Methylation biomarkers for detecting gastric cancer, methylation biomarker combinations and applications thereof

Abstract

The present invention provides a methylation biomarker for detecting gastric cancer, a methylation biomarker combination and application thereof, and relates to the technical field of biological detection. The methylation biomarker for detecting gastric cancer includes any one or any combination selected from the following differentially methylated regions: chr7:37488070-37488477, chr7:87229906-87230345, chr22:17849808-17850021, chr7:27196138-27196564, chr10:20105505-20105717, chr6:31830364-31830826, chr5:35925138-35925258, chr15:48010822-48010942 and chr6:39693254-39693374. The methylation biomarker for detecting gastric cancer provided by the present invention can be used for non-invasive screening of gastric cancer and has good sensitivity, specificity and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of biological detection technology, and in particular to a methylation biomarker for detecting gastric cancer, a methylation biomarker combination and applications thereof. Background Art

[0002] The vast majority of gastric cancers are adenocarcinomas, which have no obvious symptoms in the early stages, or may present non-specific symptoms such as upper abdominal discomfort and belching, which are often similar to the symptoms of chronic gastric diseases such as gastritis and gastric ulcers and are easily overlooked. Therefore, screening for gastric cancer and timely treatment of early gastric cancer are important means to reduce gastric cancer mortality and prolong patient survival.

[0003] Currently, gastrointestinal endoscopy is still the main means of diagnosing gastric cancer. However, since gastrointestinal endoscopy is an invasive examination, the operation is relatively complicated, which will cause certain pain to the patient and greatly affect the patient's physical and mental health. It is also accompanied by the risk of infection and bleeding.

[0004] In recent years, with the development of high-throughput sequencing technology, scholars at home and abroad have conducted increasingly in-depth research on cancer. Among them, the technology of non-invasive screening using methylation signals of free DNA (cfDNA) in body fluids is very important. Since DNA methylation occurs in the early stages of the tumor and is tissue-specific, it is one of the best choices for tracking early tumor signals.

[0005] Therefore, it is necessary and urgent to research and develop new DNA methylation biomarkers to identify early gastric cancer, assist in accurate clinical diagnosis and guide treatment.

[0006] In view of this, the present invention is proposed. Summary of the invention

[0007] The first object of the present invention is to provide a methylation biomarker for detecting gastric cancer, wherein the methylation biomarker can be used for non-invasive screening of gastric cancer.

[0008] The second object of the present invention is to provide a methylation biomarker combination for detecting gastric cancer.

[0009] The third object of the present invention is to provide an application of the above-mentioned methylation biomarker or methylation biomarker combination for detecting gastric cancer.

[0010] A fourth object of the present invention is to provide a kit for detecting gastric cancer.

[0011] In order to achieve the above-mentioned purpose of the present invention, the following technical solutions are particularly adopted:

[0012] The present invention provides a methylation biomarker for detecting gastric cancer, wherein the methylation biomarker comprises any one or any combination selected from the following differentially methylated regions:

[0013] chr7:37488070-37488477, chr7:87229906-87230345, chr22:17849808-17850021, chr7:27196138-27196564, chr10:20105505-20105717, chr6:31830364-31830826, chr5:35925138-35925258, chr15:48010822-48010942 and chr6:39693254-39693374.

[0014] The present invention provides a methylation biomarker combination for detecting gastric cancer, wherein the methylation biomarker combination is obtained based on the above-mentioned methylation biomarker for detecting gastric cancer;

[0015] The methylation biomarker combination includes:

[0016] The differentially methylated region is at least one of the methylation biomarkers of chr7:37488070-37488477;

[0017] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr7:87229906-87230345;

[0018] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr22:17849808-17850021;

[0019] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr7:27196138-27196564;

[0020] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr10:20105505-20105717;

[0021] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr6:31830364-31830826;

[0022] and / or, at least one of the methylation biomarkers whose differentially methylated region is chr5:35925138-35925258;

[0023] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr15:48010822-48010942;

[0024] And / or, the differentially methylated region is at least one of the methylation biomarkers of chr6:39693254-39693374.

[0025] Furthermore, the methylation biomarker combination includes:

[0026] At least one of the methylation biomarkers whose differential methylation region is chr7:37488070-37488477, at least one of the methylation biomarkers whose differential methylation region is chr7:87229906-87230345, and at least one of the methylation biomarkers whose differential methylation region is chr22:17849808-17850021;

[0027] Or, the methylation biomarker combination includes:

[0028] At least one of the methylation biomarkers whose differential methylation region is chr7:37488070-37488477, at least one of the methylation biomarkers whose differential methylation region is chr7:87229906-87230345, at least one of the methylation biomarkers whose differential methylation region is chr22:17849808-17850021, and at least one of the methylation biomarkers whose differential methylation region is chr7:27196138-27196564;

[0029] Or, the methylation biomarker combination includes:

[0030] At least one of the methylation biomarkers whose differential methylation region is chr7:37488070-37488477, at least one of the methylation biomarkers whose differential methylation region is chr7:87229906-87230345, at least one of the methylation biomarkers whose differential methylation region is chr22:17849808-17850021, at least one of the methylation biomarkers whose differential methylation region is chr7:27196138-27196564, and at least one of the methylation biomarkers whose differential methylation region is chr10:20105505-20105717;

[0031] Or, the methylation biomarker combination includes:

[0032] At least one of the methylation biomarkers whose differential methylation region is chr7:37488070-37488477, at least one of the methylation biomarkers whose differential methylation region is chr7:87229906-87230345, at least one of the methylation biomarkers whose differential methylation region is chr22:17849808-17850021, at least one of the methylation biomarkers whose differential methylation region is chr7:27196138-27196564, at least one of the methylation biomarkers whose differential methylation region is chr10:20105505-20105717, and at least one of the methylation biomarkers whose differential methylation region is chr6:31830364-31830826;

[0033] Or, the methylation biomarker combination includes:

[0034] At least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-2 at least one of the methylation biomarkers of chr7196564, at least one of the methylation biomarkers of the differentially methylated region chr10:20105505-20105717, at least one of the methylation biomarkers of the differentially methylated region chr6:31830364-31830826, and at least one of the methylation biomarkers of the differentially methylated region chr5:35925138-35925258;

[0035] Or, the methylation biomarker combination includes:

[0036] At least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-27196564, at least one of the methylation biomarkers whose differential methylation region is chr10:20105505-20105717, at least one of the methylation biomarkers whose differential methylation region is chr6:31830364-31830826, at least one of the methylation biomarkers whose differential methylation region is chr5:35925138-35925258, and at least one of the methylation biomarkers whose differential methylation region is chr15:48010822-48010942;

[0037] Or, the methylation biomarker combination includes:

[0038] At least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-27196564, at least one of the methylation biomarkers with a differential methylation region of chr10:20105505- At least one of the methylation biomarkers of 20105717, at least one of the methylation biomarkers with a differential methylation region of chr6:31830364-31830826, at least one of the methylation biomarkers with a differential methylation region of chr5:35925138-35925258, at least one of the methylation biomarkers with a differential methylation region of chr15:48010822-48010942, and at least one of the methylation biomarkers with a differential methylation region of chr6:39693254-39693374.

[0039] Furthermore, the gastric cancer is gastric cancer present in a subject, and the subject is a mammal;

[0040] Preferably, the gastric cancer includes any one of stage I, stage II, stage III or stage IV gastric cancer.

[0041] Furthermore, the mammal is a human.

[0042] Further, the methylation degree of the methylation biomarker combination is detected by one or more of the following methods:

[0043] Fluorescence quantitative PCR, methylation-specific PCR, digital PCR, DNA methylation chip, targeted DNA methylation sequencing, methylation-sensitive restriction endonuclease (MS-RE)-PCR / Southern method, direct sequencing, methylation-sensitive single nucleotide primer extension (Ms-SnuPE), bisulfite-combined restriction endonuclease method (COBRA), methylation-sensitive single-strand conformation analysis (MS-SSCA), methylation-sensitive denaturing gradient gel electrophoresis (MSDGGE), methylation-specific denaturing high-performance liquid chromatography (MS-DHPLC), methylation-specific microarray (MSO), methylation-sensitive melting curve analysis (MS-MCA) methylation-sensitive dot analysis (MS-DBA), methylation-specific multiple ligation-dependent probe amplification, bisulfite sequencing and pyrosequencing.

[0044] The present invention provides a kit for detecting gastric cancer, which comprises a reagent for detecting the methylation level of the above-mentioned methylation biomarker for detecting gastric cancer or the above-mentioned methylation biomarker combination.

[0045] Furthermore, the kit is tested using any of the following detection methods:

[0046] Apply fluorescent quantitative PCR, methylation-specific PCR, digital PCR, DNA methylation chip, targeted DNA methylation sequencing, whole-genome methylation sequencing or DNA methylation mass spectrometry;

[0047] Furthermore, the kit is used for targeted DNA methylation sequencing.

[0048] The present invention provides a use of the above-mentioned methylation biomarker for detecting gastric cancer or the above-mentioned methylation biomarker combination in the following (i) to (ii):

[0049] (i) Use in the preparation of a reagent or kit for diagnosing gastric cancer;

[0050] (ii) Use of a reagent for determining the methylation level of a methylation biomarker in the preparation of a reagent or kit for diagnosing gastric cancer.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention studies the methylation differences of DNA methylation modifications at specific CpG sites of plasma cfDNA in gastric cancer patients, and finds that the DNA methylation markers and their combinations can be used as diagnostic markers for non-invasive screening of gastric cancer. The present invention discloses 9 methylation biomarkers for detecting gastric cancer, which can significantly improve the detection rate of gastric cancer. The application of them in non-invasive screening of gastric cancer has good sensitivity, specificity and accuracy, and is suitable for large-scale promotion and application.

[0053] The methylation biomarker for detecting gastric cancer or the methylation biomarker combination provided by the present invention can be widely used in preparing reagents or kits for diagnosing gastric cancer.

[0054] The present invention provides a kit for detecting gastric cancer, the kit comprising a reagent for detecting the methylation level of the above-mentioned methylation biomarker or the above-mentioned combination of methylation biomarkers for detecting gastric cancer. The above-mentioned kit detects the difference between the methylation level of the methylation biomarker in the sample to be tested and the methylation level of the methylation biomarker in the sample from the subject without gastric cancer, thereby indicating whether the subject corresponding to the sample to be tested has gastric cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A schematic diagram of heat map clustering of 110 gastric cancer samples and 429 non-gastric cancer samples provided in Example 2 of the present invention;

[0057] Figure 2 Schematic diagram of ROC curves of each methylation biomarker combination provided in Example 3 of the present invention in the training set, validation set and independent validation set samples respectively. DETAILED DESCRIPTION

[0058] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] It should be noted that the experimental methods in the following examples of the present invention without specifying specific conditions are usually carried out under conventional conditions, such as the conditions described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or under the conditions recommended by the manufacturer. The various commonly used chemical reagents used in the examples are all commercially available products.

[0060] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0061] To facilitate understanding of the present technology, certain terms and phrases are defined below.

[0062] In this specification, the term "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0063] In this specification, the term "cancer" (also called carcinoma) generally refers to any type of malignant neoplasm, i.e., any morphological and / or physiological changes (based on genetic reprogramming) in target cells that show or have a tendency to develop cancerous characteristics compared to unaffected (healthy) wild-type control cells. Examples of such changes may involve cell size and shape (becoming larger or smaller), cell proliferation (increase in cell number), cell differentiation (change in physiological state), apoptosis (programmed cell death) or cell survival. Therefore, the term "gastric cancer" refers to cancerous growths in the parenchymal part of the stomach.

[0064] In this specification, the term "nucleic acid detection" refers to any method for determining the nucleotide composition of a target nucleic acid. Nucleic acid detection assays include, but are not limited to, DNA sequencing methods and probe hybridization methods.

[0065] In this specification, "methylated nucleotide" or "methylated nucleotide base" refers to the presence of a methyl moiety on a nucleotide base, wherein the methyl moiety is not present in a recognized typical 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. Therefore, 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. However, for the purposes of this article, thymine is not considered to be a methylated nucleotide when present in DNA, because thymine is a typical nucleotide base of DNA.

[0066] In this specification, the "methylation level", "methylation degree", "methylation state", "methylation profile" and "methylation status" of a nucleic acid molecule refer to the presence or absence of one or more methylated nucleotide bases in a nucleic acid molecule. For example, a nucleic acid molecule containing methylated cytosine is considered to be methylated (e.g., the methylation state of the nucleic acid molecule is methylated). A nucleic acid molecule that does not contain any methylated nucleotides is considered to be unmethylated.

[0067] In this specification, the methylation state can be optionally represented or indicated by a "methylation value" (e.g., representing the methylation frequency, score, ratio, percentage, etc.). The methylation value can be generated, for example, by quantifying the amount of intact nucleic acid present after restriction digestion with a methylation-dependent restriction enzyme, or by comparing the amplification spectrum after a bisulfite reaction, or by comparing the sequences of bisulfite-treated and untreated nucleic acids. Therefore, values ​​such as methylation values ​​represent the methylation state and can therefore be used as a quantitative indicator of the methylation state in multiple copies of a locus. The degree of co-methylation is represented or indicated by the methylation state of more than one methylation site, and co-methylation is defined as when the methylation state of more than one methylation site is methylated within a methylated region.

[0068] In this specification, "methylation rate", "methylation frequency" or "methylation percentage (%)" refers to the number of instances where a molecule or locus is methylated relative to the number of instances where the molecule or locus is unmethylated. For example, in some embodiments, the methylation rate of each target region is calculated as the number of co-methylated reads / total number of reads, where co-methylated reads are defined as reads in which 3 or more CpG sites are C in a window of 5 consecutive CpG sites in the target region.

[0069] In this specification, when the sequences differ in the degree (e.g., one has increased or decreased methylation relative to the other), frequency or pattern of methylation, the sequences are referred to as "differentially methylated" or as having a "methylation difference" or having a "different methylation state". The term "differential methylation" refers to the difference in the level or pattern of nucleic acid methylation in a cancer-positive sample compared to the level or pattern of nucleic acid methylation in a cancer-negative sample.

[0070] In this specification, the term "differentially methylated region" (DMR) refers to a DNA region containing one or more differentially methylated sites.

[0071] In the present specification, the term "methylation assay" or "methylation level detection" or "methylation degree detection" refers to any assay for determining the methylation status of one or more CpG dinucleotide sequences within a nucleic acid sequence.

[0072] In this specification, the term "bisulfite reagent" refers to a reagent that, in some embodiments, comprises bisulfite, disulfite, hydrogen sulfite or a combination thereof. After DNA is treated with a bisulfite reagent, unmethylated cytosine nucleotides are converted into uracil, while methylated cytosine and other bases remain unchanged, thereby making it possible to distinguish between methylated and unmethylated cytidine in, for example, a CpG dinucleotide sequence.

[0073] In this specification, the term "sample" refers to any substance that may contain a target molecule that needs to be analyzed, including biological samples. As used herein, "sample" or "biological sample" refers to any sample obtained from a live or viral (or prion) source or other macromolecule and biomolecule source, and includes any cell type or tissue of a subject from which nucleic acids, proteins and / or other macromolecules can be obtained. Samples or biological samples can be samples obtained directly from biological sources or processed samples. Samples or biological samples include, but are not limited to, body fluids (such as whole blood, plasma, serum, cerebrospinal fluid, synovial fluid, urine, sweat, semen, feces, sputum, tears, mucus, amniotic fluid, etc.), exudates, bone marrow samples, ascites, pelvic washings, pleural fluid, spinal fluid, lymph, eye fluid, extracts from nasal, throat or genital swabs, cell suspensions of digested tissues, or extracts of fecal matter, and tissue and organ samples from humans, animals (such as non-human mammals) and plants, and processed samples derived therefrom.

[0074] In this specification, the term "subject" can be a mammal or a cell, tissue, organ or part of the mammal. In the present invention, mammal refers to any kind of mammal, preferably a human (including a human, a human subject or a human patient). Subjects and mammals include, but are not limited to, farm animals, sports animals, pets, primates, horses, dogs, cats and rodents such as mice and rats.

[0075] In this specification, the term "extracellular DNA" or its synonyms "cfDNA (circulating free DNA)", "circulating DNA" and "free circulating DNA" refers to DNA that is not contained in intact cells in the sample or the corresponding body fluid from which the sample is obtained, but is freely circulating in the body fluid sample. Extracellular DNA is usually fragmented genomic DNA.

[0076] As used in the present invention, the term "sensitivity" refers to the proportion of positive samples detected from confirmed positive samples, and its calculation formula is: sensitivity = (detected positive / true positive), and true positive is confirmed as positive using the recognized gold standard. "Specificity" refers to the proportion of normal samples detected from a certain normal person, and its calculation formula is specificity = (detected negative / true negative).

[0077] In this specification, the term "AUC" is an abbreviation for "area under the curve". Specifically, it refers to the area under the receiver operating characteristic (ROC) curve. The ROC curve is a graph of the true positive ratio relative to the false positive ratio for different possible block cut points of a diagnostic test. It shows a compromise between sensitivity and specificity according to the selected cut point (any increase in sensitivity will be accompanied by a decrease in specificity). The area under the ROC curve (AUC) is a measure of the accuracy of a diagnostic test (the larger the area, the better; the best is 1; a random test will have an ROC curve on the diagonal with an area of ​​0.5; see: JPEgan. (1975) Signal Detection Theory and ROC Analysis, Academic Press, New York).

[0078] As used herein, diagnosis includes the detection or identification of a disease state or condition in a subject, determining the likelihood that a subject will develop a given disease or condition, determining the likelihood that a subject with a disease or condition will respond to treatment, determining the prognosis of a subject with a disease or condition (or its likely progression or regression), and determining the effectiveness of a treatment on a subject with a disease or condition.

[0079] The technical solution of the present invention is described in detail below.

[0080] According to one aspect of the present invention, a methylation biomarker for detecting gastric cancer comprises any one or any combination thereof selected from the following differentially methylated regions:

[0081] chr7:37488070-37488477, chr7:87229906-87230345, chr22:17849808-17850021, chr7:27196138-27196564, chr10:20105505-20105717, chr6:31830364-31830826, chr5:35925138-35925258, chr15:48010822-48010942 and chr6:39693254-39693374.

[0082] The present invention studies the methylation differences of DNA methylation modifications at specific CpG sites of plasma cfDNA in gastric cancer patients, and finds that the DNA methylation markers and their combinations can be used as diagnostic markers for non-invasive screening of gastric cancer. The present invention discloses 9 methylation biomarkers for detecting gastric cancer, and at the same time limits the combination of the above methylation biomarkers, which can significantly improve the detection rate of gastric cancer. The application of the above methylation biomarkers in non-invasive screening of gastric cancer has good sensitivity, specificity and accuracy, and is suitable for large-scale promotion and application.

[0083] It should be noted that the methylation biomarker whose differential methylation region is chr7:37488070-37488477 refers to the CpG site between chr7:37488070-37488477 in the hg19 human genome;

[0084] The methylation biomarker whose differential methylation region is chr7:87229906-87230345 refers to the CpG site between chr7:87229906-87230345 in the hg19 human genome;

[0085] The methylation biomarker whose differential methylation region is chr22:17849808-17850021 refers to the CpG site between chr22:17849808-17850021 in the hg19 human genome;

[0086] The methylation biomarker whose differential methylation region is chr7:27196138-27196564 refers to the CpG site between chr7:27196138-27196564 in the hg19 human genome;

[0087] The methylation biomarker whose differential methylation region is chr10:20105505-20105717 refers to the CpG site between chr10:20105505-20105717 in the hg19 human genome;

[0088] The methylation biomarker whose differential methylation region is chr6:31830364-31830826 refers to the CpG site between chr6:31830364-31830826 in the hg19 human genome;

[0089] The methylation biomarker whose differential methylation region is chr5:35925138-35925258 refers to the CpG site between chr5:35925138-35925258 in the hg19 human genome;

[0090] The methylation biomarker whose differential methylation region is chr15:48010822-48010942 refers to the CpG site between chr15:48010822-48010942 in the hg19 human genome;

[0091] The methylation biomarker whose differentially methylated region is chr6:39693254-39693374 refers to the CpG site between chr6:39693254-39693374 in the hg19 human genome.

[0092] Those skilled in the art understand that the methylation biomarker may also include a continuous segment of at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98%, at least 99% of the full-length sequence of each differentially methylated region. In some embodiments, the continuous segment contains part or all of the (differential) methylation sites in the original differentially methylated region. For the embodiments of the methylation biomarker described above, the same applies to continuous segments from differentially methylated regions. In some embodiments, a continuous segment of a differentially methylated region and another differentially methylated region may also be used in combination to constitute the embodiments of the methylation biomarker described above.

[0093] According to one aspect of the present invention, a methylation biomarker combination for detecting gastric cancer is provided, wherein the methylation biomarker combination is obtained based on the above-mentioned methylation biomarker for detecting gastric cancer;

[0094] The methylation biomarker combination includes:

[0095] The differentially methylated region is at least one of the methylation biomarkers of chr7:37488070-37488477;

[0096] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr7:87229906-87230345;

[0097] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr22:17849808-17850021;

[0098] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr7:27196138-27196564;

[0099] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr10:20105505-20105717;

[0100] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr6:31830364-31830826;

[0101] and / or, at least one of the methylation biomarkers whose differentially methylated region is chr5:35925138-35925258;

[0102] and / or, the differentially methylated region is at least one of the methylation biomarkers of chr15:48010822-48010942;

[0103] And / or, the differentially methylated region is at least one of the methylation biomarkers of chr6:39693254-39693374.

[0104] In a preferred embodiment of the present invention, the methylation biomarker combination comprises:

[0105] At least one of the methylation biomarkers whose differential methylation region is chr7:37488070-37488477, at least one of the methylation biomarkers whose differential methylation region is chr7:87229906-87230345, and at least one of the methylation biomarkers whose differential methylation region is chr22:17849808-17850021;

[0106] Or, the methylation biomarker combination includes:

[0107] At least one of the methylation biomarkers whose differential methylation region is chr7:37488070-37488477, at least one of the methylation biomarkers whose differential methylation region is chr7:87229906-87230345, at least one of the methylation biomarkers whose differential methylation region is chr22:17849808-17850021, and at least one of the methylation biomarkers whose differential methylation region is chr7:27196138-27196564;

[0108] Or, the methylation biomarker combination includes:

[0109] At least one of the methylation biomarkers whose differential methylation region is chr7:37488070-37488477, at least one of the methylation biomarkers whose differential methylation region is chr7:87229906-87230345, at least one of the methylation biomarkers whose differential methylation region is chr22:17849808-17850021, at least one of the methylation biomarkers whose differential methylation region is chr7:27196138-27196564, and at least one of the methylation biomarkers whose differential methylation region is chr10:20105505-20105717;

[0110] Or, the methylation biomarker combination includes:

[0111] At least one of the methylation biomarkers whose differential methylation region is chr7:37488070-37488477, at least one of the methylation biomarkers whose differential methylation region is chr7:87229906-87230345, at least one of the methylation biomarkers whose differential methylation region is chr22:17849808-17850021, at least one of the methylation biomarkers whose differential methylation region is chr7:27196138-27196564, at least one of the methylation biomarkers whose differential methylation region is chr10:20105505-20105717, and at least one of the methylation biomarkers whose differential methylation region is chr6:31830364-31830826;

[0112] Or, the methylation biomarker combination includes:

[0113] At least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-2 at least one of the methylation biomarkers of chr7196564, at least one of the methylation biomarkers of the differentially methylated region chr10:20105505-20105717, at least one of the methylation biomarkers of the differentially methylated region chr6:31830364-31830826, and at least one of the methylation biomarkers of the differentially methylated region chr5:35925138-35925258;

[0114] Or, the methylation biomarker combination includes:

[0115] At least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-27196564, at least one of the methylation biomarkers whose differential methylation region is chr10:20105505-20105717, at least one of the methylation biomarkers whose differential methylation region is chr6:31830364-31830826, at least one of the methylation biomarkers whose differential methylation region is chr5:35925138-35925258, and at least one of the methylation biomarkers whose differential methylation region is chr15:48010822-48010942;

[0116] Or, the methylation biomarker combination includes:

[0117] At least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-27196564, at least one of the methylation biomarkers with a differential methylation region of chr10:20105505- At least one of the methylation biomarkers of 20105717, at least one of the methylation biomarkers with a differential methylation region of chr6:31830364-31830826, at least one of the methylation biomarkers with a differential methylation region of chr5:35925138-35925258, at least one of the methylation biomarkers with a differential methylation region of chr15:48010822-48010942, and at least one of the methylation biomarkers with a differential methylation region of chr6:39693254-39693374.

[0118] In a preferred embodiment of the present invention, the gastric cancer is gastric cancer present in a subject, and the subject is a mammal;

[0119] Preferably, the gastric cancer includes any one of stage I, stage II, stage III or stage IV gastric cancer.

[0120] In the above preferred embodiment, the mammal is a human.

[0121] In a preferred embodiment of the present invention, the methylation degree of the methylation biomarker combination is detected by one or more of the following methods:

[0122] Fluorescence quantitative PCR, methylation-specific PCR, digital PCR, DNA methylation chip, targeted DNA methylation sequencing, methylation-sensitive restriction endonuclease (MS-RE)-PCR / Southern method, direct sequencing, methylation-sensitive single nucleotide primer extension (Ms-SnuPE), bisulfite-combined restriction endonuclease method (COBRA), methylation-sensitive single-strand conformation analysis (MS-SSCA), methylation-sensitive denaturing gradient gel electrophoresis (MSDGGE), methylation-specific denaturing high-performance liquid chromatography (MS-DHPLC), methylation-specific microarray (MSO), methylation-sensitive melting curve analysis (MS-MCA) methylation-sensitive dot analysis (MS-DBA), methylation-specific multiple ligation-dependent probe amplification, bisulfite sequencing and pyrosequencing.

[0123] According to one aspect of the present invention, a methylation biomarker or a combination of methylation biomarkers for detecting gastric cancer is used in the following (i) to (ii):

[0124] (i) Use in the preparation of a reagent or kit for diagnosing gastric cancer;

[0125] (ii) Use of a reagent for determining the methylation level of a methylation biomarker in the preparation of a reagent or kit for diagnosing gastric cancer.

[0126] The methylation biomarker for detecting gastric cancer or the methylation biomarker combination provided by the present invention can be widely used in preparing reagents or kits for diagnosing gastric cancer.

[0127] According to one aspect of the present invention, a kit for detecting gastric cancer comprises a reagent for detecting the methylation level of the above-mentioned methylation biomarker for detecting gastric cancer or the above-mentioned methylation biomarker combination.

[0128] The present invention provides a kit for detecting gastric cancer, the kit comprising a reagent for detecting the methylation level of the above-mentioned methylation biomarker or the above-mentioned combination of methylation biomarkers for detecting gastric cancer. The above-mentioned kit detects the difference between the methylation level of the methylation biomarker in the sample to be tested and the methylation level of the methylation biomarker in the sample from the subject without gastric cancer, thereby indicating whether the subject corresponding to the sample to be tested has gastric cancer.

[0129] Preferably, the gastric cancer is gastric cancer present in a subject, which is a malignant tumor of different classifications, different tumor sizes and different stages. Optionally, the subject is a mammal; preferably, the mammal is a human.

[0130] In some specific embodiments, if the methylation level of the methylation biomarker in the test sample is different from the methylation level of the methylation biomarker in samples from healthy subjects or adjacent cancer tissues or subjects with other cancers (non-gastric cancer) or benign diseases, it indicates that the subject corresponding to the test sample has gastric cancer.

[0131] In a preferred embodiment of the present invention, the subject not suffering from gastric cancer includes a healthy subject or a subject suffering from a benign digestive system disease.

[0132] In a preferred embodiment of the present invention, the sample to be tested is selected from one or more of tissue, whole blood, plasma, saliva, serum, urine, urine exfoliated cells, urine sediment, and urine supernatant.

[0133] As a preferred embodiment, the sample to be detected is a biopsy sample, for example, the biopsy sample is tissue, blood, or body fluid.

[0134] Preferably, the sample to be tested is whole blood, plasma or serum. Due to the convenience of sampling and other aspects, the diagnosis of the present invention includes early screening, detection / diagnosis or auxiliary detection / diagnosis of gastric cancer. In some specific embodiments of the present invention, the methylated region is a methylated region present in cfDNA. In some more specific embodiments, the methylated region is a methylated region present in cfDNA in whole blood, plasma or serum samples.

[0135] In a preferred embodiment of the present invention, the kit is detected by any of the following detection methods:

[0136] Apply fluorescent quantitative PCR, methylation-specific PCR, digital PCR, DNA methylation chip, targeted DNA methylation sequencing, whole-genome methylation sequencing or DNA methylation mass spectrometry;

[0137] In the above preferred embodiment, the kit is used for targeted DNA methylation sequencing.

[0138] Preferably, the present invention provides a method for diagnosing gastric cancer, comprising the following steps:

[0139] 1. Obtain the test samples from the subjects;

[0140] 2. Extracting genomic DNA and / or free DNA from the sample to be tested;

[0141] 3. Detecting the methylation level of the methylation biomarker as described in the first aspect of the present invention in the DNA;

[0142] 4. Determine whether the subject has gastric cancer.

[0143] Preferably, the present invention provides a system for diagnosing gastric cancer, wherein the system comprises a detection device, a calculation device and an output device;

[0144] The detection device comprises an injector and a detector, wherein the injector is used to collect a sample to be tested from a subject, and the detector is used to detect the methylation level of the methylation biomarker described in the first aspect of the present invention in the sample to be tested;

[0145] The computing device includes a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program stored in the memory to achieve the following determination:

[0146] If the methylation level of the methylation biomarker of the present invention in the sample is different from the methylation level of the methylation biomarker measured in a sample from a subject without gastric cancer, it is determined that the subject corresponding to the sample has gastric cancer.

[0147] The technical solution of the present invention will be further described below in conjunction with embodiments.

[0148] Example 1: A novel ctDNA methylation marker detection method for gastric cancer diagnosis

[0149] The methylation library and sequencing of 539 collected blood samples were performed. The process and steps are as follows:

[0150] (I) DNA extraction and methylation library construction:

[0151] (1) DNA extraction:

[0152] Using MagMAX TM Circulating free DNA extraction kit (MagMAX TM Cell Free DNA Isolation Kit) was used to extract circulating free DNA in plasma. For specific steps, please refer to the product manual.

[0153] (2) Conversion:

[0154] After DNA is treated with bisulfite conversion, the unmethylated C bases in the CG double-base sequence in the DNA double strand will be converted to U bases, while the C bases in the methylated CG double-base sequence will not undergo base conversion. The extracted plasma cfDNA was bisulfite converted using the DNA bisulfite conversion kit Zymo Lightning Conversion Reagent. For specific steps, please refer to the product manual.

[0155] (3) Construction of DNA methylation pre-library:

[0156] After completing the bisulfite conversion treatment of plasma cfDNA samples, targeted DNA methylation pre-library construction was performed using the AnchorIRIS library construction kit.

[0157] (3.1), reaction 1:

[0158] 1. Add the converted 17 μL sample to the following reagents for reaction, as shown in Table 1.

[0159] Table 1:

[0160] Components Volume (μL) After conversion 17 Repair fluid 1 8 Repair fluid 2 5 Total volume 30

[0161] 2. Place the sample in a PCR instrument and perform the reaction according to the following procedure, as shown in Table 2.

[0162] Table 2:

[0163]

[0164]

[0165] 3. When the second step of the PCR reaction (65°C) reaches 5 minutes, immediately remove the sample from the PCR instrument, insert it directly into ice, and leave it for more than 2 minutes before proceeding to the next step.

[0166] (3.2), reaction 2:

[0167] 1. Prepare the following reaction solution, as shown in Table 3.

[0168] Table 3:

[0169] Components Single dosage (μL) Product of the previous reaction (reaction 1) 30 Connection Fluid A1 10 Ligating Fluid A2 4 Connection Fluid A3 2 Connection Fluid A4 2 Connection Fluid A5 2 Total volume 50

[0170] 2. Place the sample in a PCR instrument and perform the reaction according to the following procedure, as shown in Table 4.

[0171] Table 4:

[0172] temperature time 20℃ 30min 95℃ 5min 10℃ hold Heat cover 105℃

[0173] (3.3), reaction 3:

[0174] 1. Prepare the following reaction solution, as shown in Table 5.

[0175] Table 5:

[0176] Components Single dosage (μL) Product of the previous reaction (reaction 2) 50 DEPC water 10 Pre-amplification buffer 3 25 Pre-amplification buffer 1 5 Pre-amplification buffer 2 2 Pre-amplification buffer 4 8 Total volume 100

[0177] 2. Place the sample in a PCR instrument and perform the reaction according to the following procedure, as shown in Table 6.

[0178] Table 6:

[0179]

[0180]

[0181] 3. XP Beads purification I:

[0182] Add 180 μL XP Beads (need to be equilibrated at room temperature for half an hour in advance) to purify the product of reaction 3, and elute with 31 μL EB. The specific steps of purification are as follows:

[0183] a. Take the reaction product of reaction 3 and centrifuge it. Add 180 μL of Agencourt AMPureBeads to each sample and mix well by pipetting.

[0184] b. Incubate at room temperature for 5 minutes.

[0185] c. Immediately separate and place on a magnetic rack for 5 minutes. After the liquid becomes clear, discard the supernatant.

[0186] d. Add 500 μL of 80% anhydrous ethanol, let stand for 30 seconds, and aspirate away the ethanol.

[0187] e. Repeat step d) once.

[0188] f. Centrifuge briefly, place the PCR tube on a magnetic rack, and remove the remaining ethanol.

[0189] g. Open the lid and dry the magnetic beads for 2-3 minutes, being careful not to overdry them.

[0190] h. Add 31 μL EB for elution, mix thoroughly with a pipette, and let stand at room temperature for 3 minutes.

[0191] I. Centrifuge briefly, place the PCR tube on a magnetic rack, and let it stand for 3 minutes.

[0192] J. Pipette 30 μL of supernatant into a new 200 μL PCR tube.

[0193] (3.4), reaction 4:

[0194] 1. Prepare the following reaction solution, as shown in Table 7.

[0195] Table 7:

[0196] Components Volume (μL) Volume of the product of the previous step (reaction 3) 30 Ligating Fluid B1 10 Connection Fluid B2 4 Connection Fluid B3 2 Connection Fluid B4 2 Connection Fluid B5 2 Total volume 50

[0197] 2. Place the sample in a PCR instrument and perform the reaction according to the following procedure, as shown in Table 8.

[0198] Table 8:

[0199] temperature time Number of cycles 37℃ 30min 1 95℃ 5min 1 10℃ Hold 1

[0200] (3.5), reaction 5:

[0201] 1. Prepare the following reaction solution, as shown in Table 9.

[0202] Table 9:

[0203]

[0204] 2. Place the sample in a PCR instrument and perform the reaction according to the following procedure, as shown in Table 10.

[0205] Table 10:

[0206]

[0207] 3. XP Beads purification II

[0208] Add 71 μL XP Beads (need to be equilibrated at room temperature for half an hour in advance) to purify the PCR product of reaction 5, and elute with 55 μL EB. The specific steps of purification are as follows:

[0209] a. Take the reaction product of reaction 5 and centrifuge it. Add 71 μL XP Beads to each sample and mix well by pipetting.

[0210] b. Incubate at room temperature for 5 minutes.

[0211] c. Centrifuge briefly and place on a magnetic rack for 5 minutes. After the liquid becomes clear, discard the supernatant.

[0212] d. Add 500 μL of 80% anhydrous ethanol, let stand for 30 seconds, and aspirate away the ethanol.

[0213] e. Repeat step d) once.

[0214] f. Centrifuge briefly, place the PCR tube on a magnetic rack, and remove the remaining ethanol.

[0215] g. Open the lid and dry the magnetic beads for 2-3 minutes, being careful not to overdry them.

[0216] h. Add 55 μL EB for elution, mix thoroughly with a pipette, and let stand at room temperature for 3 minutes.

[0217] i. Centrifuge briefly, place the PCR tube on a magnetic rack, and let it stand for 3 minutes.

[0218] j. Pipette 54 μL of supernatant into a new 1.5 mL centrifuge tube.

[0219] k.Qubit quantification: Take 1 μL and use Qubit dsDNA HS Assay Kit to quantify the library.

[0220] (II) DNA methylation sequencing targeted enrichment:

[0221] After the DNA methylation sequencing pre-library was constructed, the cfDNA pre-library (i.e. the purified reaction 5 product) was targeted enriched using the IDT xGen Lockdown Reagents kit. The specific steps are as follows:

[0222] (1) Pre-library hybridization experimental steps:

[0223] 1. Preparation:

[0224] 1. According to the concentration of the pre-library, calculate the input volume of each pre-library. The input volume of each pre-library is 250ng. Every 8-16 pre-libraries can be placed in the same reaction tube (pool) for hybridization. When the number of pre-libraries is less than 8, other pre-libraries need to be used to make up.

[0225] 2. Thaw the probe solution at 4°C and thaw the other reagents at room temperature. After thawing, vortex and mix well for later use.

[0226] 3. Preheat the vacuum concentrator: set the temperature to 60°C, V-AQ mode, brake to OFF, and run for 20 minutes.

[0227] 4. Note that the amplification primer numbers of samples in the same reaction tube cannot be exactly the same.

[0228] 2. Specific process:

[0229] After all reagents and libraries are melted, they all need to be Vortex mixed and centrifuged for 5 seconds for later use. The libraries are arranged in groups of four on the EP tube rack according to the hybridization table. xGen Hybridization Buffer Enhancer is kept at room temperature for later use. All other reagents are placed on ice for later use.

[0230] 1. Add the pre-library into the corresponding 0.2mL PCR tube according to the pre-library combination and input volume in the hybridization experiment record sheet.

[0231] 2. Prepare the premix by adding the components as shown in Table 11.

[0232] Table 11:

[0233]

[0234] Preparation for concentration reaction: Add 7 μL of premixed solution to the PCR tube in step 2. After adding and mixing, centrifuge instantly and concentrate according to the above reaction program settings until it is completely dried to powdery crystals. If there is more liquid, the drying time can be appropriately extended.

[0235] 3. Reaction 6:

[0236] Prepare the premix by adding each component on ice as follows and use it immediately after preparation, as shown in Table 12.

[0237] Table 12:

[0238]

[0239] PCR reaction preparation: Add 17 μL of premixed solution to the concentrated and dried PCR tube in step 2, mix the sample, centrifuge instantly, and perform the reaction according to the above PCR reaction program settings. Hybridize at 65℃ for 4-16h and then perform elution.

[0240] (2) Pre-library elution experimental steps:

[0241] 1. Preparation:

[0242] 1. Open the hybridization chamber in advance and set it to 65℃ for preheating.

[0243] 2. Take out the Dynabeads M-270 Streptavidin 30 minutes in advance and place it at room temperature for equilibrium. Put the remaining elution reagents into a thin film glove and thaw in the hybridization box until they are completely dissolved and free of crystals.

[0244] 3. Turn on the shaking thermostatic metal bath and set it to 65°C for preheating.

[0245] 4. Prepare 1× diluted working solution according to the number of reaction tubes (pool) as follows, and place the diluted working solution that needs to be heated in a shaking constant temperature metal bath for preheating, as shown in Table 13.

[0246] Table 13:

[0247]

[0248]

[0249] 5.M270 magnetic beads preparation:

[0250] a. Vortex the M-270Beads thoroughly, take 50μL to a 1.5mL centrifuge tube, place it on a 1.5mL magnetic rack, let it stand for 30s, then aspirate and discard the supernatant to avoid aspirating the magnetic beads. A small amount of liquid may remain.

[0251] b. Take 100 μL of 1× Bead Wash Buffer into the centrifuge tube prepared in step a, vortex to mix, centrifuge briefly, place on a magnetic rack, and discard the supernatant after the liquid is clarified. Repeat the operation twice.

[0252] c. Prepare as follows: take 17 μL of the magnetic bead mixture into the centrifuge tube in step b, mix well by pipetting to resuspend the magnetic beads, and transfer to a 0.2 mL PCR tube for later use, as shown in Table 14.

[0253] Table 14:

[0254] Components Volume per reaction (μL) xGen 2×Hybridization Buffer 8.5 xGen Hybridization Buffer Enhancer 2.7 DEPC water 5.8 Total volume 17

[0255] 2. Experimental operation:

[0256] 1. Rapidly transfer the hybridized PCR tube to a 0.2mL 96-well yellow plate preheated in a 65℃ hybridization box, centrifuge quickly, and place in a 65℃ hybridization box after centrifugation. Transfer 17μL of the magnetic bead mixture containing M270 magnetic beads to the PCR tube of the hybridization pool sample, blow and mix, avoid bubbles during the operation, place it in the hybridization box, float on the float, start the rotor at medium speed, and rotate for 45 minutes or more. Repeat the operation for multiple samples.

[0257] 2. Prepare two 1.5 mL centrifuge tubes of the same quantity as the sample and number them. Preheat one tube in a 65°C metal bath and keep the other tube at room temperature for later use.

[0258] 3. Take 100μL of preheated xGen 1×Wash Buffer 1 diluent and add it to the sample. Mix well by pipetting. Transfer all to a preheated 1.5mL centrifuge tube. Centrifuge and place on a magnetic rack. After the liquid is clarified, discard the supernatant.

[0259] 4. Add 150 μL of preheated xGen 1× Stringent Wash Buffer, vortex to mix, incubate on a thermomixer at 65°C, 2000 rpm for 5 min, centrifuge, place on a magnetic rack, and discard the supernatant after the liquid is clarified. Repeat the operation once.

[0260] 5. Add 150 μL of xGen 1×Wash Buffer 1 diluent (without preheating), mix by pipetting, transfer to a new centrifuge tube, and incubate at room temperature at 1800 rpm for 2 minutes; centrifuge, place on a magnetic rack, and discard the supernatant after the liquid is clarified.

[0261] 6. Add 150 μL of xGen 1×Wash Buffer 2 diluent, vortex to mix, and incubate at room temperature at 1800 rpm for 2 minutes; centrifuge, place on a magnetic rack, and discard the supernatant after the liquid is clarified.

[0262] 7. Add 150 μL of xGen 1×Wash Buffer 3 diluent and vortex to mix. Mix at room temperature at 1800 rpm and incubate for 2 minutes. Centrifuge and place on a magnetic rack. After the liquid is clarified, discard the supernatant.

[0263] 8. Add 20 μL of DEPC water, vortex at medium speed to mix and resuspend the magnetic beads, and centrifuge for later use.

[0264] 9. Reaction 7:

[0265] Prepare a premix by adding each component on ice as follows, as shown in Table 15.

[0266] Table 15:

[0267]

[0268]

[0269] PCR reaction preparation: Add the sample and mix as above, centrifuge briefly, place in PCR instrument and start the reaction program.

[0270] 10. Magnetic bead purification:

[0271] a.Equilibrate XP Beads at room temperature for 30 min, mix well, take 75 μL XP Beads and reaction product 7, incubate at room temperature for 5 min, centrifuge for 30 s, adsorb on magnetic rack for 5 min until the liquid is clear, aspirate and discard supernatant, rinse with 500 μL 80% ethanol for 30 s, discard supernatant, add 500 μL 80% ethanol to rinse for 30 s, discard supernatant, centrifuge for 1 min, put on the rack until the liquid is clear, aspirate the remaining liquid, open the lid and dry until the surface of the magnetic beads is non-reflective, add 22 μL EB to elute, mix well, incubate at room temperature for 5 min, centrifuge for 1-2 min, adsorb on the rack for 3-5 min until the liquid is clear, collect 20 μL supernatant.

[0272] b. The supernatant collected after purification is the final library. The concentration of the final library is measured using Qubit. Part of the final library is taken for sequencing, and the remaining final library samples are stored in a -20°C refrigerator.

[0273] (III) Sequencing the samples after hybridization capture is performed using a sequencer from Illumina to obtain sequencing results.

[0274] (IV) Analysis of off-board data:

[0275] The raw fastq files obtained by sequencing were analyzed using fastp 0.19.6 software to analyze the sequencing quality of each batch of data. If the proportion of Q30 bases in the batch data was higher than 75%, the quality control passed. The adapter sequences and low-quality base fragments introduced during the library construction process were removed from the sequencing reads to obtain clean fastq files.

[0276] The clean reads were aligned to the human genome (hg19) using the sequence alignment software bismark v0.22.1 to obtain the alignment bam file. The quality control passed if the conversion efficiency of sulfite treatment was ≥97%. The reads were deduplicated according to the UMI, and the deduplicated alignment bam file was obtained. The methylation status of the CpG sites in the target segment was extracted from the bam file, and the methylation rate of each target region was calculated as the number of co-methylated reads / total number of reads.

[0277] Co-methylated reads are defined as reads with 3 or more CpG sites as C in a window of 5 consecutive CpG sites in the target region. The methylation rate is obtained for subsequent data analysis.

[0278] Example 2

[0279] This embodiment discloses a methylation-specific biomarker for diagnosing gastric cancer. In the early stage, based on the methylation data of 395 gastric cancer samples and 113 gastrointestinal adjacent cancer control samples from the cancer methylation public database TCGA and 23 gastric cancer samples and 23 adjacent cancer control samples from the local methylation EPIC sequencing database, after the methylation level difference test and analysis of different groups, combined with the literature and gene function annotations, a total of 9 methylation markers for diagnosing gastric cancer were screened, and then a methylation targeted capture sequencing platform was established.

[0280] A total of 539 blood samples were collected (110 gastric cancer cases and 429 non-gastric cancer cases), including 216 training set samples (44 gastric cancer cases and 172 non-gastric cancer cases), 162 validation set samples (33 gastric cancer cases and 129 non-gastric cancer cases), and 161 independent validation set samples (33 gastric cancer cases and 128 non-gastric cancer cases). Their clinical information is shown in Table 16.

[0281] The 9 methylation markers (hereinafter referred to as markers) and their independent AUC values ​​are shown in Table 17. The heat map distribution of the 9 methylation markers is shown in Figure 1 .

[0282] Figure 1 A heat map clustering diagram of 110 gastric cancer samples and 429 non-gastric cancer samples provided in this embodiment.

[0283] Table 16: Clinical information of 539 blood samples:

[0284]

[0285]

[0286]

[0287]

[0288]

[0289]

[0290]

[0291]

[0292]

[0293]

[0294]

[0295]

[0296]

[0297] Note: The above table uses the UICC / AJCC staging system (8th edition) for gastric cancer staging.

[0298] Table 17: Specific expression data of 9 methylation markers in 539 blood samples of gastric cancer and non-gastric cancer are shown in Table 17.

[0299] Table 17:

[0300] Marker Anno_type Anno_gene AUC chr7:37488070-37488477 UTR5 ELMO1 77.30％ chr7:87229906-87230345 UTR5 ABCB1 80.60％ chr22:17849808-17850021 intronic CECR2 79.30％ chr7:27196138-27196564 exonic HOXA7 85.40％ chr10:20105505-20105717 UTR5 PLXDC2 77.40％ chr6:31830364-31830826 exonic NEU1 66.30％ chr5:35925138-35925258 intronic CAPSL 72.20％ chr15:48010822-48010942 UTR5 SEMA6D 74.80％ chr6:39693254-39693374 upstream KIF6 70.90％

[0301] As shown in Table 17, among the above 9 methylation markers, chr7:87229906-87230345 and chr7:27196138-27196564 had the best overall performance, with AUC values ​​exceeding 80%.

[0302] Example 3

[0303] Using the three batches of data sets collected in Example 2, the methylation signals of the nine methylation markers of these samples were detected according to the library construction and sequencing technology of Example 1, and three core methylation marker information (chr7:37488070-37488477, chr7:87229906-87230345, chr22:17849808-17850021) were used.

[0304] The co-methylation rate values ​​of the three methylation markers are used as input, and the negative / positive is judged by the logistic regression formula fitting score method. The model calculation formula of the logistic regression obtained by the training set is as follows:

[0305] Logistic regression formula score = 1 / (1+e (-x) );

[0306] Where x = weight 1 × methylation marker 1 (e.g. chr17: 49021947-49022096) + weight 2 × methylation marker 2 (e.g. chr19: 11998652-11998772) + ... + weight n × methylation marker n + intercept, weight 1-n and intercept are constants. e represents a natural constant, and its value is approximately 2.718281828459045.

[0307] Logistic regression formula fitting was implemented using the Logistic Regression software package of Sklearn, with default parameters. The higher the co-methylation rate, the higher the score of the logistic regression formula fitting.

[0308] The model performance results of the three methylation markers showed that in the training set, the AUC was 83.65%, the sensitivity (SE) was 68.18%, the specificity (SP) was 85.47%, and the accuracy (ACC) reached 81.94%.

[0309] In the validation set, the AUC was 84.06%, the sensitivity SE was 63.64%, the specificity SP was 86.82%, and the accuracy ACC reached 82.1%.

[0310] In the independent validation set, the AUC was 87.42%, the sensitivity SE was 72.73%, the specificity SP was 83.59%, and the accuracy ACC reached 81.37%.

[0311] The ROC curves of each batch of data are shown in Figure 2 .

[0312] Figure 2 Schematic diagram of ROC curves of each methylation biomarker combination provided in this embodiment in the training set, validation set and independent validation set samples.

[0313] Figure 2 The middle 3 marker group includes: at least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, and at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021;

[0314] Example 4

[0315] On the basis of Example 3, the number of markers was randomly expanded to 4, 5, 6, 7, 8, and 9 marker combinations, and then a logistic regression model was constructed in the training set, and the test was verified in the validation set and the independent validation set. The ROC curve of its performance is shown in Figure 2 As shown:

[0316] Figure 2The middle 4 marker group includes: at least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, and at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-27196564;

[0317] Figure 2 The 5-marker group includes: at least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-27196564 and at least one of the methylation biomarkers with a differential methylation region of chr10:20105505-20105717.

[0318] Figure 2 The 6 marker group includes: at least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-27196564, at least one of the methylation biomarkers with a differential methylation region of chr10:20105505-20105717, and at least one of the methylation biomarkers with a differential methylation region of chr6:31830364-31830826;

[0319] Figure 2The 7 marker group includes: at least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:2719 at least one of the methylation biomarkers of chr6:31830364-31830826, and at least one of the methylation biomarkers of chr5:35925138-35925258;

[0320] Figure 2 The 8 marker group includes: at least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-27196564 at least one of the methylation biomarkers of the differentially methylated region chr10:20105505-20105717, at least one of the methylation biomarkers of the differentially methylated region chr6:31830364-31830826, at least one of the methylation biomarkers of the differentially methylated region chr5:35925138-35925258, and at least one of the methylation biomarkers of the differentially methylated region chr15:48010822-48010942;

[0321] Figure 2The 9 marker group includes: at least one of the methylation biomarkers with a differential methylation region of chr7:37488070-37488477, at least one of the methylation biomarkers with a differential methylation region of chr7:87229906-87230345, at least one of the methylation biomarkers with a differential methylation region of chr22:17849808-17850021, at least one of the methylation biomarkers with a differential methylation region of chr7:27196138-27196564, at least one of the methylation biomarkers with a differential methylation region of chr10:201 05505-20105717, at least one of the methylation biomarkers with a differential methylation region of chr6:31830364-31830826, at least one of the methylation biomarkers with a differential methylation region of chr5:35925138-35925258, at least one of the methylation biomarkers with a differential methylation region of chr15:48010822-48010942, and at least one of the methylation biomarkers with a differential methylation region of chr6:39693254-39693374.

[0322] Specifically, the performance of the training set is shown in Table 18, the performance of the validation set is shown in Table 19, and the performance of the independent validation set is shown in Table 20.

[0323] Table 18. Performance of each methylation marker combination in the training set

[0324] AUC Sensitivity Specificity Accuracy Marker combination 83.65％ 68.18％ 85.47％ 81.94％ 3marker group 86.83％ 65.91％ 84.88％ 81.02％ 4 marker group 87.76％ 68.18％ 85.47％ 81.94％ 5 marker group 87.84％ 68.18％ 85.47％ 81.94％ 6marker group 88.68％ 70.45％ 84.88％ 81.94％ 7marker group 88.93％ 70.45％ 84.88％ 81.94％ 8marker group 88.35％ 72.73％ 84.88％ 82.41％ 9marker group

[0325] Table 19. Performance of each methylation marker combination in the validation set

[0326] AUC Sensitivity Specificity Accuracy Marker combination 84.06％ 63.64％ 86.82％ 82.10％ 3marker group 88.04％ 72.73％ 86.82％ 83.95％ 4 marker group 89.09％ 69.70％ 88.37％ 84.57％ 5 marker group 89.56％ 69.70％ 88.37％ 84.57％ 6marker group 91.05％ 78.79％ 89.92％ 87.65％ 7marker group 90.94％ 75.76％ 86.82％ 84.57％ 8marker group 90.84％ 78.79％ 86.05％ 84.57％ 9marker group

[0327] Table 20. Performance of each methylation marker combination in the independent validation set

[0328]

[0329]

[0330] From the above results, it can be seen that the AUC of the training set is above 83%, the AUC of the validation set is above 84%, and some individual combinations even reach 91%. The overall AUC of the independent validation set is above 87%, indicating that the model constructed by the training set is relatively stable and these biomarkers also show good distinguishing performance in new samples, and can be used as methylation markers to distinguish between gastric cancer and non-gastric cancer samples.

[0331] The performance of different combination models in the validation set and independent validation set was compared with the currently marketed gastric cancer auxiliary detection product (RNF180 / Septin9 gene methylation detection kit (PCR fluorescent probe method) (National Medical Device Registration No. 20203400447), with a sensitivity of 61.76%, a specificity of 85.07%, and an accuracy of 72.04%). The accuracy of these markers and their combination models is better than that of the marketed products.

[0332] In summary, it can be seen that the methylation markers screened by this method have a very high correlation with the diagnosis of gastric cancer.

[0333] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A methylation biomarker combination for detecting gastric cancer, characterized in that: The methylation biomarker combination is: A methylation biomarker whose differential methylation region is chr7:37488070-37488477, a methylation biomarker whose differential methylation region is chr7:87229906-87230345, and a methylation biomarker whose differential methylation region is chr22:17849808-17850021; Or, the methylation biomarker combination is: A methylation biomarker whose differential methylation region is chr7:37488070-37488477, a methylation biomarker whose differential methylation region is chr7:87229906-87230345, a methylation biomarker whose differential methylation region is chr22:17849808-17850021, and a methylation biomarker whose differential methylation region is chr7:27196138-27196564; Or, the methylation biomarker combination is: A methylation biomarker of a differentially methylated region of chr7:37488070-37488477, a methylation biomarker of a differentially methylated region of chr7:87229906-87230345, a methylation biomarker of a differentially methylated region of chr22:17849808-17850021, a methylation biomarker of a differentially methylated region of chr7:27196138-27196564, and a methylation biomarker of a differentially methylated region of chr10:20105505-20105717; Or, the methylation biomarker combination is: Methylation biomarkers of differentially methylated regions of chr7:37488070-37488477, methylation biomarkers of differentially methylated regions of chr7:87229906-87230345, methylation biomarkers of differentially methylated regions of chr22:17849808-17850021, methylation biomarkers of differentially methylated regions of chr7:27196138-27196564, methylation biomarkers of differentially methylated regions of chr10:20105505-20105717, and methylation biomarkers of differentially methylated regions of chr6:31830364-31830826; Or, the methylation biomarker combination is: Methylation biomarkers of differentially methylated regions of chr7:37488070-37488477, methylation biomarkers of differentially methylated regions of chr7:87229906-87230345, methylation biomarkers of differentially methylated regions of chr22:17849808-17850021, methylation biomarkers of differentially methylated regions of chr7:27196138-27196564, methylation biomarkers of differentially methylated regions of chr10:20105505-20105717, methylation biomarkers of differentially methylated regions of chr6:31830364-31830826, and methylation biomarkers of differentially methylated regions of chr5:35925138-35925258; Or, the methylation biomarker combination is: The methylation biomarker of the differential methylation region is chr7:37488070-37488477, the methylation biomarker of the differential methylation region is chr7:87229906-87230345, the methylation biomarker of the differential methylation region is chr22:17849808-17850021, the methylation biomarker of the differential methylation region is chr7:27196138-27196564, A methylation biomarker whose differential methylation region is chr10:20105505-20105717, a methylation biomarker whose differential methylation region is chr6:31830364-31830826, a methylation biomarker whose differential methylation region is chr5:35925138-35925258, and a methylation biomarker whose differential methylation region is chr15:48010822-48010942; Or, the methylation biomarker combination is: Methylation biomarkers with differential methylation region of chr7:37488070-37488477, methylation biomarkers with differential methylation region of chr7:87229906-87230345, methylation biomarkers with differential methylation region of chr22:17849808-17850021, methylation biomarkers with differential methylation region of chr7:27196138-27196564, methylation biomarkers with differential methylation region of chr10:201055 05-20105717, a methylation biomarker of a differentially methylated region of chr6:31830364-31830826, a methylation biomarker of a differentially methylated region of chr5:35925138-35925258, a methylation biomarker of a differentially methylated region of chr15:48010822-48010942, and a methylation biomarker of a differentially methylated region of chr6:39693254-39693374; The methylation biomarkers were determined based on the human genome hg19 alignment.

2. The methylation biomarker combination for detecting gastric cancer according to claim 1, characterized in that: The gastric cancer is gastric cancer present in a subject, and the subject is a mammal; The gastric cancer is any one of stage I, stage II, stage III or stage IV gastric cancer.

3. The methylation biomarker combination for detecting gastric cancer according to claim 2, characterized in that: The mammal is a human.

4. The methylation biomarker combination for detecting gastric cancer according to any one of claims 1 to 3, characterized in that: The methylation level of the methylation biomarker combination is detected by one or more of the following methods: Fluorescence quantitative PCR, methylation-specific PCR, digital PCR, DNA methylation chip, targeted DNA methylation sequencing, methylation-sensitive restriction endonuclease method, direct sequencing method, methylation-sensitive single nucleotide primer extension, bisulfite-combined restriction endonuclease method, methylation-sensitive single-strand conformation analysis, methylation-sensitive denaturing gradient gel electrophoresis, methylation-specific denaturing high-performance liquid chromatography, methylation-specific microarray, methylation-sensitive melting curve analysis, methylation-sensitive spot analysis, methylation-specific multiple ligation-dependent probe amplification, bisulfite sequencing and pyrosequencing.

5. A kit for detecting gastric cancer, characterized in that: The kit comprises a reagent for detecting the methylation level of the methylation biomarker combination according to any one of claims 1 to 4.

6. The kit for detecting gastric cancer according to claim 5, characterized in that: The kit is tested using any of the following detection methods: Use fluorescent quantitative PCR, methylation-specific PCR, digital PCR, DNA methylation chip, targeted DNA methylation sequencing, whole gene methylation sequencing or DNA methylation mass spectrometry.

7. The kit for detecting gastric cancer according to claim 6, characterized in that: The kit uses a targeted DNA methylation sequencing method for detection.

8. Use of a reagent for determining the methylation level of the methylation biomarker combination for detecting gastric cancer according to any one of claims 1 to 4 in the preparation of a reagent or a kit for diagnosing gastric cancer.

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