in SEQ ID NO. 63;

[0055] The SFMBT2 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO. 64, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 65, and a probe with a nucleotide sequence as shown in SEQ ID NO. 66;

[0056] The SFMBT2 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO. 67, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 68, and a probe with a nucleotide sequence as shown in SEQ ID NO. 69;

[0057] The SFMBT2 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO.70, a reverse primer with a nucleotide sequence as shown in SEQ ID NO.71, and a probe with a nucleotide sequence as shown in SEQ ID NO.72;

[0058] The PRDM14 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO.73, a reverse primer with a nucleotide sequence as shown in SEQ ID NO.74, and a probe with a nucleotide sequence as shown in SEQ ID NO.75;

[0059] The PRDM14 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO.76, a reverse primer with a nucleotide sequence as shown in SEQ ID NO.77, and a probe with a nucleotide sequence as shown in SEQ ID NO.78;

[0060] The PRDM14 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO.79, a reverse primer with a nucleotide sequence as shown in SEQ ID NO.80, and a probe with a nucleotide sequence as shown in SEQ ID NO.81;

[0061] The VWC2 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO. 82, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 83, and a probe with a nucleotide sequence as shown in SEQ ID NO. 84;

[0062] The VWC2 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO. 85, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 86, and a probe with a nucleotide sequence as shown in SEQ ID NO. 87;

[0063] The VWC2 methylation detection primer pair includes a forward primer with a nucleotide sequence as shown in SEQ ID NO. 88, a reverse primer with a nucleotide sequence as shown in SEQ ID NO. 89, and a probe with a nucleotide sequence as shown in SEQ ID NO. 90.

[0064] Preferably, the probe is a fluorescent probe.

[0065] In a preferred embodiment, the 5' end of the fluorescent probe is labeled with a fluorescent dye (e.g., FAM, HEX / VIC, TAMRA, Texas Red, or Cy5), and the 3' end is labeled with a quencher (e.g., BHQ1, BHQ2, BHQ3, DABCYL, or TAMRA).

[0066] On the other hand, this application also provides a kit for detecting the risk of gastric cancer, the kit comprising reagents for specifically detecting the presence and / or abundance of the methylation status of the methylation markers.

[0067] Preferably, the gastric cancer risk is the risk of early-stage gastric cancer.

[0068] Furthermore, the kit includes primers and / or probes for the methylation markers described above.

[0069] Preferably, the kit further includes primers, probes, or sequences for detecting the internal reference gene ACTB.

[0070] Furthermore, the kit also includes reagents used in any one or a combination of PCR amplification, quantitative real-time PCR, digital PCR, liquid phase-array, first-generation sequencing, high-throughput sequencing, and third-generation sequencing.

[0071] Furthermore, the reagents may be selected from the following: PCR buffer, polymerase, dNTP, primers, probes, methylation-sensitive or insensitive restriction endonucleases, enzyme digestion buffers, fluorescent dyes, fluorescence quenchers, fluorescent reporter agents, exonucleases, alkaline phosphatase, internal standards, controls, etc.

[0072] Furthermore, the kit also includes bisulfite, bisulfite and / or deaminase.

[0073] Preferably, the bisulfite is selected from one or more of calcium bisulfite, sodium bisulfite, potassium bisulfite, and ammonium bisulfite; and the disulfite is selected from one or more of sodium disulfite, potassium disulfite, and ammonium disulfite.

[0074] Those skilled in the art will understand that bisulfite, bisulfite, and / or deaminase can be selected for experiments depending on the actual situation, as long as the effect of converting unmethylated cytosine on DNA into bases that do not bind to guanine is achieved.

[0075] On the other hand, this application also provides a gastric cancer risk assessment model, which includes:

[0076] The sequencing module is used to obtain the methylation level of at least one region of the methylation markers in an individual sample to be tested; the data comparison module is used to compare the methylation level with a corresponding reference level, wherein one or more methylation markers having the same or higher methylation level relative to their corresponding reference level indicates that the individual has gastric cancer, or that the individual has gastric cancer formation or risk of forming.

[0077] In a preferred embodiment, the sequencing is performed via polymerase chain reaction (e.g., real-time polymerase chain reaction, digital polymerase chain reaction), nucleic acid sequencing, quality-based separation (e.g., electrophoresis, mass spectrometry), or target capture (e.g., hybridization, microarray). Preferably, the sequencing is performed via real-time polymerase chain reaction, optionally the real-time polymerase chain reaction is a multiplex real-time polymerase chain reaction.

[0078] In a preferred embodiment, the methylation level is expressed as a Ct value, wherein a Ct value of at least one target marker that is the same as or lower than its corresponding reference Ct value indicates that the individual has gastric cancer, or that the individual has gastric cancer formation or risk of formation, or that the individual has an increased likelihood of developing or developing gastric cancer, or that the individual has a poor prognosis or risk of poor prognosis for gastric cancer.

[0079] In a preferred embodiment, the sample is a liquid sample, which is blood from mammals or other biologically derived liquid samples, such as peripheral blood, serum, plasma, ascites, urine, cerebrospinal fluid, sputum, saliva, etc. The mammals include rats, mice, and humans; preferably humans.

[0080] In one embodiment, the sample is a fine-needle aspiration biopsy or plasma. The sample includes genomic DNA or cell-free DNA (cfDNA). In one embodiment, the sample preferably refers to plasma cfDNA. cfDNA refers to circulating cell-free DNA or cell-free DNA, degraded DNA fragments released into the plasma.

[0081] Those skilled in the art can select the sample to be tested according to the actual situation. In a preferred embodiment, the amount in the biological sample is not less than 20 ng or not less than 10 ng.

[0082] On the other hand, this application also provides a method for constructing a gastric cancer risk assessment model, the method comprising the following steps:

[0083] Step 1: Collect gastric cancer samples and non-gastric cancer samples, which are divided into training set and test set;

[0084] Step 2: Extract cfDNA from the sample, construct a library, and sequence it;

[0085] Step 3: Use the samples in the training set to train and build the algorithm model, and select methylation markers based on the training set sample data;

[0086] Step 4: Validate the model's effectiveness using test set sample data and determine the final methylation marker.

[0087] Specifically, after testing clinical samples at different stages, a diagnostic model based on the methylation levels of methylation biomarker genes was constructed using statistical methods. First, biomarkers showing significant differences in methylation expression levels across different clinical stages were screened. Statistical tests included: F-test, T-test, Wald test, likelihood ratio test, and score test. Subsequently, the selected biomarkers were used to construct the diagnostic model, employing statistical methods such as: random forest model, support vector machine, Probit regression, logistic regression, cluster analysis, neighborhood analysis, genetic algorithm, Bayesian and non-Bayesian methods, etc.

[0088] On the other hand, this application also provides an information data processing terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0089] (a) Obtain the methylation level of at least one region of the methylation markers as described in claim 1 or 2 in an individual sample to be tested; (b) Compare the methylation level in step (a) with the corresponding reference level, wherein a methylation level of one or more methylation markers relative to their corresponding reference level indicates that the individual has gastric cancer, or that the individual has gastric cancer or is at risk of developing gastric cancer.

[0090] Preferably, the gastric cancer is early-stage gastric cancer.

[0091] On the other hand, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the following steps:

[0092] (a) Obtain the methylation level of at least one region of the methylation markers as described in claim 1 or 2 in an individual sample to be tested; (b) Compare the methylation level in step (a) with the corresponding reference level, wherein a methylation level of one or more methylation markers relative to their corresponding reference level indicates that the individual has gastric cancer, or that the individual has gastric cancer or is at risk of developing gastric cancer.

[0093] On the other hand, this application also provides a method for detecting gastric cancer risk, the method comprising the following steps:

[0094] In one aspect, this application provides a method for diagnosing the formation or risk of gastric cancer in an individual or for assessing the progression or prognosis of colorectal tumors, the method comprising the following steps:

[0095] Step 1: Treat the DNA obtained from the biological sample with a reagent that can distinguish between unmethylated and methylated sites in the DNA, thereby obtaining treated DNA;

[0096] Step 2: Quantitatively analyze the methylation level of a set of target markers in the treated DNA described in Step 1, wherein the target markers are selected from one or more sequences or subregion sequences of ZNF671, TJP2, DLX4, SFMBT2, PRDM14, and VWC2.

[0097] Step 3: Compare the methylation level of at least one of the target markers quantitatively analyzed in Step 2 with the corresponding reference level. If one or more target markers have the same or higher methylation level relative to their corresponding reference level, it indicates that the individual has gastric cancer, or the individual has the risk of developing or developing gastric cancer, or the individual has an increased likelihood of developing or developing gastric cancer, or the individual has a poor prognosis or risk of poor prognosis for gastric cancer.

[0098] The present invention has the following beneficial effects:

[0099] This invention provides a combination of methylation biomarkers for early, non-invasive identification of gastric cancer and their application. Based on the measured expression levels of methylation biomarkers in blood samples from patients with and without gastric cancer, biomarkers for gastric cancer are identified. The biomarker combination of this invention can not only assist clinicians in predicting and diagnosing the risk of patients developing gastric cancer, but also serve as a tool for screening high-risk populations for gastric cancer, helping patients achieve early detection and treatment. Moreover, compared to current gastric cancer early screening products and existing technologies, the detection process of this invention is non-invasive, has higher sensitivity and specificity, is mature and rapid, has lower cost, higher safety, and is suitable for large-scale clinical application. Attached Figure Description

[0100] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0101] Figure 1 AUC curves of the six target combinations in the training and test sets. Detailed Implementation

[0102] To more clearly illustrate the overall concept of this application, a detailed description is provided below with reference to the accompanying drawings and embodiments. Numerous specific details are set forth in the following description to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described to avoid confusion with the invention.

[0103] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0104] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0105] Unless otherwise specified, all reagents or instruments used in the following embodiments, unless otherwise indicated by the manufacturer, are commercially available products. The plasmids, restriction enzymes, PCR enzymes, column-based DNA extraction kits, and DNA gel recovery kits used in the following examples are commercially available products, and the specific operations were performed according to the kit instructions.

[0106] Before further describing the specific embodiments of the present invention, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments described below; it should also be understood that the terminology used in the embodiments of the present invention is for describing specific embodiments and not for limiting the scope of protection of the present invention. Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in the present invention all adopt conventional techniques in the fields of molecular biology, biochemistry, chromatin structure and analysis, analytical chemistry, cell culture, recombinant DNA technology, and related fields, and can be specifically performed according to Molecular Cloning: A Laboratory Manual (Fourth Edition).

[0107] This application involves all patients signing informed consent forms.

[0108] The following embodiments describe the extraction, quality control, and conversion of unmethylated cytosine on the sample DNA (or cfDNA) 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 with bisulfite or disulfite treatment, more preferably with calcium bisulfite, sodium bisulfite, potassium bisulfite, ammonium bisulfite, sodium disulfite, potassium disulfite, and ammonium disulfite.

[0109] Furthermore, those skilled in the art will understand that, in this context, diagnostic “sensitivity” is defined as the proportion of correctly identified positive results, that is, the percentage of individuals correctly identified as having the disease. “Specificity,” on the other hand, is defined as the proportion of correctly identified negative results, that is, the percentage of individuals correctly identified as not having the disease.

[0110] Example 1: Methylation primer probes specifically recognize methylated templates

[0111] 1. Sample collection

[0112] All blood samples were collected in Streck tubes. To extract plasma, the blood samples were first centrifuged at 1600g for 15 min at 4°C. A smooth braking mode was used to prevent damage to the buffy coat layer. The supernatant was then transferred to a new 1.5mL conical tube and centrifuged at 16000g for 10 min at 4°C. The supernatant was then transferred again to a new 1.5mL conical tube and stored at -80°C.

[0113] To extract circulating cell-free DNA (cfDNA), a nucleic acid extraction or purification kit (Jiangsu Kunyuan Biotechnology Co., Ltd.) was used. Following the manufacturer's instructions, the cfDNA was purified and concentrated through four steps: lysis, binding, washing, and elution. The resulting cfDNA could be used for downstream experiments (PCR, NGS, etc.). The concentration of the extracted cfDNA was quantified using qubit 3.0.

[0114] 2. Bisulfite Conversion

[0115] The nucleic acid DNA or cfDNA to be tested is converted to bisulfite using a methylation detection sample pretreatment kit (Jiangsu Kunyuan Biotechnology Co., Ltd.). Following the manufacturer's instructions, 5-500 ng of genomic DNA or cfDNA is converted and purified for downstream applications.

[0116] 3. Target screening

[0117] (1) Primer and probe sequence

[0118] This embodiment screens for methylation biomarkers associated with early gastric cancer by performing simplified genomic methylation sequencing (RRBS) analysis on samples from gastric cancer patients and non-gastric cancer patients. This method involves enriching fragments rich in CCGG sites on genomic DNA using restriction enzyme digestion, followed by bisulfite conversion and sequencing. The methylation ratio is calculated based on the ratio of the number of reads not converted to T at a single C site to the number of reads covered. The sequenced data is compared with the human whole genome sequence (hg19) to determine its genomic location. Genes with significantly different methylation between gastric cancer and non-gastric cancer samples are then screened. Machine learning models such as LASSO, SVM-REF, and random forest algorithms are used to further screen differentially expressed genes. Finally, the following six methylation biomarkers were selected: ZNF671 (chr19:58238280-58239295), TJP2 (chr19:71... The following genes were used to detect the methylation levels of methylation marker genes or corresponding fragments in biological samples from subjects: 788710-71789520, DLX4 (chr17:48042090-48043030), SMBT2 (Chr10:7451800-7453780), PRDM14 (chr8:70981860-70982995), and VWC2 (Chr7:49813040-49814275).Among them, the subregions of ZNF671 include ZNF671-1, ZNF671-2, and ZNF671-3. ZNF671-1 is chr19:58238811-58238888, ZNF671-2 is chr19:58238286-58238371, and ZNF671-3 is chr19:58239189-58239291. The subregions of TJP2 include TJP2-1, TJP2- 2. TJP2-3, TJP2-1 is Chr9:71789442-71789513, TJP2-2 is Chr9:71788716-71788835, TJP2-3 is Chr9:71788985-71789075. The subregions of DLX4 include DLX4-1, DLX4-2, and DLX4-3. DLX4-1 is chr17:48042492-48042581, DL... X4-2 is chr17:48042093-48042182, DLX4-3 is chr17:48042903-48043020, the subregions of SFMBT2 include SMFBT2-1, SMFBT2-2, and SMFBT2-3, SMFBT2-1 is Chr10:7452340-7452405, SMFBT2-2 is Chr10:7451805-7451890, and SMFBT2-3 is Chr10:7453669-7453770, the subregions of PRDM14 include PRDM14-1, PRDM14-2, and PRDM14-3, PRDM14-1 is chr 8:70982125-70982184, PR... DM14-2 is chr8:70981864-70981943, PRDM14-3 is chr8:70982863-70982990, and the subregions of VWC2 include VWC2-1, VWC2-2, and VWC2-3. VWC2-1 is Chr7:49813254-49813323, VWC2-2 is Chr7:49813044-49813165, and VWC2-3 is Chr7:49814169-49814271. The specific sequences are shown in Table 1.

[0119] Table 1

[0120]

[0121] PCR amplification primers were designed for the aforementioned methylation marker genes or their fragments, and the specific primer sequences are shown in Table 2. PCR amplification reactions were performed using the primer and probe sequences with the aforementioned bisulfite-treated methylation marker genes or their fragments as templates. Simultaneously, the process also included primer and probe sequences for detecting the internal reference gene ACTB, used for quality control of sample quality and bisulfite conversion efficiency.

[0122] (2) PCR detection

[0123] Mix the primer mixture and PCR reaction solution. Each reaction requires 12.5 μL of PCR reaction solution and 2.5 μL of primer mixture. Add 10 μL of sample converted with bisulfite, vortex to mix, and briefly centrifuge before PCR detection. The reaction program is: 95℃ for 5 min, 95℃ for 15 s, 60℃ for 30 s, for 50 cycles.

[0124] Table 2

[0125]

[0126]

[0127] The methylation level was determined by using the above primers and probes to obtain the presence or level of multiple CpG dinucleotides in DNA by PCR detection. Table 3 shows the detection results of the above primers and probes on methylated and unmethylated templates (treated with bisulfite) of different genes.

[0128] As shown in Table 3, the primer and probe combinations designed in Table 2 are highly specific for methylated templates, and unmethylated DNA was not detected. The Ct values ​​of methylated DNA were generally low.

[0129] Table 3. Detection results (Ct values) of primers and probes on methylated and unmethylated templates.

[0130]

[0131] Table 3 (continued) Detection results (Ct values) of primers and probes on methylated and unmethylated templates

[0132]

[0133] Example 2: Methylation primer probes specifically recognize methylated templates

[0134] In this embodiment, plasma samples from patients with different types of cancer and healthy individuals were used to further verify the effectiveness and specificity of the primers and probes. Following the method described in Example 1, plasma samples from 6 patients with gastric cancer, 6 healthy individuals, and 3 other cancer patients were extracted and converted to bisulfite to obtain DNA templates. The above primer and probe combination was used for PCR detection, and the specific results are shown in Table 4.

[0135] Table 4. Detection results of primer probes on plasma samples from different patients.

[0136]

[0137] Table 4 (continued) Detection results of primer probes on plasma samples from different patients

[0138]

[0139] As shown in Table 4, there are certain differences in the methylation detection of ZNF671, TJP2, DLX4, SFMBT2, PRDM14, and VWC2 genes between cancer patients and healthy individuals. The primer and probe combinations listed above exhibit highly specific amplification of methylated DNA from gastric cancer, while other cancers and healthy individuals showed no amplification or an amplification Ct value greater than 37. The methylation levels of different genes vary across different cancers; for example, DLX4 methylation levels are higher in lung cancer, and VWC2 methylation levels are also higher in liver cancer. Furthermore, the methylation levels of different genes vary at different stages of gastric cancer. The six gastric cancer samples included samples from different stages of gastric cancer, thus the methylation levels of the above genes differed in the six gastric cancer samples. This can be further illustrated by considering AUC. Although the amplification Ct values ​​of different primer and probe combinations for gastric cancer samples showed some differences, they were all significantly different from those of other cancers and healthy individuals. Therefore, the primer combinations for each gene listed above are suitable for gastric cancer detection.

[0140] Example 3: Performance of methylation markers in clinical samples

[0141] This study collected samples from a total of 224 patients with gastric cancer, 304 patients with varying degrees of gastric disease, 28 healthy controls, and 14 patients with other cancers. All enrolled patients signed informed consent forms. Gastric cancer and gastric polyps were diagnosed through imaging and pathology. Sample staging was based on the international TNM staging system, and sample subtypes were determined by tissue biopsy and immunohistochemistry. Benign samples included common types of benign conditions observed throughout the study population. Complete clinicopathological reports were obtained after surgery, including patient age, smoking history, ethnicity, stage, and subtype. Each sample was coded and collected; sample information is shown in Table 5 below.

[0142] Table 5 Gastric cancer stage and other characteristics of the subjects whose samples were collected.

[0143]

[0144] The collected samples were subjected to plasma collection, nucleic acid extraction, and sulfite conversion as described in Example 1. Real-time fluorescence PCR was then performed using the primer and probe combinations in Table 2 to detect the methylation levels of ZNF671, TJP2, DLX4, SFBBT2, PRDM14, and VWC2 gene fragments, with the internal control gene ACTB as the internal control.

[0145] Descriptive statistics, receiver operating characteristic (ROC) curves, and graphs of plasma methylation biomarker levels were performed using commercially available software packages (IBM SPSS Statistics 27 and MedCalc 11.4.3.0, purchased from IBM and MedCalc, respectively). Statistical differences were determined using the nonparametric Kruskal-Wallis test (ANOVA) followed by Dunn's post-multiple comparison test. For all statistical comparisons, a p-value <0.05 was considered statistically significant. The results are shown in Table 6.

[0146] Table 6. Area under the receiver operating characteristic (ROC) curve analysis of the six biomarker genes (AUC)

[0147]

[0148]

[0149] As can be seen from Table 6, the detection results of the six methylation markers and their subregions with different primer probes can all reach an AUC of 0.7 or higher, which are all good gastric cancer markers.

[0150] Example 4: Machine learning model for 6 methylation markers

[0151] In this embodiment, the samples from Example 3 above are divided into a training set and a test set in a 1:1 ratio (grouping is shown in Table 7). The training set is used to build the machine learning model described below, and the test set is used to test the performance of the model.

[0152] Table 7 Classification of Training and Test Set Samples

[0153] training set test set Stomach cancer 112 112 Non-gastric cancer 173 173 age 59.93(26-89) 59.97(22-91) gender male 170 171 female 115 114

[0154] This embodiment uses the methylation levels of six methylation markers from Table 1 above (taking the subregions ZNF671 chr19: 58238811-58238888; TJP2 chr9: 71789442-71789513; DLX4 chr17: 48042492-48042581; SFMBT2 chr10: 7452340-7452405; PRDM14 chr8: 70982125-70982184; VWC2 chr7: 49813254-49813323 as examples) to construct a logistic regression machine learning model, accurately distinguishing between gastric cancer and non-gastric cancer samples in the data. The specific steps are as follows:

[0155] 1. Use samples from the training set for training, and determine the relevant threshold of the model based on the samples from the training set (≥0.5, the detection result is positive; <0.5, the detection result is negative).

[0156] 2. Test using samples from the test set; use the predicted score and the above threshold to determine whether the sample is gastric cancer.

[0157] 3. Evaluate the model's performance and calculate the model's AUC metric.

[0158] The model obtained from the training set was validated on the test set. The AUC for distinguishing between gastric cancer and non-gastric cancer samples reached 0.897, indicating good differentiation capabilities. The sensitivity and specificity were 66.96% and 91.33%, respectively. The ROC curve is shown below. Figure 1 .

[0159] Example 5: Machine learning model for two methylation markers

[0160] To optimize the effect of the combination of methylation biomarkers, this embodiment selects two from the methylation levels of six methylation biomarkers (ZNF671, TJP2, DLX4, SMBT2, PRDM14, VWC2) and their subregions to construct a logistic regression machine learning model.

[0161] The method for constructing the machine learning model is the same as in Example 4, but only data from two markers were used for the relevant samples, and the top 5 combinations with the highest AUC of the combined target were output, as shown in Tables 8 and 9.

[0162] Table 8. AUC of target combinations on the training and test sets.

[0163] Target 1 Target 2 train-AUC test-AUC ZNF671 TJP2 0.81 0.84 TJP2 DLX4 0.79 0.80 ZNF671 VWC2 0.78 0.80 ZNF671 DLX4 0.77 0.79 TJP2 VWC2 0.76 0.79

[0164] Table 9 shows the AUC of different primer-probe combinations for ZNF671 and TJP2 on the training and test sets.

[0165] Target 1 Target 2 train-AUC test-AUC ZNF671-1 TJP2-1 0.81 0.84 ZNF671-1 TJP2-2 0.80 0.83 ZNF671-1 TJP2-3 0.80 0.83 ZNF671-2 TJP2-1 0.80 0.84 ZNF671-2 TJP2-2 0.79 0.82 ZNF671-2 TJP2-3 0.79 0.82 ZNF671-3 TJP2-1 0.79 0.83 ZNF671-3 TJP2-2 0.78 0.82 ZNF671-3 TJP2-3 0.78 0.81

[0166] The results in Table 8 above, showing the dual-target combinations, are the average values ​​of the detection results for each methylation marker subregion. It can be seen that ZNF671 and TJP2 have high discriminative power for gastric cancer and non-gastric cancer samples, and their ability to distinguish between gastric cancer and non-gastric cancer samples remains high when combined with other targets. Subsequently, as shown in Table 9, AUC analysis was performed on different primer-probe combinations for the two targets. The results showed that the AUC of the test set for different primer-probe combinations of ZNF671 and TJP2 was above 0.8. Therefore, ZNF671 and TJP2 are the optimal dual-target combination.

[0167] Example 6: Machine learning model for three methylation markers

[0168] To optimize the effect of the combination of methylation biomarkers, this embodiment selects three of the methylation levels of six methylation biomarkers (ZNF671, TJP2, DLX4, SFMBT2, PRDM14, VWC2) and their subregions to construct a logistic regression machine learning model.

[0169] The method for constructing the machine learning model is the same as in Examples 4 and 5, but the relevant samples only use the data of the above 3 markers in this example, and output the top 10 combinations with the highest AUC of the combined target, as shown in Tables 10 and 11.

[0170] Table 10. AUC of the three target combinations on the training and test sets.

[0171] Target 1 Target 2 Target 3 train-AUC test-AUC ZNF671 TJP2 VWC2 0.82 0.88 ZNF671 TJP2 DLX4 0.84 0.87 ZNF671 TJP2 PRDM14 0.80 0.86 ZNF671 DLX4 VWC2 0.84 0.86 ZNF671 DLX4 SFMBT2 0.84 0.86 ZNF671 PRDM14 VWC2 0.81 0.86 TJP2 DLX4 SFMBT2 0.83 0.86 TJP2 DLX4 VWC2 0.82 0.84 ZNF671 DLX4 PRDM14 0.83 0.85 ZNF671 TJP2 SFMBT2 0.79 0.85

[0172] Table 11. AUC of different primer-probe combinations for the three genes in the training and test sets.

[0173] Target 1 Target 2 Target 3 train-AUC test-AUC ZNF671-1 TJP2-1 DLX4-1 0.84 0.87 ZNF671-1 TJP2-1 DLX4-2 0.82 0.85 ZNF671-1 TJP2-1 DLX4-3 0.82 0.84 ZNF671-1 TJP2-1 SFMBT2-1 0.79 0.85 ZNF671-1 TJP2-1 SFMBT2-2 0.8 0.83 ZNF671-1 TJP2-1 SFMBT2-3 0.79 0.83 ZNF671-1 TJP2-1 PRDM14-1 0.8 0.86 ZNF671-1 TJP2-1 PRDM14-2 0.8 0.84 ZNF671-1 TJP2-1 PRDM14-3 0.81 0.83 ZNF671-1 TJP2-1 VWC2-1 0.82 0.88 ZNF671-1 TJP2-1 VWC2-2 0.81 0.85 ZNF671-1 TJP2-1 VWC2-3 0.8 0.84

[0174] The results above show that the model has significant differences in scores between gastric cancer and non-gastric cancer samples in both the training and testing sets. The model's AUC on the testing set reached over 0.79, indicating the good performance of this combined model. Furthermore, different primer-probe combinations of ZNF671 and TJP2 with the other four targets (DLX4, SFMBT2, PRDM14, and VWC2) all achieved AUCs above 0.83 in the testing group. This demonstrates that any combination of ZNF671 and TJP2 with the other four targets can achieve good differentiation between gastric cancer and non-gastric cancer samples.

[0175] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A methylation marker for detecting gastric cancer, characterized in that, The methylation markers include any one or more of the following (a1)-(a5): (a1), one or more subregion sequences from ZNF671, TJP2, DLX4, SFMBT2, PRDM14, and VWC2; (a2), the region of the sequence shown in (a1) after treatment with bisulfite, bisulfite and / or deaminase; (a3), variants that have at least 90% sequence identity with (a1) or (a2) and have the same methylation sites as (a1) or (a2); DNA methylation sites in the regions of sequences shown in (a4), (a1), (a2), or (a3); DNA methylation abundance covered in the regions of sequences shown in (a5), (a1), (a2), (a3), or (a4).

2. The methylation marker according to claim 1, characterized in that, The Hg19 coordinates of the methylation marker ZNF671 include chr19:58238280-58239295; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker ZNF671 include chr19:58238811-58238888, chr19:58238286-58238371, chr19:58239189-58239291; and / or, The Hg19 coordinates of the methylation marker TJP2 include chr19:71788710-71789520; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker TJP2 include Chr9:71789442-71789513, Chr9:71788716-71788835, Chr9:71788985-71789075; and / or, The Hg19 coordinates of the methylation marker DLX4 include chr17:48042090-48043030; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker DLX4 include chr17:48042492-48042581, chr17:48042093-48042182, and chr17:48042903-48043020; and / or, The Hg19 coordinates of the methylation marker SMFBT2 include Chr10:7451800-7453780; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker SMFBT2 include Chr10:7452340-7452405, Chr10:7451805-7451890, and Chr10:7453669-7453770; and / or, The Hg19 coordinates of the methylation marker PRDM14 include chr8:70981860-70982995; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker PRDM14 include chr8:70982125-70982184, chr8:70981864-70981943, chr8:70982863-70982990; and / or, The Hg19 coordinates of the methylation marker VWC2 include Chr7:49813040-49814275; preferably, the Hg19 coordinates of the subregion sequence of the methylation marker VWC2 include Chr7:49813254-49813323, Chr7:49813044-49813165, and Chr7:49814169-49814271.

3. Primers and / or probes for detecting the methylation markers of claim 1 or 2, characterized in that, The primers target the nucleotide sequence containing the methylation marker for specific amplification of the target sequence; the probes specifically capture the nucleotide sequence containing the methylation marker.

4. A kit for detecting the risk of gastric cancer, characterized in that, The kit contains reagents for specifically detecting the presence and / or abundance of the methylation status of the methylation markers of claim 1 or 2.

5. The reagent kit according to claim 4, characterized in that, The kit includes primers and / or probes for the methylation markers as described in claim 3.

6. The reagent kit according to claim 4, characterized in that, The kit also includes reagents used in any one or a combination of PCR amplification, quantitative real-time PCR, digital PCR, liquid phase-array, first-generation sequencing, high-throughput sequencing, and third-generation sequencing.

7. The reagent kit according to claim 4, characterized in that, The kit also includes bisulfite, bisulfite and / or deaminase.

8. A gastric cancer risk assessment model, characterized in that, The gastric cancer risk assessment model includes: The sequencing module is used to obtain the methylation level of at least one region of the methylation markers as described in claim 1 or 2 in an individual sample to be tested; the data comparison module is used to compare the methylation level with a corresponding reference level, wherein a methylation level of one or more methylation markers relative to their corresponding reference levels indicates that the individual has gastric cancer, or that the individual has gastric cancer formation or is at risk of forming gastric cancer.

9. An information data processing terminal, the information data processing terminal 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 computer program, it performs the following steps: (a) Obtain the methylation level of at least one region of the methylation markers as described in claim 1 or 2 in an individual sample to be tested; (b) Compare the methylation level in step (a) with the corresponding reference level, wherein a methylation level of one or more methylation markers relative to their corresponding reference level indicates that the individual has gastric cancer, or that the individual has gastric cancer or is at risk of developing gastric cancer.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, the following steps are performed: (a) Obtain the methylation level of at least one region of the methylation markers as described in claim 1 or 2 in an individual sample to be tested; (b) Compare the methylation level in step (a) with the corresponding reference level, wherein a methylation level of one or more methylation markers relative to their corresponding reference level indicates that the individual has gastric cancer, or that the individual has gastric cancer or is at risk of developing gastric cancer.

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