Liver cancer methylation markers and their applications

Abstract

The present invention provides a liver cancer cell-specific methylation marker and its application. In the biomedical field, by detecting methylation signals at the fragment level of these methylation markers, the liver cancer risk screening of the examined population can be achieved, and the detection rate of liver cancer patients can be significantly improved while ensuring a low false positive rate. In addition, through targeted methylation sequencing, it is expected to further improve the early screening performance of liver cancer, and provide accurate, low-cost, convenient and easy-to-use early screening for liver cancer, thereby improving the survival benefits of liver cancer patients and reducing the medical burden.

Description

Technical Field

[0001] The present invention relates to the biomedical field and mainly relates to a liver cancer methylation marker and its application. Background Art

[0002] Liver cancer is one of the most common malignant tumors in my country, with extremely high morbidity and mortality rates, posing a great threat to the survival of patients. At present, my country mainly uses alpha-fetoprotein (AFP) and ultrasound examinations to screen for liver cancer, but these methods are not specific enough and have low sensitivity, making it difficult to meet the needs of detecting early liver cancer. According to relevant reports, the sensitivity of AFP for early liver cancer screening is only 30-40%, and the sensitivity of combined ultrasound examination is only 63%. In addition, most liver cancers are already in the late stage when they are discovered, with poor prognosis, short survival period, and high mortality rate. Therefore, it is of great significance to develop specific markers that can diagnose early liver cancer.

[0003] When tumor cells rupture or die, they release circulating tumor DNA (ctDNA), which carries the genetic information of tumor cells. Liquid biopsy provides a new idea for early cancer screening because of its non-invasive sampling, low invasiveness and high accuracy. Detecting ctDNA through liquid biopsy technology can detect cancer earlier and predict the possibility of cancer.

[0004] A variety of high-throughput sequencing technologies can be used to detect signals of ctDNA in plasma. First, scientists have detected gene mutations in individual ctDNA as potential biomarkers, but ctDNA mutations in plasma are rare and difficult to detect accurately, and there is a risk of false positives, which cannot be effectively used in early cancer screening. DNA methylation is an epigenetic mechanism that regulates gene expression. It is reported that the methylation level of plasma ctDNA is consistent with the methylation level in the primary tumor. In addition, abnormal methylation of a wide range of related genes has been observed in many types of cancer, and methylation changes are often found in the early stages of cancer. Moreover, tumors of tissue origin have specific methylation patterns, and ctDNA can be used for tissue tracing. Therefore, the plasma ctDNA methylation signal is a marker with great potential for early tumor screening.

[0005] DNA methylation sequencing is a high-resolution, high-throughput technology used in cancer screening, diagnosis, and monitoring. However, the methylation sites covered by the methylation markers currently used in DNA methylation sequencing for liver cancer are not comprehensive enough, and the sensitivity and specificity need to be improved. Therefore, there is an urgent need for liver cancer methylation markers that cover comprehensive sites and have high sensitivity and specificity. Summary of the invention

[0006] In order to solve at least one of the above problems, the present disclosure provides a liver cancer cell-specific methylation marker and its application. Using the methylation marker provided by the present disclosure, the liver cancer risk screening of the examined population can be achieved, and the detection rate of liver cancer patients can be significantly improved while ensuring a low false positive rate.

[0007] According to one aspect of the present disclosure, a liver cancer methylation marker comprises one or more of the following: chr1:40105051-40105187; chr1:41284184-41284510; chr1:47695423-47696061; chr1:50889588-50889711; chr1:87617604-87617917; chr1:110626210-110626376; ​​chr1:110626438-110626701; chr1:118149765-118150390; chr1:1195269 93-119527583;chr1:119543077-119543454;chr1:147775138-14777588 9; chr1:147790503-147790807; chr1:153652281-153652419; chr1:1568 14999-156815356;chr1:171810392-171811744;chr1:197881741-19788 2163;chr1:197888092-197888350;chr2:1746781-1748873;chr2:19551 736-19551790;chr2:19556760-19556860;chr2:45159552-45159773;ch r2:45232106-45232392; chr2:63278887-63278944; chr2:63279378-632 79598; chr2:63279917-63280030; chr2:63280968-63281638; chr2:6680 9366-66809448;chr2:73147183-73147433;chr2:87016406-87016861;c hr2:87018329-87018715; chr2:87884057-87884168; chr2:106681787-1 06681946;chr2:111876971-111877258;chr2:112123761-112124041;ch r2:114034275-114034443; chr2:114034646-114034958; chr2:11906762 7-119068276; chr2:175207220-175207450; chr2:200333307-200333426;<h2 style=";text-align:left;direction:ltr">chr2:203036187-203036287;chr3:147109108-147109338;chr3:181443 776-181444047;chr4:3371520-3371567;chr4:54969963-54970418;chr4 :54975677-54976202;chr4:76555347-76555870;chr4:155662743-1556 62863;chr5:2755158-2755241;chr5:42951575-42951967;chr5:4299198 1-42992075;chr5:42994823-42995380;chr5:92908819-92908989;chr5:92932393-92932576;chr5:92932727-92932805;chr5:92935126-929351 97;chr5:139047620-139047740;chr5:140306333-140306817;chr6:3230 145-3230453;chr6:6007259-6008064;chr6:10381523-10381727;chr6:2 6251817-26252266;chr6:27647843-27647971;chr6:28175078-2817543 8;chr6:29943349-29943528;chr6:106957902-106958141;chr7:2350754 0-23507707;chr7:76139449-76139489;chr7:96636596-96636690;chr7:96651453-96651668;chr7:96652222-96652482;chr7:129417903-12941 8079;chr7:149112129-149112328;chr7:150786209-150786462;chr8:11541006-11541189;chr8:11554732-11555107;chr8:29387217-29387495 ;chr8:49292486-49292620;chr8:102503944-102504032;chr8:10250642 1-102506795;chr8:144509486-144509901;chr8:144510208-144510445;chr9:99449036-99449367;chr9:117149582-117150405;chr9:126777284-126777363;chr9:129884948-129885519;chr9:136654342-136654632;chr10:88730767-88731375;chr10:102997326-102997643;chr10:118891628-118891717;chr10:118900199-118900294;chr11:314044-314114;chr11:9025986-9026252;chr11:57250204-57250848;chr11:69517005-69517211;chr11:69517700-69517971;chr12:49366489-49366539;chr12:58021140-58021347;chr12:103696091-103696309;chr12:109162703-109162838;chr13:28545186-28545377;chr13:28674034-28674073;chr13:76211191-76211486;chr13:100643270-100643520;chr14:37050835-37051197;chr14:37125653-37126113;chr14:61109281-61109399;chr14:101923185-101923503;chr15:37171903-37172010;chr15:37172356-37172479;chr15:41805530-41806125;chr15:65186607-65186912;chr15:65360366-65360499;chr15:66546674-66546860;chr15:75251468-75251807;chr15:76635709-76635762;chr15:83951913-83952088;chr16:28891407-28891906;chr16:29887967-29888305;chr16:54324210-54324471;chr17:5000370-5000922;chr17:27044509-27045031;chr17:43037427-43037587;chr17:43047405-43047453; chr17:46796854-46797292; chr17:70112858-70113 008;chr17:75368620-75368751;chr17:75370803-75371003;chr18:3499671-349 9814; chr18:28621773-28622247; chr19:11532147-11532223; chr19:21646251-2 1646726;chr19:36736389-36736708;chr19:38182803-38183461;chr19:3870034 9-38700577; chr19:46001663-46002248; chr19:48984379-48984451; chr19:505 53780-50554540;chr19:51830195-51830717;chr19:52206853-52206957;chr19: 57182976-57183079;chr19:58520758-58521322;chr20:50720871-50721407;chr 21:26934241-26934805; chr21:28340159-28340237; chr22:37731203-37731495. ;

[0008] In some embodiments, the physical location of the methylation marker is determined based on a human whole genome sequence alignment.

[0009] In some embodiments, the version number of the human whole genome sequence is selected from hg19, GRCh38, hg38 or GRCh37.

[0010] According to another aspect of the present disclosure, a primer pair is provided, wherein the primer pair uses the nucleotide sequence where the methylation marker described in the first aspect is located as the target sequence, and is used for specific amplification of the target sequence.

[0011] According to another aspect of the present disclosure, a probe is provided, wherein the probe is used to specifically capture the nucleotide sequence where the methylation marker described in the first aspect is located.

[0012] According to another aspect of the present disclosure, a kit is provided, wherein the kit is used for the methylation marker described in the first aspect.

[0013] In some embodiments, the kit includes the primer pair described in the second aspect and / or the probe described in the third aspect.

[0014] In some embodiments, the kit further comprises a conversion reagent capable of converting methylated or unmethylated cytosine into uracil.

[0015] In some embodiments, the conversion reagent comprises bisulfite or Ten-Eleven Translocation Enzymes (TET enzymes).

[0016] In some embodiments, the kit further comprises a control nucleic acid, wherein the control nucleic acid comprises the methylation marker described in the first aspect and has a methylation state associated with a cancer patient.

[0017] According to another aspect of the present disclosure, a method for detecting liver cancer is provided, the method comprising using the methylation marker, the primer pair, the probe or the kit to detect a sample to be tested collected from a subject.

[0018] In some embodiments, the sample to be tested includes a natural sample or a standard.

[0019] In some embodiments, the sample to be tested includes a tissue sample or a body fluid sample.

[0020] In some embodiments, the body fluid sample includes: blood, serum, plasma, urine, feces, tears, tumor cells, alveolar lavage fluid, sputum, pus and / or processed products thereof.

[0021] In some embodiments, the tissue sample includes tissue, paraffin sections, and / or processed materials thereof.

[0022] In some embodiments, the liver cancer comprises hepatocellular carcinoma, cholangiocarcinoma, hepatic angiosarcoma, and hepatoblastoma.

[0023] In some embodiments, the liver cancer includes liver cancer at stages 0, A, B, and C.

[0024] According to another aspect of the present disclosure, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for detecting liver cancer is implemented.

[0025] According to another aspect of the present disclosure, the methylation marker, the primer pair, the probe or the kit is used in preparing a product for early screening, diagnosis, dynamic monitoring or prognosis assessment of liver cancer.

[0026] In some embodiments, the liver cancer comprises hepatocellular carcinoma, cholangiocarcinoma, hepatic angiosarcoma, and hepatoblastoma.

[0027] In some embodiments, the liver cancer includes liver cancer at stages 0, A, B, and C.

[0028] The present disclosure provides methylation markers for liver cancer screening. By detecting methylation signals at the fragment level for these methylation markers, the liver cancer risk screening of the tested population can be achieved, and the detection rate of liver cancer patients can be significantly improved while ensuring a low false positive rate. In addition, through targeted methylation sequencing, it is expected to further improve the early screening performance of liver cancer, and provide accurate, low-cost, convenient and easy-to-use early screening technology for liver cancer, thereby improving the survival benefits of liver cancer patients and reducing the medical burden. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The performance of the statistical model established based on the positive fragment ratio index of 145 hypermethylated markers in the training cohort is shown. The horizontal axis false positive rate = 1-specificity, and the vertical axis true positive rate = sensitivity. Based on the high requirements for specificity in early screening, the positive threshold is set more strictly.

[0030] Figure 2 The performance of the statistical model established based on 145 hypermethylated markers in the validation cohort is shown. The horizontal axis is false positive rate = 1-specificity, and the vertical axis is true positive rate = sensitivity.

[0031] Figure 3 The sensitivity of the statistical model established based on 145 hypermethylated markers in each stage is shown (under the condition of 100% specificity). DETAILED DESCRIPTION

[0032] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with embodiments. The specific embodiments described herein are only used to explain the present invention and are not intended to constitute any limitation of the present invention. In addition, in the following description, the description of known structures and technologies is omitted to avoid unnecessary confusion of the concepts of the present disclosure. Such structures and technologies are also described in many publications.

[0033] The present invention identified 14,582 cancer tissue-specific hypermethylated regions by performing preliminary screening of differentially methylated regions in liver cancer tissue samples, paracancerous tissue samples, and WBC samples from the Cancer Genome Atlas (TCGA).

[0034] Then, the present invention optimized the boundaries and verified the difference trends of 14,582 highly methylated markers through whole genome methylation sequencing of 50 liver cancer tissues and 11 adjacent cancer tissues, and retained a total of 4,009 methylation marker candidates.

[0035] Furthermore, the present invention verified the sensitivity and specificity of 4009 candidate methylation markers in plasma by whole genome methylation sequencing of 43 liver cancer plasma, 15 liver cirrhosis plasma and 35 healthy plasma, and finally retained 145 highly methylated marker combinations with high specificity and high sensitivity. (Specifically shown in Table 1).

[0036] Table 1. 145 methylation markers

[0037]

[0038]

[0039]

[0040]

[0041] The physical positions of the 145 methylation markers were determined based on the alignment of the human whole genome sequence (version number: hg19).

[0042] definition

[0043] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly used in the field to which the present invention belongs. For the purpose of interpreting this specification, the following definitions will apply, and where appropriate, terms used in the singular will also include the plural form, and vice versa.

[0044] As used herein, the articles "a," "an," and "an" include plural referents unless the context clearly dictates otherwise.

[0045] The expression "about" as used herein is understood by one of ordinary skill in the art and varies within a certain range depending on the context in which it is used. If one of ordinary skill in the art is not aware of the use of the term based on the context in which it is used, "about" will mean up to plus or minus 10% of the specified value.

[0046] The term "methylation" herein refers to the presence of methyl in the position where there is no methyl in the generally recognized typical nucleotide base on the nucleotide base. For example, cytosine does not contain a methyl moiety on its pyrimidine ring, but 5-methylcytosine contains a methyl moiety at the 5th position of its pyrimidine ring. In this case, cytosine is not a methylated nucleotide, and 5-methylcytosine is a methylated nucleotide. In another example, thymine contains a methyl moiety at the 5th position of its pyrimidine ring. In this article, when thymine is present in DNA, it is not considered to be a methylated nucleotide because thymine is a typical nucleotide base of DNA. The typical nucleoside bases of DNA are thymine, adenine, cytosine and guanine. The typical bases of RNA are uracil, adenine, cytosine and guanine. Accordingly, "methylation site" is a position where methylation occurs or may occur in the target gene nucleic acid region. For example, a position containing CpG is a methylation site, wherein cytosine may be methylated or may not be methylated. A "site" may correspond to a single site, which is a single base position or a group of related base positions, for example, a CpG site. A methylation site may refer to a CpG site or a non-CpG site of a DNA molecule that is likely to be methylated.

[0047] The term "CpG site" as used herein is a region of a DNA molecule in which a cytosine nucleotide is followed by a guanine nucleotide in a linear sequence of bases along its 5' to 3' direction, which region is methylated by events occurring naturally in vivo or by chemical action performed in vitro. A non-CpG site is a region that does not have a CpG dinucleotide sequence but is also susceptible to methylation by events occurring naturally in vivo or by chemical action performed in vitro.

[0048] As used herein, the term "sequencing" refers to the process of determining the sequence (eg, the identity and order of monomeric units) of a biological molecule, eg, a nucleic acid, such as DNA or RNA. Exemplary sequencing methods include, but are not limited to, targeted sequencing, single molecule real-time sequencing, exon or exome sequencing, intron sequencing, electron microscopy-based sequencing, panel sequencing, transistor-mediated sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxy terminator sequencing, whole genome sequencing, hybridization sequencing, pyrosequencing, capillary electrophoresis, duplex sequencing, cycle sequencing, single base extension sequencing, solid phase sequencing, high-throughput sequencing, massively parallel signature sequencing, emulsion PCR, low denaturation temperature co-amplification PCR (COLD-PCR), multiplex PCR, reversible dye terminator sequencing, paired end sequencing, near-term sequencing, exonuclease sequencing, ligation sequencing, short read sequencing, single molecule sequencing, sequencing by synthesis, real-time sequencing, reverse terminator sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiD™ sequencing, MS-PET sequencing, DNA nanoball sequencing (DNBSEQ), composite probe anchor polymerase sequencing (cPAS), and combinations thereof. In some embodiments, sequencing can be performed by a genetic analyzer, such as a genetic analyzer commercially available from Illumina, Inc., Pacific Biosciences, Inc., Applied Biosystems / Thermo Fisher Scientific, or Shenzhen MGI Intelligent Manufacturing Technology Co., Ltd., etc. For example, BGI DNBseq sequencing platforms such as BGISEQ-500, BGISEQ-50, MGISEQ-2000, MGISEQ-200, DNBSEQ-T7, DNBSEQ-G99, DNBSEQ-T20X2, or Illumina's HiSeq2000, HiSeq2500, HiSeq4000, HiSeqX10, NovaSeq6000, etc.

[0049] The term "liver cancer" as used herein is used in the broadest sense and is intended to cover all cancers that begin in the liver. In some embodiments, liver cancer may include, but is not limited to, hepatocellular carcinoma (HCC), cholangiocarcinoma, hepatic angiosarcoma, and hepatoblastoma. It is divided into the following stages according to tumor burden, liver function, and physical condition: Stage 0 (very early): single lesion ≤ 2 cm, preserved liver function, PS 0; Stage A (early): single lesion > 2 cm or ≤ 3 lesions, each ≤ 3 cm, preserved liver function, PS 0; Stage B (mid-stage): multiple lesions, preserved liver function, PS 0; Stage C (late stage): portal invasion and / or extrahepatic spread, preserved liver function, PS1-2; Stage D (terminal stage): any tumor burden, terminal liver function, PS3-4.

[0050] The term "diagnosis" as used herein refers not only to "judgment", but also includes "determination", "differentiation", "identification", "inspection", "detection" and "prediction" of cancer and benign lesions, cancer and the degree of cancer development, the risk of cancer, the effect of treatment and the risk of recurrence. The benign lesions include but are not limited to benign liver lesions or cirrhosis.

[0051] The term "prognosis" as used herein refers to predicting the likely consequences of cancer, including the expectation of cancer recovery. As used herein, the terms prognostic information and predictive information interchangeably represent any aspect of information that can be used to predict the course of a disease or condition when treatment is or is not performed. Such information may include, but is not limited to, the average life expectancy of a patient, the likelihood that a patient will survive a given time (e.g., 6 months, 1 year, 5 years, etc.), the likelihood that a patient's disease will be cured, the likelihood that a patient's disease will respond to a specific treatment (wherein response can be defined in any different ways). Both prognostic and predictive information are included in the broad category of diagnostic information.

[0052] In some embodiments, "prognosis" refers to the prediction of the possible course and outcome of a clinical condition or disease. The prognosis of a patient is usually determined by evaluating factors or symptoms that symbolize favorable or unfavorable disease processes or outcomes. The term "prognosis" does not mean that the process or outcome of a condition can be predicted with 100% accuracy. On the contrary, it will be understood by those skilled in the art that the term "prognosis" refers to an increased probability that a particular process or outcome will occur; that is, the process or outcome is more likely to occur in a patient who exhibits a given condition than in an individual who does not exhibit the condition. The prognosis can be expressed as the amount of time that the patient is expected to survive. Alternatively, the prognosis can refer to the likelihood of disease remission or the amount of time that the disease is expected to remain in remission. The prognosis can be expressed in different ways; for example, the prognosis can be expressed as the percentage probability that the patient will survive after one year, five years, ten years, etc. Optionally, the prognosis can be expressed as the average number of months that the patient can be expected to survive due to the condition or disease. The prognosis of a patient can be considered a relative performance, and there are many factors that affect the final outcome. For example, for a patient with a particular condition, the prognosis may be appropriately expressed as the likelihood that the condition is treatable or curable, or the likelihood that the disease will go into remission, whereas the prognosis for a patient with a more serious condition may be more appropriately expressed as the likelihood of surviving a particular time.

[0053] The term "robust" used in this article refers to the property of a control system to maintain certain other performance under certain (structure, size) parameter perturbations. Models with high robustness have better implementation effects.

[0054] The term "Youden index", also known as the correct index, used in this article is a method for evaluating the authenticity of a screening test. The Youden index can be applied when it is assumed that the harmfulness of false negatives (missed diagnosis rate) and false positives (misdiagnosis rate) is of equal significance. The Youden index is the sum of sensitivity and specificity minus 1. It represents the total ability of the screening method to detect true patients and non-patients. The larger the index, the better the effect of the screening experiment and the greater its authenticity. The term "optimal Youden index" means the case where the value after the sum of sensitivity and specificity minus 1 is the largest.

[0055] The term "sensitivity" as used herein refers to the proportion of positive samples that yield a positive result using an assay, also known as the True Positive Rate. Sensitivity is typically calculated by dividing the number of true positives identified by the assay by the sum of the number of true positives and false negatives determined by the assay for known positive samples.

[0056] Similarly, the term "specificity" refers to the ratio of the number of true negatives determined by the assay divided by the sum of the number of true negatives and false positives determined by the assay performed on known negative samples.

[0057] Examples and drawings are provided below to help understand the present invention. However, it should be understood that these examples and drawings are only used to illustrate the present invention, but do not constitute any limitation. The actual protection scope of the present invention is set forth in the claims. It should be understood that any modifications and changes can be made without departing from the spirit of the present invention.

[0058] Example

[0059] sample:

[0060] The samples involved in the examples were obtained from Xiangya Hospital of Central South University, Oriental Hepatobiliary Hospital and PLA General Hospital, and were approved by the Ethics Committee of PLA General Hospital.

[0061] The liver cancer samples used in the examples were diagnosed as primary liver cancer by histopathology, and were required to be without any treatment, and the following conditions were excluded: a) patients with previous or current other malignant tumors; b) other serious extrahepatic diseases (such as primary immunodeficiency disease, etc.), especially major diseases with a survival period of less than 3 years; c) female patients who were pregnant or preparing for pregnancy; d) patients who had not received or had not received cancer treatment before surgery, including surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy; e) patients with a known history of allogeneic organ transplantation and allogeneic hematopoietic stem cell transplantation; f) patients with a history of blood transfusion within 1 month. Cirrhosis samples are required to have a clear clinical diagnosis. Healthy samples are samples with no abnormalities found in physical examinations.

[0062] The sequencing experimental steps involved in the embodiment are as follows:

[0063] 1. DNA Preparation

[0064] 1. Tissue Sample Extraction

[0065] DNA was extracted from liver cancer tissues and paired adjacent liver cancer tissues using RNeasy Mini Kit (Qiagen), and 100 ng of gDNA was ultrasonically sheared. The sheared DNA was retained for subsequent library construction and sequencing experiments.

[0066] 2. Plasma Sample Extraction

[0067] For whole blood, plasma / blood cell separation should be performed promptly (EDTA anticoagulant tube, within 4 hours; Streck tube within 72 hours). The separation steps are as follows:

[0068] ① Centrifuge at 1600g for 10 minutes at 4°C. After centrifugation, divide the upper layer of plasma into multiple 1.5mL or 2.0mL centrifuge tubes. Be careful not to aspirate the white blood cells in the middle layer during the plasma aspiration process.

[0069] ② Centrifuge at 16000g for 10 min at 4°C to remove residual cells, transfer the supernatant into a new 1.5mL or 2.0mL centrifuge tube (be careful not to aspirate the white blood cells at the bottom of the tube) to obtain the required plasma.

[0070] Plasma according to MagMAX TM Cell-Free DNA Isolation Kit extraction reagent instructions for extracting plasma cfDNA.

[0071] 3. Methylation library construction

[0072] Using Hieff The Ultima Pro DNA Library Prep Kit for Illumina is used to construct a library for the extracted DNA, which is then oxidized by TET enzyme and reduced by pyridine borane, followed by PCR amplification to construct a library that can be identified by sequencing.

[0073] 4. Sequencing

[0074] Gene+seq sequencer and other sequencers with the same principle were used for sequencing. The sequencing experiment was performed according to the instructions provided by the manufacturer. The sequencing depth of tissue samples was 30×, and the sequencing depth of plasma samples was 60×.

[0075] Example 1 Screening of liver cancer screening marker combination

[0076] First, the human reference genome was divided into 147,438 regions based on CpG sites.

[0077] Secondly, for the 147,438 regions, 375 liver cancer tissue samples from TCGA were compared with 50 adjacent cancer tissue samples and 732 white blood cell (WBC) samples (derived from GSE87571 in the Gene Expression Omnibus (GEO) dataset), and 14,582 differentially methylated regions (DMRs, here referring to regions with higher methylation levels in cancer tissue) related to the occurrence and development of liver cancer were screened out.

[0078] Then, based on the whole genome methylation sequencing data of 50 liver cancer tissues and 11 adjacent paracancerous tissues, the wgbstools tool was used to re-divide the boundaries of 14,582 DMRs according to the cancer tissues to improve the consistency of CpG methylation status in the re-divided regions. At the same time, based on the whole genome methylation sequencing results, 4,009 highly methylated regions with the same differential methylation trend were screened out, that is, regions where the methylation level of liver cancer tissues was significantly higher than that of adjacent paracancerous tissues.

[0079] Furthermore, GMseq whole genome methylation sequencing (60×) was performed in 43 cases of liver cancer plasma, 15 cases of liver cirrhosis plasma, and 35 cases of healthy plasma. By comparing liver cancer plasma with non-liver cancer plasma (liver cirrhosis plasma + healthy plasma), high methylation regions with significant methylation signal differences in both tissue samples and plasma samples were further screened as liver cancer markers. Finally, 145 high methylation markers with excellent distinguishing performance in both tissues and plasma were retained (see Table 1).

[0080] Furthermore, the threshold of each marker was determined according to the Youden coefficient, and a statistical prediction model for early screening of liver cancer was constructed using the 145 liver cancer markers from the above 43 liver cancer plasmas, 15 liver cirrhosis plasmas, and 35 healthy plasmas. Figure 1 The performance of the statistical model established based on 145 hypermethylated markers is shown. The horizontal axis represents 1-specificity, and the vertical axis represents sensitivity. As can be seen from the results, under high specificity, the statistical model has a higher sensitivity.

[0081] Example 2 Independent performance verification of liver cancer diagnostic markers in whole genome methylation plasma data

[0082] This example verifies the screening performance of the 145 hypermethylated markers screened out in Example 1 in an independent 60× whole-genome GMseq methylated plasma sample set.

[0083] Plasma samples from 40 liver cancer patients, 15 cirrhosis patients, and 35 healthy people were used. All liver cancer samples were diagnosed as primary liver cancer by histopathology, and were required to have not received any treatment, and the following conditions were excluded: a) patients with previous or current other malignant tumors; b) other serious extrahepatic diseases (such as primary immunodeficiency diseases, etc.), especially major diseases with a survival period of less than 3 years; c) pregnant or pregnant female patients; d) patients who had not received or had not received cancer treatment before surgery, including surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy; e) patients with a known history of allogeneic organ transplantation and allogeneic hematopoietic stem cell transplantation; f) patients with a history of blood transfusion within 1 month. Cirrhosis samples are required to have a clear clinical diagnosis. Healthy samples are samples with no abnormalities found in physical examinations.

[0084] Among the patients with HCC, 13 were in stage 0 / A, 3 were in stage B, 5 were in stage C, and 19 were of unknown stage.

[0085] The output of sample GMseq whole genome methylation data is the same as in Example 1.

[0086] The performance of the sample set (validation set) of this embodiment was verified by applying the screening model constructed in Example 1 and 145 hypermethylated markers. 34 positive cases were detected in 40 liver cancer patients, 0 negative cases were detected in 15 liver cirrhosis patients, and 0 negative cases were detected in 35 healthy people.

[0087] The results show that the specificity of these 145 hypermethylated markers is 100% and the sensitivity is 85%. At a sequencing depth of 60×, the 145 hypermethylated markers can be used to construct a screening model with good early screening performance. It is expected that the early screening performance of the combination of 145 hypermethylated markers will be further improved through targeted methylation sequencing.

[0088] Results Figure 2 or 3.

[0089] Figure 2 The performance of the screening model based on 145 hypermethylated markers in the validation cohort is shown. The horizontal axis represents the false positive rate = 1-specificity, and the vertical axis represents the sensitivity. As can be seen from the results, the model still maintains high specificity and high sensitivity in the validation set, and the model is highly robust.

[0090] Figure 3 The sensitivity of the statistical model established based on 145 hypermethylated markers in each stage is shown (specificity 100%). As can be seen from the results, the sensitivity of stage 0 / A in the validation set is 92.3%, stage B and stage C are 100%, and the sensitivity of unknown stage is 73.7%. The combination of 145 methylation markers has accurate screening performance for early liver cancer.

[0091] The results show that the combination of 145 methylation markers screened by the present disclosure has more accurate screening performance for early liver cancer.

[0092] The technical solution of the present invention is not limited to the above-mentioned specific embodiments. All technical variations made according to the technical solution of the present invention fall within the protection scope of the present invention.

Claims

1. A liver cancer methylation marker, characterized in that: The methylation markers include: chr1:40105051-40105187; chr1:41284184-41284510; chr1:47695423-47696061; chr1:50889588-50889711; chr1:87617604-87617917; chr1:110626210-110626376; ​​chr1:110626438-110626701; chr1:118149765-118150390; chr1:119526993-119527583; chr1:119543077-11954345 4; chr1:147775138-147775889; chr1:147790503-147790807; chr1:15365 2281-153652419;chr1:156814999-156815356;chr1:171810392-1718117 44; chr1:197881741-197882163; chr1:197888092-197888350; chr2:1746 781-1748873; chr2:19551736-19551790; chr2:19556760-19556860; chr2: 45159552-45159773; chr2:45232106-45232392; chr2:63278887-6327894 4; chr2:63279378-63279598; chr2:63279917-63280030; chr2:63280968- 63281638; chr2:66809366-66809448; chr2:73147183-73147433; chr2:87 016406-87016861;chr2:87018329-87018715;chr2:87884057-87884168;c hr2:106681787-106681946; chr2:111876971-111877258; chr2:11212376 1-112124041; chr2:114034275-114034443; chr2:114034646-114034958; chr2:119067627-119068276; chr2:175207220-175207450; chr2:2003333 07-200333426; chr2:203036187-203036287; chr3:147109108-147109338;<h2 style=";text-align:left;direction:ltr">chr3:181443776-181444047;chr4:3371520-3371567;chr4:54969963-5 4970418;chr4:54975677-54976202;chr4:76555347-76555870;chr4:155 662743-155662863;chr5:2755158-2755241;chr5:42951575-42951967;chr5:42991981-42992075;chr5:42994823-42995380;chr5:92908819-92 908989;chr5:92932393-92932576;chr5:92932727-92932805;chr5:92935126-92935197;chr5:139047620-139047740;chr5:140306333-1403068 17;chr6:3230145-3230453;chr6:6007259-6008064;chr6:10381523-10 381727;chr6:26251817-26252266;chr6:27647843-27647971;chr6:2817 5078-28175438;chr6:29943349-29943528;chr6:106957902-106958141;chr7:23507540-23507707;chr7:76139449-76139489;chr7:96636596-9 6636690;chr7:96651453-96651668;chr7:96652222-96652482;chr7:129417903-129418079;chr7:149112129-149112328;chr7:150786209-1507 86462;chr8:11541006-11541189;chr8:11554732-11555107;chr8:2938 7217-29387495;chr8:49292486-49292620;chr8:102503944-102504032; chr8:102506421-102506795;chr8:144509486-144509901;chr8:144510208-144510445;chr9:99449036-99449367;chr9:117149582-117150405;chr9:126777284-126777363;chr9:129884948-129885519;chr9:136654342-136654632;chr10:88730767-88731375;chr10:102997326-102997643;chr10:118891628-118891717;chr10:118900199-118900294;chr11:314044-314114;chr11:9025986-9026252;chr11:57250204-57250848;chr11:69517005-69517211;chr11:69517700-69517971;chr12:49366489-49366539;chr12:58021140-58021347;chr12:103696091-103696309;chr12:109162703-109162838;chr13:28545186-28545377;chr13:28674034-28674073;chr13:76211191-76211486;chr13:100643270-100643520;chr14:37050835-37051197;chr14:37125653-37126113;chr14:61109281-61109399;chr14:101923185-101923503;chr15:37171903-37172010;chr15:37172356-37172479;chr15:41805530-41806125;chr15:65186607-65186912;chr15:65360366-65360499;chr15:66546674-66546860;chr15:75251468-75251807;chr15:76635709-76635762;chr15:83951913-83952088;chr16:28891407-28891906;chr16:29887967-29888305;chr16:54324210-54324471;chr17:5000370-5000922;chr17:27044509-27045031;chr17:43037427-43037587;chr17:43047405-43047453;chr17:46796854-46797292;chr17:70112858-70113008; chr17:75368620-75368751; chr17:75370803-753 71003;chr18:3499671-3499814;chr18:28621773-28622247;chr19:11532147- 11532223;chr19:21646251-21646726;chr19:36736389-36736708;chr19:3818 2803-38183461;chr19:38700349-38700577;chr19:46001663-46002248;chr19 :48984379-48984451; chr19:50553780-50554540; chr19:51830195-51830717; chr19:52206853-52206957; chr19:57182976-57183079; chr19:58520758-5852 1322; chr20:50720871-50721407; chr21:26934241-26934805; chr21:28340159-28340237; chr22:37731203-37731495; The physical location of the methylation marker is determined based on the human whole genome sequence hg19 alignment. ; 2. The methylation marker according to claim 1, characterized in that The methylation markers include CpG sites modified by 5-methylation or 5-hydroxymethylation.

3. Use of a reagent for detecting the methylation marker according to claim 1 or 2 in the preparation of a product for early screening, diagnosis, dynamic monitoring or prognosis assessment of liver cancer in a sample to be tested.

4. The use according to claim 3, characterized in that: The sample to be tested includes a natural sample.

5. The use according to claim 3, characterized in that: The sample to be tested includes a tissue sample or a body fluid sample.

6. The use according to claim 5, characterized in that: The body fluid sample includes: blood, serum, plasma, urine, feces, tears, tumor cells, alveolar lavage fluid, sputum, pus and / or processed products thereof.

7. The use according to claim 5, characterized in that: The tissue sample includes tissue, paraffin section and / or processed materials thereof.

8. The use according to claim 3, characterized in that: The liver cancers include hepatocellular carcinoma, cholangiocarcinoma, hepatic angiosarcoma and hepatoblastoma.

9. The use according to claim 3, characterized in that: The liver cancer includes liver cancers classified into stage 0, stage A, stage B and stage C.

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