Gastric cancer detection markers and application thereof

Abstract

The invention relates to application of a marker in diagnosis of gastric cancer, prediction of gastric cancer occurrence risk or determination of gastric cancer state. The invention discloses application of a reagent in preparation of a kit for diagnosing gastric cancer, predicting the risk of occurrence of gastric cancer or determining the state of gastric cancer in an individual. The invention also discloses a kit for diagnosing the gastric cancer, predicting the risk of occurrence of the gastric cancer or determining the state of the gastric cancer in an individual.

Description

Technical Field

[0001] The present application relates to the field of molecular biomedical technologies, and particularly to detection methods and kits based on gastric cancer markers. Specifically, the present application relates to the use of a reagent in the preparation of a kit for diagnosing gastric cancer, predicting the risk of gastric cancer occurrence, or determining the status of gastric cancer in an individual. The present application also relates to a kit for diagnosing gastric cancer, predicting the risk of gastric cancer occurrence, or determining the status of gastric cancer in an individual. Background Art

[0002] Gastric cancer is one of the most common malignant tumors globally. According to the data provided by the WHO, there are more than 1.5 million gastric cancer patients globally, and its mortality rate ranks third among global cancer deaths. Gastric cancer is mainly diagnosed by histological specimens taken by endoscopy, but the diagnostic technology has not significantly reduced the mortality rate of gastric cancer patients. Therefore, how to improve the survival rate of gastric cancer patients depends on the early diagnosis of gastric cancer, and screening and exploring valuable biological markers for early gastric cancer has become an urgent problem to be solved.

[0003] In recent years, the research on gastric cancer epigenetics has made rapid progress, especially DNA methylation. Studies have found that there are many specific tumor-related genes with varying degrees of methylation status changes at the early stage of gastric cancer occurrence, providing a new opportunity for the exploration of early gastric cancer diagnostic markers. Currently, there is a lack of effective molecular markers for gastric cancer in clinical practice.

[0004] Therefore, there is an urgent need to develop sensitive and specific detection methods and kits based on novel gastric cancer markers to improve the early detection rate of gastric cancer, improve the treatment effect of gastric cancer, and reduce the mortality rate of gastric cancer. Summary of the Invention

[0005] To solve the deficiencies of the prior art, the inventors screened a large number of markers and found that the markers (or targets) of the present invention can diagnose gastric cancer, predict the risk of gastric cancer occurrence, or determine the status of gastric cancer with high sensitivity and specificity. Based on the markers (or targets) of the present invention, cancer tissues and non-cancer tissues can be effectively distinguished.

[0006] More specifically, the present invention provides 15 specific markers (or targets), and establishes a diagnostic model for the relationship between a single marker or target, any combination of two markers or targets, and combinations of three or more markers or targets and gastric cancer. This model has the advantages of non-invasive detection, safe and convenient detection, high throughput, and high detection accuracy.

[0007] In one aspect, the present invention relates to the use of a reagent in the preparation of a kit for diagnosing gastric cancer, predicting the risk of gastric cancer development or determining the status of gastric cancer in an individual, characterized in that the reagent is used to detect the methylation level of at least one marker selected from the following in a sample isolated from the individual: SEPTIN9, SEPTIN9_2, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, FGF2, and any combination thereof.

[0008] In some embodiments, the reagent is a reagent selected from the following:

[0009] i) a substance that hybridizes with at least one target region of the marker or amplifies at least one target region of the marker, such as an oligonucleotide primer or probe, preferably, the oligonucleotide primer or probe is complementary to or identical to a fragment of at least 9 bases in length of at least one target region of the marker; and

[0010] ii) a bisulfite reagent or a methylation-sensitive restriction enzyme reagent, which differentiates between methylated and unmethylated dinucleotides within at least one target region of the marker, such as methylated and unmethylated CpG dinucleotides.

[0011] In some embodiments, the at least one marker is a combination of markers selected from the following:

[0012] i) DLX4 and PRDM14;

[0013] ii) DLX4, PRDM14, TJP2, HOXB3, and LOC645323;

[0014] iii) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, and HOXB6;

[0015] iv) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, and VWC2; or

[0016] v) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, VWC2, SEPTIN9, FGF2, ADCY1, and RNF180.

[0017] In some embodiments, the sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids, and combinations thereof; preferably, the sample is selected from gastric tissues, plasma, serum, whole blood, isolated blood cells, and combinations thereof.

[0018] In some embodiments, the reagent is used to detect the methylation level of at least one target region of the at least one biomarker, and the target region is selected from: region chr17:75369558-75369622, chr17:75369603-75369691, chr6:392282-392377, chr9:71736209-71870124, chr1:111217074-111217181, chr5:63461942-63462020, chr7:25896423-25896507, chr7:49813254-49813323, chr8:70982125-70982184, chr17:46673901-46674018, chr13:103046952-103047051, chr7:45613861-45613949, chr17:46671415-46671501, chr17:48042492-48042581, chr4:123748602-123748663, or their complementary sequences or processed sequences; or processed sequences of the complementary sequences; or any combination of the foregoing sequences and / or regions.

[0019] In some specific embodiments, the oligonucleotide primers are selected from any one or more of SEQ ID NO: 1-30. [[ID=~]]

[0020] In some specific embodiments, the oligonucleotide probes are selected from any one or more of SEQ ID NO: 33-47.

[0021] In another aspect, the present invention relates to a kit for diagnosing gastric cancer, predicting the risk of gastric cancer occurrence, or determining the status of gastric cancer in an individual, characterized in that the kit contains a reagent for detecting the methylation level of at least one target region of at least one biomarker selected from the following in a sample isolated from the individual: SEPTIN9, SEPTIN9_2, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, FGF2, and any combination thereof.

[0022] In some embodiments, the reagent is a reagent selected from the following:

[0023] i) A substance that hybridizes to at least one target region of the marker or amplifies at least one target region of the marker, such as an oligonucleotide primer or probe. Preferably, the oligonucleotide primer or probe is complementary to or identical to a fragment of at least 9 bases in length in at least one target region of the marker; and

[0024] ii) A bisulfite reagent or a methylation-sensitive restriction enzyme reagent that differentiates between methylated and unmethylated dinucleotides within at least one target region of the marker, such as methylated and unmethylated CpG dinucleotides.

[0025] In some embodiments, the at least one marker is a combination of markers selected from:

[0026] i) DLX4 and PRDM14;

[0027] ii) DLX4, PRDM14, TJP2, HOXB3, and LOC645323;

[0028] iii) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, and HOXB6;

[0029] iv) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, and VWC2; or

[0030] v) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, VWC2, SEPTIN9, FGF2, ADCY1, and RNF180.

[0031] In some embodiments, the sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids, and combinations thereof; preferably, the sample is selected from gastric tissue, plasma, serum, whole blood, isolated blood cells, and combinations thereof.

[0032] In some embodiments, the reagent is used to detect the methylation level of at least one target region of the at least one biomarker, and the target region is selected from: region chr17:75369558-75369622, chr17:75369603-75369691, chr6:392282-392377, chr9:71736209-71870124, chr1:111217074-111217181, chr5:63461942-63462020, chr7:25896423-25896507, chr7:49813254-49813323, chr8:70982125-70982184, chr17:46673901-46674018, chr13:103046952-103047051, chr7:45613861-45613949, chr17:46671415-46671501, chr17:48042492-48042581, chr4:123748602-123748663 or their complementary sequences or processed sequences; or processed sequences of the complementary sequences; or any combination of the foregoing sequences and / or regions.

[0033] In some specific embodiments, the oligonucleotide primers are selected from any one or more of SEQ ID NO: 1-30.

[0034] In some specific embodiments, the oligonucleotide probes are selected from any one or more of SEQ ID NO: 33-47.

[0035] In yet another aspect, the present invention relates to a method for diagnosing gastric cancer, predicting the risk of gastric cancer occurrence or determining the status of gastric cancer in an individual, and the method comprises the following steps:

[0036] (a) obtaining a biological sample containing DNA from the individual; and

[0037] (b) treating the DNA in the biological sample obtained in step (a) with a reagent that can distinguish methylated and unmethylated sites in the DNA, such as CpG sites, to obtain treated DNA;

[0038] (c) Optionally, pre-amplify at least one target region of at least one target marker in the processed DNA obtained from step (b) using a pool of pre-amplification primers, wherein at least one target region of each target marker is pre-amplified to obtain at least one pre-amplified product, and the at least one target marker comprises one or more markers selected from the group consisting of SEPTIN9, SEPTIN9_2, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, FGF2, and any combination thereof, and wherein the target region comprises at least one CpG dinucleotide sequence; and

[0039] (d) Detect the methylation template of at least one target region of at least one target marker in step (b) or step (c), wherein the at least one target marker comprises one or more markers selected from the group consisting of SEPTIN9, SEPTIN9_2, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, FGF2, and any combination thereof.

[0040] In some embodiments, the reagent is a bisulfite reagent or a methylation-sensitive restriction enzyme reagent, and the bisulfite reagent or the methylation-sensitive restriction enzyme reagent differentiates between methylated and unmethylated dinucleotides within at least one target region of the marker, such as methylated and unmethylated CpG dinucleotides.

[0041] In some embodiments, in step (c), amplification is carried out using a substance that amplifies at least one target region of the marker, such as an oligonucleotide primer. In some embodiments, the oligonucleotide primer is complementary or identical to a fragment of at least 9 bases in length of at least one target region of the marker. In some specific embodiments, the oligonucleotide primer is selected from any one or more of SEQ ID NO:1 - 30. In some specific embodiments, the oligonucleotide probe is selected from any one or more of SEQ ID NO:33 - 47. In some embodiments, in step (d), detection is carried out using a substance that hybridizes to at least one target region of the marker, such as a probe. In some embodiments, the probe is complementary or identical to a fragment of at least 9 bases in length of at least one target region of the marker.

[0042] In some embodiments, the at least one marker is a combination of markers selected from the following:

[0043] i) DLX4 and PRDM14;

[0044] ii) DLX4, PRDM14, TJP2, HOXB3, and LOC645323;

[0045] iii) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, and HOXB6;

[0046] iv) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, and VWC2; or

[0047] v) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, VWC2, SEPTIN9, FGF2, ADCY1, and RNF180.

[0048] In some embodiments, the sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids, and combinations thereof; preferably, the sample is selected from gastric tissues, plasma, serum, whole blood, isolated blood cells, and combinations thereof.

[0049] In some embodiments, the reagent is used to detect the methylation level of at least one target region of the at least one biomarker, and the target region is selected from: region chr17:75369558-75369622, chr17:75369603-75369691, chr6:392282-392377, chr9:71736209-71870124, chr1:111217074-111217181, chr5:63461942-63462020, chr7:25896423-25896507, chr7:49813254-49813323, chr8:70982125-70982184, chr17:46673901-46674018, chr13:103046952-103047051, chr7:45613861-45613949, chr17:46671415-46671501, chr17:48042492-48042581, chr4:123748602-123748663, or their complementary sequences or processed sequences; or processed sequences of the complementary sequences; or any combination of the foregoing sequences and / or regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1The ROC curves of 2 target points, 5 target points, 8 target points, 11 target points and 15 target points in the training set are shown. Detailed implementation mode

[0051] Reference is made to the example applications for illustration to describe several aspects of the present invention hereinafter. It should be understood that numerous specific details, relationships and methods are set forth to provide a thorough understanding of the present invention. However, those of ordinary skill in the relevant art will readily recognize that the present invention can be practiced without one or more of the specific details or can be practiced with other methods.

[0052] The present invention relates to the relationship between the methylation level of newly discovered markers and gastric cancer. The markers described herein provide methods for diagnosing gastric cancer or assessing the risk of gastric cancer in an individual. Thus, one embodiment of the present invention represents an improvement of the marker, which is suitable for diagnosing gastric cancer or assessing the risk of gastric cancer. In yet another embodiment, the newly discovered markers of the present invention can be used in combination with one or more other gastric cancer markers known in the art (such as CEA, CA125, CA199, CA724, CA242, etc.), for example, for diagnosing gastric cancer or assessing the risk of gastric cancer in an individual or for preparing a kit and / or microarray for this purpose.

[0053] The term "sample" means a material known or suspected of expressing or containing the markers described herein. Samples can be derived from biological sources ("biological samples"), such as tissues (e.g., biopsy samples), extracts or cell cultures including cells (e.g., tumor cells), cell lysates, and biological or physiological fluids, such as whole blood, plasma, serum, saliva, cerebrospinal fluid, sweat, urine, milk, peritoneal fluid, etc. Samples obtained from the source or samples after pretreatment to improve sample characteristics (such as preparing plasma from blood, etc.) can be used directly. In certain aspects of the present invention, the sample is a human physiological fluid, such as human plasma. In certain aspects of the present invention, the sample is a biopsy sample, such as tumor tissue or cells obtained by tissue examination.

[0054] In certain specific aspects of the present invention, the sample is plasma or gastric tissue.

[0055] Methods known in the art can be used to detectably label the target polynucleotide or a substance (such as an oligonucleotide primer or probe) hybridizing or amplifying with the target polynucleotide on one or more nucleotides. The detectable label can be, but is not limited to, a luminescent label, a fluorescent label, a bioluminescent label, a chemiluminescent label, a radioactive label, and a colorimetric label.

[0056] As used herein, the term "marker" refers to a target nucleic acid, gene region, or methylation site whose methylation level or the score of a computational model based on the methylation level (e.g., the AUC of the ROC curve in the case of using a machine learning model such as a logistic regression model) indicates gastric cancer diagnosis or high risk of gastric cancer. A gene shall be considered to include all of its transcriptional variants and all of its promoters and regulatory elements. As will be understood by those skilled in the art, certain genes are known to exhibit allelic variations or single nucleotide polymorphisms ("SNPs") among individuals. SNPs include insertions and deletions of simple repeat sequences of different lengths (e.g., dinucleotide and trinucleotide repeats). Accordingly, this application should be understood to extend to all forms of markers / genes resulting from any other mutations, polymorphisms, or allelic variations. Additionally, it should be understood that the term "marker" shall include both the sense strand sequence of the marker or gene and the antisense strand sequence of the marker or gene.

[0057] The term "marker" as used herein is broadly construed to include both 1) the original marker (in a particular methylation state) found in a biological sample or genomic DNA, and 2) its processed sequences (e.g., the corresponding region after bisulfite conversion or the corresponding region after MSRE treatment). The corresponding region after bisulfite conversion differs from the target marker in the genomic sequence in that one or more unmethylated cytosine residues are converted to uracil bases, thymine bases, or other bases that behave differently from cytosine in hybridization behavior. The corresponding region after MSRE treatment differs from the target marker in the genomic sequence in that the sequence is cleaved at one or more MSRE cleavage sites.

[0058] In the present invention, "methylation state" refers to the presence, absence, and / or amount of one or more methylated nucleobases in a nucleic acid molecule. For example, a nucleic acid molecule containing methylated cytosine is considered to be methylated, and at this time the methylation state of the nucleic acid molecule is methylated. A nucleic acid molecule that does not contain any methylated modified cytosine is considered to be unmethylated, and at this time the methylation state of the nucleic acid molecule is unmethylated. In some embodiments, a nucleic acid may be characterized as "unmethylated" if it is not methylated at a particular locus (e.g., the locus of a particular single CpG dinucleotide) or a particular combination of loci, even if it is methylated at other loci of the same gene or molecule.

[0059] Thus, the methylation state describes the state of methylation of a nucleic acid (e.g., a genomic sequence). Additionally, the methylation state refers to the methylation-related characteristics of a nucleic acid segment at a specific genomic locus. Such characteristics include, but are not limited to, whether any cytosine (C) residues within the DNA sequence are methylated, the positions of one or more methylated C residues, the frequency or percentage of methylated C throughout any specific region of the nucleic acid, and methylation allele differences due to, for example, differences in allele starting points. The "methylation state" refers to the relative concentration, absolute concentration, or pattern of methylated C or unmethylated C throughout any specific region of the nucleic acid in a biological sample. For example, if one or more cytosine (C) residues within a nucleic acid sequence are methylated, it may be referred to as "hypermethylated" or having "increased methylation", while if one or more cytosine (C) residues within a DNA sequence are unmethylated, it may be referred to as "demethylated" or having "decreased methylation". Similarly, if one or more cytosine (C) residues within a nucleic acid sequence are methylated compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.), that sequence is considered hypermethylated or having increased methylation compared to the other nucleic acid sequences. Or, if one or more cytosine (C) residues within a DNA sequence are unmethylated compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.), that sequence is considered demethylated or having decreased methylation compared to the other nucleic acid sequences.

[0060] In the present invention, the methylation level represents the proportion of one or more sites in a methylated state. The methylation level of a region (or a group of sites) is the mean of the methylation levels of all sites in that region (or all sites in the group). Thus, an increase or decrease in the methylation level of a region does not mean that the methylation levels of all methylated sites in the region have increased or decreased. Those skilled in the art are aware of the process of converting the results obtained by methods for detecting DNA methylation (e.g., reduced representation bisulfite sequencing, quantitative fluorescence PCR) into methylation levels.

[0061] As used herein, the "methylation level" includes the relationship between the methylation states of CpGs at any number and any position within the sequence involved. The relationship can be the addition or subtraction of methylation state parameters (e.g., 0 or 1) or the result of a mathematical algorithm calculation (e.g., mean, percentage, fraction, ratio, degree, or calculation using a mathematical model), including but not limited to methylation level metrics, methylation haplotype ratios, methylation haplotype loads, or in the case of using a machine learning model such as a logistic regression model, the AUC of the ROC curve.

[0062] Genes used as markers in the present invention are expected to include naturally occurring variants of said genes, their complementary sequences, all their promoters and regulatory elements (e.g., nucleic acid sequences within 5 kb (e.g., 4 kb, 3 kb, 2 kb, or 1 kb) upstream of the gene annotation start site and within 5 kb downstream of the gene annotation termination site), and fragments of said genes or said variants, particularly fragments that are detectable by molecular biology. In the present invention, the terms "fragment detectable by molecular biology", "target region", and "target gene region" can be used interchangeably. A fragment detectable by molecular biology preferably contains at least 16, 17, 18, 19, 20, 22, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, or more consecutive nucleotides of said marker. In some embodiments, said consecutive nucleotides contain at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, or more CpG dinucleotide sequences. In some embodiments, the target gene region is preferably rich in CpG dinucleotides.

[0063] In the present invention, the term "target region" or "target gene region" refers to any fragment detectable by molecular biology within the nucleic acid region constituted by the marker gene itself, 5 kb (e.g., 4 kb, 3 kb, 2 kb, or 1 kb) upstream of its gene annotation start site, and 5 kb (e.g., 4 kb, 3 kb, 2 kb, or 1 kb) downstream of its gene annotation termination site, or its complementary sequence or processed sequence (e.g., the corresponding sequence after bisulfite conversion or after MSRE treatment), or the processed sequence of said complementary sequence (e.g., the corresponding sequence after bisulfite conversion or after MSRE treatment). For example, the target gene region of the target marker in Table 1 below includes its Hg19 coordinates and any fragment detectable by molecular biology within 5 kb (e.g., 4 kb, 3 kb, 2 kb, or 1 kb) upstream and downstream of said coordinates, its complementary sequence or processed sequence (e.g., the corresponding sequence after bisulfite conversion or after MSRE treatment), and the processed sequence of said complementary sequence (e.g., the corresponding sequence after bisulfite conversion or after MSRE treatment). More preferably, the target gene region of the target marker in Table 1 below includes its Hg19 coordinates and any fragment detectable by molecular biology within 5 kb (e.g., 4 kb, 3 kb, 2 kb, or 1 kb) upstream of said coordinates, its complementary sequence or processed sequence (e.g., the corresponding sequence after bisulfite conversion or after MSRE treatment), and the processed sequence of said complementary sequence (e.g., the corresponding sequence after bisulfite conversion or after MSRE treatment).

[0064] In some embodiments, it is preferred to use and detect target markers selected from Table 1 below and their target regions or any combination thereof (according to Hg19 coordinates):

[0065] Table 1. Target Markers and Target Regions

[0066]

[0067]

[0068] SEPTIN9 and SEPTIN9_2 described herein are protein-coding genes, also known as septin 9, which is a member of the mitogen family and is involved in cell division and cell cycle regulation. This gene is a candidate for an ovarian tumor suppressor gene. Mutations in this gene can lead to hereditary neuropathic muscular atrophy, also known as neuritis with brachial plexus predominance. Chromosomal translocations involving this gene on chromosome 17 and the MLL gene on chromosome 11 can lead to acute myelomonocytic leukemia.

[0069] IRF4 described herein is a protein-coding gene, also known as interferon regulatory factor 4. The protein encoded by this gene belongs to the IRF (interferon regulatory factor) transcription factor family and is characterized by a unique tryptophan pentapeptide repeat DNA-binding domain. IRFs play important roles in regulating interferons in response to viral infections and in regulating interferon-induced genes.

[0070] TJP2 described herein is a protein-coding gene, also known as tight junction protein 2. The gene encodes a tight junction protein that is a member of the membrane-associated guanylate kinase homolog family. The encoded protein plays a role in the tight junction barrier of epithelial and endothelial cells and is necessary for the correct assembly of tight junctions.

[0071] KCNA3 described herein is a protein-coding gene, also known as potassium voltage-gated channel subfamily A member 3. This gene encodes a member of the potassium channel, voltage-gated, shaker-related subfamily. This member contains six transmembrane domains, and the fourth segment contains a shaker-type repeat. It belongs to the delayed rectifier class, and members of this class allow nerve cells to efficiently repolarize after an action potential. It plays an important role in T cell proliferation and activation.

[0072] RNF180 described in this article is a protein-coding gene, also known as ring finger protein 180. The protein encoded by this gene is predicted to have ubiquitin-conjugating enzyme binding activity and ubiquitin-protein ligase activity. It is predicted to be involved in the metabolism of norepinephrine; the positive regulation of protein ubiquitination in the process of protein degradation; and the metabolism of tryptophan. It is predicted to play a role in multiple processes, including adult behavior; the positive regulation of protein ubiquitination; and protein polyubiquitination. It is predicted to be located on the nuclear membrane. It is predicted to be an integral component of the membrane and an integral component of the endoplasmic reticulum membrane.

[0073] LOC645323 described in this article belongs to an intergenic region and is located on chromosome 7.

[0074] VWC2 described in this article is a protein-coding gene, also known as von Willebrand factor C domain-containing 2. This gene encodes a secreted bone morphogenetic protein antagonist. The encoded protein may be involved in nerve function and development and may play a role in cell adhesion.

[0075] PRDM14 described in this article is a protein-coding gene, also known as PR / SET domain 14. This gene encodes a protein that is a member of the PRDI-BF1 and RIZ homologous domain-containing members of the transcriptional regulator family. The encoded protein may have histone methyltransferase activity and play a key role in cell pluripotency by inhibiting the expression of differentiation marker genes.

[0076] HOXB3 described in this article is a protein-coding gene, also known as homeobox B3. This gene is a member of the Antp homeobox family and encodes a nuclear protein with a homeobox DNA-binding domain. It is included in a cluster of homeobox B genes located on chromosome 17. The encoded protein is a transcription factor with a specific sequence and is involved in the developmental process. The increased expression of this gene is associated with a specific biological subtype of acute myeloid leukemia.

[0077] FGF14 described in this article is a protein-coding gene, also known as fibroblast growth factor 14. The protein encoded by this gene is a member of the fibroblast growth factor (FGF) family. Members of the FGF family have a wide range of mitogenic and cell survival activities and are involved in multiple biological processes, including embryonic development, cell growth, morphogenesis, tissue repair, tumor growth, and invasion.

[0078] ADCY1 described herein is a protein-coding gene, also known as adenylate cyclase 1. The gene encodes a member of the adenylate cyclase gene family and is mainly expressed in the brain. The protein is regulated by the calcium / calmodulin concentration and may be involved in brain development. Multiple transcript variants are produced by alternative splicing.

[0079] HOXB6 described herein is a protein-coding gene, also known as homeobox B6. The gene is a member of the Antp homeobox family and encodes a protein with a homeobox DNA-binding domain. It is included in a cluster of homeobox B genes located on chromosome 17. The encoded protein is a transcription factor with a specific sequence and is involved in development, including the development of the lung and skin, and has been localized in the nucleus and cytoplasm. Alterations in the expression of this gene or changes in the subcellular localization of its protein are associated with some cases of acute myeloid leukemia and colorectal cancer.

[0080] DLX4 described herein is a protein-coding gene, also known as distal-less homeobox 4. It is speculated that DLX proteins play a role in forebrain and craniofacial development.

[0081] FGF2 described herein is a protein-coding gene, also known as fibroblast growth factor2. The protein encoded by this gene is a member of the fibroblast growth factor (FGF) family. Members of the FGF family can bind to heparin and have a wide range of mitogenic and angiogenic activities. This protein has been implicated in various biological processes, such as limb and nervous system development, wound healing, and tumor growth.

[0082] In some embodiments, the at least one marker includes SEPTIN9 and any one or more selected from SEPTIN9_2, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0083] In some embodiments, the at least one marker includes SEPTIN9_2 and any one or more selected from SEPTIN9, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0084] In some embodiments, the at least one marker includes IRF4 and any one or more selected from SEPTIN9_2, SEPTIN9, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0085] In some embodiments, the at least one marker includes TJP2 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0086] In some embodiments, the at least one marker includes KCNA3 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0087] In some embodiments, the at least one marker includes RNF180 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0088] In some embodiments, the at least one marker includes LOC645323 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, RNF180, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0089] In some embodiments, the at least one marker includes VWC2 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, RNF180, LOC645323, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0090] In some embodiments, the at least one marker comprises PRDM14 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, HOXB3, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0091] In some embodiments, the at least one marker comprises HOXB3 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, FGF14, ADCY1, HOXB6, DLX4, and FGF2.

[0092] In some embodiments, the at least one marker comprises FGF14 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, ADCY1, HOXB6, DLX4, and FGF2.

[0093] In some embodiments, the at least one marker comprises ADCY1 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, HOXB6, DLX4, and FGF2.

[0094] In some embodiments, the at least one marker comprises HOXB6 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, DLX4, and FGF2.

[0095] In some embodiments, the at least one marker comprises DLX4 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, and FGF2.

[0096] In some embodiments, the at least one biomarker comprises FGRF2 and any one or more selected from SEPTIN9_2, SEPTIN9, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, and DLX4.

[0097] In some embodiments, it is preferred to use and detect a combination of two or more of the target biomarkers and their target regions in Table 1. In some embodiments, it is preferred to use and detect the following combinations of the target biomarkers and their target regions in Table 1:

[0098] i) DLX4 and PRDM14;

[0099] ii) DLX4, PRDM14, TJP2, HOXB3, and LOC645323;

[0100] iii) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, and HOXB6;

[0101] iv) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, and VWC2; or

[0102] v) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, VWC2, SEPTIN9, FGF2, ADCY1, and RNF180.

[0103] In some embodiments, using the target biomarker and its target gene region of the present invention or a combination thereof can achieve a sensitivity of at least 25%, such as at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 81%, at least 82%, or at least 83% with a specificity greater than 80%, such as greater than 85% or greater than 90%.

[0104] The terms "subject", "patient", and "individual" are used interchangeably herein and refer to warm-blooded animals, such as mammals. The term includes, but is not limited to, domestic animals, rodents (such as rats and mice), primates, and humans. Preferably, the term refers to humans.

[0105] The term "methylation assay" refers to any assay for determining the methylation status of one or more dinucleotide (e.g., CpG) sequences within a DNA sequence.

[0106] In this article, the term "threshold" should be understood according to the general understanding of those skilled in the art, and represents any useful reference for reflecting the DNA methylation level.

[0107] In one or more embodiments, when compared with a reference level, the methylation level (e.g., Ct value) of a target marker increases or decreases. When the methylation marker level (e.g., Ct value) meets a certain threshold, it is identified as having gastric cancer, being at risk of developing gastric cancer, or having progression of gastric cancer. Exemplarily, the cut-off values (thresholds) of the Ct values in the plasma of each target described herein are as follows: SEPTIN9 is 44.23, SEPTIN9_2 is 44.50, IRF4 is 28.53, TJP2 is 27.68, KCNA3 is 44.50, RNF180 is 30.74, LOC645323 is 26.11, VWC2 is 24.03, PRDM14 is 25.45, HOXB3 is 25.40, FGF14 is 44.53, ADCY1 is 29.56, HOXB6 is 44.93, DLX4 is 25.97, FGF2 is 26.87. A target Ct value lower than the corresponding cut-off value indicates having gastric cancer, being at risk of developing gastric cancer, or having progression of gastric cancer, and a target Ct value higher than the corresponding cut-off value indicates not having gastric cancer, having a low risk of developing gastric cancer, or remission of gastric cancer.

[0108] Those skilled in the art are aware of the methods of conventional mathematical analysis and the process of determining thresholds, such as machine learning, which reflects the stability of the model by fitting the model in the training set and evaluating the model in the test set. Such as differential analysis, which uses a non-parametric test based on rank sum to test the methylation signal differences between the gastric cancer group and the non-gastric cancer group. Such as Lasso regression, which uses the regression coefficient of the Lasso regression penalty site to achieve feature selection of important sites. An exemplary method is the binary logistic regression mathematical model. For example, for differentially methylated markers, a binary logistic regression is constructed for the training set samples, and the accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC) of the model statistical detection results are used to statistically calculate the prediction scores of the test set samples. The method of constructing a binary logistic regression mathematical model is common knowledge in the art. The logistic regression mathematical model of the combination of n targets is as follows:

[0109] Logit = a0 + a1*Ct1 + a2*Ct2……a n *Ct n

[0110] When n targets are combined, based on the Ct value of each target and the Logistic regression formula, a Logit value can be obtained. Taking the CT value and Logit value corresponding to the Youden index of a single target and the combination of multiple targets in the training set as the cut-off value, the presence of gastric tumors can be confirmed and the risk of gastric tumor formation can be evaluated.

[0111] Any combination of the targets described in this article combined with a binary logistic regression mathematical model can confirm the presence of gastric tumors and evaluate the risk of gastric tumor formation. As shown in Tables 5 and 6, the AUCs are all higher than those of single targets. The regression models and cut-off values of some exemplary combined targets are shown in Table 7. A regression result with a Logit value higher than the corresponding cut-off value indicates the presence of gastric cancer, the risk of developing gastric cancer, or the progression of gastric cancer, while a Logit value lower than the corresponding cut-off value indicates the absence of gastric cancer, a low risk of developing gastric cancer, or the remission of gastric cancer.

[0112] The logistic regression model for the DLX4 and PRDM14 targets is: Logit = 3.822812 + (-0.058743)*CtT14 + (-0.048108)*CtT9, and the cut-off value is 0.888.

[0113] The logistic regression model for the DLX4, PRDM14, TJP2, HOXB3, and LOC645323 targets is: Logit = 7.412310 + (-0.041919)*CtT14 + (-0.023873)*CtT9 + (-0.064603)*CtT4 + (-0.037648)*CtT10 + (-0.023907)*CtT7, and the cut-off value is 0.418.

[0114] The logistic regression model for the DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, and HOXB6 targets is: Logit = 13.176031 + (-0.029628)*CtT14 + (-0.017306)*CtT9 + (-0.047626)*CtT4 + (-0.035422)*CtT10 + (-0.018088)*CtT7 + (-0.033752)*CtT11 + (-0.074550)*CtT5 + (-0.048253)*CtT13, and the cut-off value is 0.255.

[0115] The logistic regression model for DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, and VWC2 targets is: Logit = 14.355564 + (-0.032103)*CtT14 + (-0.021949)*CtT9 + (-0.048843)*CtT4 + (-0.043056)*CtT10 + (-0.021502)*CtT7 + (-0.033408)*CtT11 + (-0.073693)*CtT5 + (-0.046266)*CtT13 + (0.007920)*CtT3 + (-0.045817)*CtT2 + (0.041211)*CtT8, and the cut-off value is 0.265.

[0116] The logistic regression model for DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, VWC2, SEPTIN9, FGF2, ADCY1, and RNF180 targets is: Logit = 16.764824 + (-0.027859)*CtT14 + (-0.022618)*CtT9 + (-0.052860)*CtT4 + (-0.045713)*CtT10 + (-0.023478)*CtT7 + (-0.035244)*CtT11 + (-0.065107)*CtT5 + (-0.046192)*CtT13 + (0.009818)*CtT3 + (-0.039743)*CtT2 + (0.011837)*CtT8 + (-0.094328)*CtT1 + (0.017130)*CtT15 + (0.015845)*CtT12 + (0.025051)*Ct T6, and the cut-off value is 0.04.

[0117] The term "oligonucleotide" refers to a polymeric form of nucleotides of any length, which can be ribonucleotides or deoxyribonucleotides. This term includes double-stranded and single-stranded DNA and RNA, such as modified and unmodified forms like methylation or capping of polynucleotides. The terms "polynucleotide" and "oligonucleotide" are used interchangeably herein. An oligonucleotide may but need not include other coding or non-coding sequences, or it may but not necessarily be linked to other molecules and / or vectors or support materials. The oligonucleotides used in the methods or kits of the present invention can have any length suitable for the specific method. In certain applications, this term refers to antisense nucleic acid molecules (e.g., mRNA or DNA strands in the direction opposite to the sense polynucleotide encoding the biomarker of the present invention).

[0118] The oligonucleotides useful in the present invention include complementary nucleic acid sequences and nucleic acids that are substantially identical to these sequences, and also include sequences that differ from the nucleic acid sequences due to the degeneracy of the genetic code. The oligonucleotides useful in the present invention also include nucleic acids that hybridize to the oligonucleotide cancer marker nucleic acid sequences under stringent conditions, preferably high stringency conditions.

[0119] As used herein, "primer" generally refers to a linear oligonucleotide that is complementary to and anneals to a target sequence. The lower limit of primer length is determined by hybridization ability, since very short primers (e.g., less than 5 nucleotides) do not form thermodynamically stable duplexes under most hybridization conditions. Primer lengths generally vary within the range of 8 - 50 nucleotides. In certain embodiments, the primer is between about 15 - 25 nucleotides. Naturally occurring nucleotides (especially guanine, adenine, cytosine, and thymine, hereinafter referred to as "G", "A", "C", and "T") as well as nucleotide analogs can be used for the primers of the present invention.

[0120] As used herein, the term "nucleotide analog" refers to a compound that is structurally similar to a naturally occurring nucleotide. Nucleotide analogs can have an altered phosphate backbone, sugar moiety, nucleobase, or combinations thereof. Nucleotide analogs that typically have an altered nucleobase especially confer different base pairing and base stacking properties. Nucleotide analogs having an altered phospho - sugar backbone (e.g., peptide nucleic acid (PNA), locked nucleic acid (LNA)) generally especially alter strand properties, such as secondary structure formation.

[0121] Exemplary primers and probes used in the present invention are shown in Tables 2 and 3, and the target gene regions they target are shown in Table 1.

[0122] The nucleotide sequences of the primers and probes of the present invention also include their modified forms, provided that the amplification or detection effect of the primers is not significantly affected. The modifications can be, for example, adding one or more nucleotide residues in or at both ends of the nucleotide sequence, deleting one or more nucleotide residues in the nucleotide sequence, or replacing one or more nucleotide residues in the sequence with other nucleotide residues, such as replacing A with T, replacing C with G, etc. Those skilled in the art will appreciate that the modified forms of the primers are also covered by the present invention, especially within the scope of the claims. In one embodiment, the modified form of the nucleotide sequence of the primer is a chemically enhanced primer as disclosed in CN103270174A.

[0123] Each nucleotide in the primers of the present invention can be chemically synthesized using, for example, a general DNA synthesizer (e.g., Model 394 manufactured by Applied Biosystems). Any other method well known in the art can also be used to synthesize oligonucleotides.

[0124] Using the DNA extracted from a sample as a template, and using PCR primers to perform an amplification reaction on a target marker to obtain an amplification product. The amplification reaction includes, but is not limited to, polymerase chain reaction (PCR), ligase chain reaction (LCP), self-sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA), strand displacement amplification (SDA), multiple displacement amplification (MDA), and rolling circle amplification (RCA), which are disclosed in the following references (incorporated herein by reference): Mullis et al., U.S. Patent No. 4,683,195; No. 4,965,188; No. 4,683,202; No. 4,800,159 (PCR); Gelfand et al., U.S. Patent No. 5,210,015 (real-time PCR using "Taqman" or "Taq" [registered trademark] probes); Wittwer et al., U.S. Patent No. 6,174,670; Kacian et al., U.S. Patent No. 5,399,491 ("NASBA"); Lizardi, U.S. Patent No. 5,854,033; Aono et al., Japanese Patent Publication No. JP 4-262799 (rolling circle amplification); and the like.

[0125] Preferably, the PCR method is used to amplify the target marker. The PCR method itself is well known in the art. The term "PCR" includes derivative forms of the reaction, which include, but are not limited to, reverse transcription PCR, real-time PCR, nested PCR, multiplex PCR, and fluorescence quantitative PCR, etc. Preferably, the fluorescence quantitative PCR method is used to quantitatively amplify the target nucleotide.

[0126] In the presence of primers, template DNA, and a thermostable DNA polymerase, PCR is carried out by repeating the cycle of denaturation, annealing, and extension steps approximately 30 to 60 times (e.g., 50 times) using a primer hybridizing to the sense strand (reverse primer) and a primer hybridizing to the antisense strand (forward primer). In one embodiment, the PCR is fluorescence quantitative PCR. In one embodiment, the PCR uses the primers shown in Table 2. Those skilled in the art can understand that other PCR methods and primers can also be used as long as the target fragment can be amplified.

[0127] In the PCR of the present invention, various conventional thermostable DNA polymerases can be used for amplification, including but not limited to FastStart Taq DNA polymerase (Roche), Ex Taq (registered trademark, Takara), Z-Taq, AccuPrime TaqDNA polymerase, and HotStarTaq Plus DNA polymerase.

[0128] Methods for selecting appropriate PCR reaction conditions based on the Tm value of primers are well known in the art, and those of ordinary skill in the art can select the optimal conditions according to primer length, GC content, target specificity and sensitivity, the nature of the polymerase used, etc. For example, the following conditions can be used for quantitative fluorescence PCR reaction: 95°C for 5 minutes; 95°C for 15 seconds, 56°C for 40 seconds, for 50 cycles. The reaction system is 25 μL.

[0129] Reagents that can be used to detect the methylation level of the target markers of the present invention are well known in the art. Such reagents suitable for the present invention, such as bisulfite reagents or methylation-sensitive restriction enzymes, are commercially available or can be routinely prepared by methods well known to those skilled in the art.

[0130] The term "bisulfite reagent" refers to bisulfite used to distinguish methylated and unmethylated CpG dinucleotide sequences.

[0131] The term "methylation-sensitive restriction enzyme" should be understood as an enzyme that selectively digests nucleic acids according to the methylation status of its recognition site. For restriction enzymes that specifically cleave only when the recognition site is unmethylated or hemimethylated, no cleavage occurs, or cleavage occurs with a significantly reduced efficiency when the recognition site is methylated. For restriction enzymes that specifically cleave only when the recognition site is methylated, no cleavage occurs, or cleavage occurs with a significantly reduced efficiency when the recognition site is unmethylated. Preferred are the following methylation-sensitive restriction enzymes whose recognition sequences contain CG dinucleotides (such as cgcg or cccggg). In some embodiments, more preferably, the restriction enzyme that does not cleave when the cytosine in the dinucleotide is methylated at the C5 carbon atom.

[0132] The kit of the present invention can be prepared by conventional methods in the art. The kit may contain materials or reagents for implementing the methods of the present invention (including reagents for detecting each target marker). The kit may include storage reaction reagents (such as primers, dNTPs, enzymes, etc. in suitable containers) and / or supporting materials (such as buffers, instructions for performing the detection, etc.). For example, the kit may include one or more containers (such as boxes) containing the corresponding reaction reagents and / or supporting materials. Such contents can be delivered to a given recipient together or separately. As an example, the kit may contain reagents for detecting each target marker, buffers, and instructions for use. The kit may also contain polymerase and dTNP, etc. The kit may also contain internal standards, positive and negative controls, etc. for quality control. The kit may also contain reagents for preparing nucleic acids such as DNA from samples. The above examples should not be construed as limiting the kits and their contents applicable to the present invention.

[0133] A microarray refers to a solid-phase support having a flat surface, which has a nucleic acid array. Each member in the array contains the same copy of an oligonucleotide or polynucleotide immobilized on a spatially defined region or site, and the regions or sites do not overlap with the regions or sites of other members in the array; that is, the regions or sites are discrete in space. In addition, the spatially defined hybridization sites can be "addressable" because their positions and the identities of the immobilized oligonucleotides are known or predetermined (e.g., known or predetermined before its use). Usually, the oligonucleotide or polynucleotide is single-stranded and is usually covalently linked to the solid-phase support at the 5'-end or 3'-end. The density of nucleic acids containing non-overlapping regions in the microarray is usually greater than 100 / cm 2 , more preferably greater than 1000 / cm 2 . The microarray technology is disclosed in, for example, the following references: Microarrays: A Practical Approach edited by Schena (IRL Press, Oxford, 2000); Southern, Current Opin. Chem. Biol., 2: 404-410, 1998, the entire contents of which are incorporated herein by reference.

[0134] The present invention discloses the use of markers in the diagnosis of gastric cancer and prediction of its risk. Those skilled in the art can draw on the content of this article and appropriately improve the process parameters to achieve it. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and are all considered to be included in the present invention. The uses described in the present invention have been described through preferred embodiments. Relevant personnel can obviously make changes or appropriate alterations and combinations to the uses described herein without departing from the content, spirit and scope of the present invention to implement and apply the technology of the present invention.

[0135] Examples

[0136] For a clearer understanding of the content of the present invention, it will be described in detail in conjunction with the drawings and examples.

[0137] Example 1: Comparing the methylation abundances of DNA samples from gastric cancer, adjacent tissues and white membrane layers

[0138] DNA samples were obtained from the white membrane layers of healthy individuals without gastric abnormalities, cancer tissues and adjacent tissues of gastric cancer patients (19 white membrane layer samples, 17 cancer tissue samples, and 17 adjacent tissue samples). The white membrane layer DNA was selected as the reference sample because most of the plasma-free DNA is derived from the DNA released after the rupture of white membrane layer cells, and its background can be a basic background signal for this detection site of plasma-free DNA.

[0139] According to the requirements of the instruction manual, Qiagen QIAamp DNA Mini Kit was used to extract DNA from the buffy coat, and Qiagen QIAamp DNA FFPE Tissue Kit was used to extract tissue DNA.

[0140] Take 20 ng of the DNA sample obtained in the above steps and treat it with bisulfite reagent (D5031, ZYMO RESEARCH) to obtain the converted DNA. The primer sequences of each methylation marker and the internal reference are shown in Table 2.

[0141] Perform pre-amplification PCR reaction. In the reaction system of pre-amplification PCR, the final concentration of each primer is 200 nM. The PCR reaction system contains 10 μL of converted DNA, 2.5 μL of primer pool containing all detection sites, and 12.5 μL of KAPA2G Fast Multiplex Mix (KAPA Biosystems, KK5802). The PCR reaction conditions are as follows: 95°C for 3 minutes; 95°C for 30 seconds, 56°C for 60 seconds, for 40 cycles. Amplification was carried out using an Applied Biosystems ProFlex PCR instrument.

[0142] Through fluorescence PCR detection, the Ct value of the fluorescence detected by the marker was obtained. In the fluorescence PCR reaction system, the final concentration of each primer is 500 nM, and the final concentration of each detection probe is 200 nM. The PCR reaction system contains: 10 μL of pre-amplified dilution product, 2.5 μL of primer and probe premix containing detection sites; 12.5 μL of PCR reagent ( UniversalProbe qPCR Master Mix (NEB). The primer sequences of each methylation marker and the internal reference are shown in Table 2, and the probe sequences are shown in Table 3. The PCR reaction conditions are as follows: 95°C for 5 minutes; 95°C for 30 seconds, 56°C for 60 seconds (collect fluorescence), for 50 cycles. Different fluorescences were detected in the corresponding fluorescence channels using an ABI 7500 Real-Time PCR System. Calculate and compare the Ct values of the samples obtained from the buffy coat, adjacent cancer tissues, and cancer tissues. The Ct value of the target point where no amplification signal was detected was set to 50.

[0143] Table 2: Detection primer sequences

[0144]

[0145]

[0146] Table 3: Detection probe sequences

[0147]

[0148]

[0149] Table 4: Summary of Sample Detection Results

[0150]

[0151] The results shown in Table 4 above indicate that the average Ct value detected in cancer tissues is small, representing a stronger methylation signal. The detection rate of methylation signals in cancer tissues is much higher than that in the tunica albuginea, which also represents a strong methylation signal in cancer tissues. Most samples in the tunica albuginea cannot detect the target methylation signal. Therefore, these targets all have the potential for blood detection of gastric cancer, demonstrating the feasibility and specificity of the selected target markers for tumor tissues.

[0152] Example 2: Comparing Methylation Signals in Plasma Samples of Gastric Cancer Patients and Gastric Control Populations

[0153] Plasma from 276 non-gastric cancer individuals (as controls) and preoperative plasma from 262 gastric cancer patients (the proportions of stages I-IV are 24.8%, 21%, 34.4%, 18.3% in turn, and also include 1.5% of gastric cancer patients with unclear stages) were selected as the training set, and methylation detection was performed on these samples.

[0154] The methods for extracting plasma DNA, bisulfite conversion, pre-amplification, and PCR reaction were the same as those in Example 1.

[0155] The area under the receiver operating characteristic curve (AUC) of the detection sites is shown in Table 5 below:

[0156] Table 5: Area Under the Receiver Operating Characteristic Curve of Detection Sites

[0157] Target Number Target AUC T1 SEPTIN9 0.626 T2 SEPTIN9_2 0.645 T3 IRF4 0.647 T4 TJP2 0.717 T5 KCNA3 0.66 T6 RNF180 0.527 T7 LOC645323 0.671 T8 VWC2 0.629 T9 PRDM14 0.721 T10 HOXB3 0.676 T11 FGF14 0.668 T12 ADCY1 0.581 T13 HOXB6 0.658 T14 DLX4 0.748 T15 FGF2 0.596

[0158] The above results show that the above target markers have high discrimination for blood samples of gastric cancer patients.

[0159] The above targets were sorted according to the AUC size, and the two targets with the largest AUC were combined using binary logistic regression. Then, 3 targets were added each time from the largest to the smallest AUC for 5-target combination, 8-target combination, 11-target combination, and 15-target combination. The AUC of the combined logistic regression is shown in Table 6 and Figure 1 as shown.

[0160] Table 6: Area Under the Receiver Operating Characteristic Curve of Target Combinations

[0161]

[0162]

[0163] The results in the above table show that the AUC of the combined detection of target points is greater than that of a single target point, proving that the combination of target points can increase the discrimination of the target marker for blood samples of gastric cancer patients.

[0164] For each combination, based on the Ct value of each target point and the Logistic regression formula, the Logit value can be obtained. Using the CT values and Logit values corresponding to the Youden index of a single target point and the combined multi-target points in the training set as the cut-off values, the presence of gastric tumors can be confirmed and the risk of gastric tumor formation can be evaluated. If the CT value of a single target point is less than the cut-off value, it is considered that gastric tumors may exist or the risk of gastric tumor formation is high. If the CT value of each combination is greater than the cut-off value, it is considered that gastric tumors may exist or the risk of gastric tumor formation is high. The single target points and different combined combinations are shown in Table 7, and the corresponding performance in the training set is shown in Table 8.

[0165] Table 7: Cut-off values for single target points and combined multi-target points

[0166]

[0167]

[0168] Table 8: Performance in the training set

[0169] Sensitivity Specificity T1 26.2％ 99.0％ T2 33.7％ 94.4％ T3 34.5％ 90.2％ T4 46.0％ 90.2％ T5 32.9％ 99.0％ T6 17.5％ 90.2％ T7 32.5％ 90.2％ T8 25.8％ 90.2％ T9 37.3％ 90.2％ T10 26.2％ 90.2％ T11 41.7％ 90.6％ T12 23.8％ 90.2％ T13 34.1％ 96.9％ T14 48.4％ 90.2％ T15 28.2％ 90.2％ 2 Targets 49.6％ 90.2％ 5 Targets 57.5％ 90.2％ 8 Targets 59.5％ 90.2％ 11 Targets 63.1％ 90.2％ 15 Targets 67.9％ 90.2％

[0170] Example 3: Comparing the verification performance in plasma samples of gastric cancer patients and gastric control populations

[0171] Select the plasma of 120 non-gastric cancer individuals (as controls) and the preoperative plasma of 110 gastric cancer patients (the proportions of stages I-IV are: 27.3%, 18.2%, 30.9%, 20.9% in turn, and also include 2.7% of gastric cancer patients with unclear stages) as the verification set, and perform methylation detection on these samples.

[0172] The method for extracting plasma DNA, bisulfite conversion, pre-amplification, and PCR reaction are the same as those in Example 1.

[0173] Detect the area under the receiver operating characteristic curve of each site, and the results are shown in Table 9 below.

[0174] Table 9: Area under the receiver operating characteristic curve of the detected sites in the verification set

[0175] Target Number Target AUC T1 SEPTIN9 0.617 T2 SEPTIN9_2 0.631 T3 IRF4 0.644 T4 TJP2 0.727 T5 KCNA3 0.643 T6 RNF180 0.547 T7 LOC645323 0.706 T8 VWC2 0.699 T9 PRDM14 0.756 T10 HOXB3 0.658 T11 FGF14 0.684 T12 ADCY1 0.602 T13 HOXB6 0.65 T14 DLX4 0.667 T15 FGF2 0.688

[0176] Using the logistic regression model of the multi-target combination in Example 2, predict the population in the verification set. The ROC curve in the verification set (see Table 10) further confirms that the combination of target points can increase the discrimination of the target marker for blood samples of gastric cancer patients.

[0177] Evaluate the risk of gastric tumor formation in the validation set according to the regression formula and cut-off value in the training set. The corresponding performance of single-target and different combinations in the test set is shown in Table 11.

[0178] Table 10: Area under the receiver operating characteristic curve for target combinations in the validation set

[0179]

[0180]

[0181] Table 11: Performance in the test set

[0182] Sensitivity Specificity T1 23.3％ 100.0％ T2 33.3％ 90.8％ T3 31.7％ 93.6％ T4 46.7％ 97.2％ T5 29.2％ 99.1％ T6 20.0％ 88.1％ T7 35.0％ 93.6％ T8 25.0％ 94.5％ T9 33.3％ 93.6％ T10 24.2％ 86.2％ T11 43.3％ 91.7％ T12 23.3％ 89.0％ T13 30.0％ 100.0％ T14 40.8％ 88.1％ T15 40.0％ 90.8％ 2 Targets 47.2％ 90.4％ 5 Targets 54.2％ 92.7％ 8 Targets 58.3％ 93.6％ 11 Targets 60.8％ 89.0％ 15 Targets 61.2％ 89.0％

[0183] This application has screened out 15 methylation markers for gastric cancer. The machine learning diagnostic model constructed based on the methylation levels of these methylation markers can better distinguish gastric cancer from healthy people, which is of great significance for the early screening of gastric cancer.

[0184] The foregoing detailed description is provided by way of explanation and example and is not intended to limit the scope of the appended claims. Various changes to the embodiments recited in the present application are obvious to those of ordinary skill in the art and are within the scope of the appended claims and their equivalents.

Claims

1. Use of a reagent in the preparation of a kit for diagnosing gastric cancer, predicting the risk of gastric cancer development or determining the status of gastric cancer in an individual, characterized in that The reagent is used to detect the methylation level of at least one biomarker selected from the following in a sample isolated from the individual: SEPTIN9, SEPTIN9_2, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, FGF2, and any combination thereof.

2. The use according to claim 1, wherein The reagent is a reagent selected from the following: i) a substance that hybridizes to at least one target region of the biomarker or amplifies at least one target region of the biomarker, such as an oligonucleotide primer or probe, preferably, the oligonucleotide primer or probe is complementary to or identical to a fragment at least 9 bases long in at least one target region of the biomarker; and ii) a bisulfite reagent or a methylation-sensitive restriction enzyme reagent, which distinguishes methylated and unmethylated dinucleotides, such as methylated and unmethylated CpG dinucleotides, within at least one target region of the biomarker.

3. The use according to claim 1, wherein The at least one biomarker is a combination of biomarkers selected from the following: i) DLX4 and PRDM14; ii) DLX4, PRDM14, TJP2, HOXB3, and LOC645323; iii) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, and HOXB6; iv) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, and VWC2; or v) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, VWC2, SEPTIN9, FGF2, ADCY1, and RNF180.

4. The use according to any one of claims 1 to 3, characterized in that The sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids, and combinations thereof; preferably, the sample is selected from gastric tissues, plasma, serum, whole blood, isolated blood cells, and combinations thereof.

5. The use according to any one of claims 1 to 3, characterized in that The reagent is used to detect the methylation level of at least one target region of the at least one biomarker, and the target regions are selected from: region chr17:75369558-75369622, chr17:75369603-75369691, chr6:392282-392377, chr9:71736209-71870124, chr1:111217074-111217181, chr5:63461942-63462020, chr7:25896423-25896507, chr7:49813254-49813323, chr8:70982125-70982184, chr17:46673901-46674018, chr13:103046952-103047051, chr7:45613861-45613949, chr17:46671415-46671501, chr17:48042492-48042581, chr4:123748602-123748663 or their complementary sequences or processed sequences; or processed sequences of the complementary sequences; or any combination of the foregoing sequences and / or regions.

6. A kit for diagnosing gastric cancer, predicting the risk of gastric cancer occurrence or determining the status of gastric cancer in an individual, characterized in that The kit contains a reagent for detecting the methylation level of at least one target region of at least one biomarker selected from the following in a sample isolated from the individual: SEPTIN9, SEPTIN9_2, IRF4, TJP2, KCNA3, RNF180, LOC645323, VWC2, PRDM14, HOXB3, FGF14, ADCY1, HOXB6, DLX4, FGF2 and any combination thereof.

7. The kit according to claim 6, characterized in that The reagent is a reagent selected from the following: i) A substance that hybridizes with at least one target region of the biomarker or amplifies at least one target region of the biomarker, such as an oligonucleotide primer or probe. Preferably, the oligonucleotide primer or probe is complementary to or identical to a fragment of at least 9 bases in length of at least one target region of the biomarker; and ii) A bisulfite reagent or a methylation-sensitive restriction enzyme reagent, which can distinguish methylated and unmethylated dinucleotides within at least one target region of the biomarker, such as methylated and unmethylated CpG dinucleotides.

8. The kit according to claim 6, wherein The at least one biomarker is a biomarker combination selected from the following: i) DLX4 and PRDM14; ii) DLX4, PRDM14, TJP2, HOXB3 and LOC645323; iii) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3 and HOXB6; iv) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, and VWC2; or v) DLX4, PRDM14, TJP2, HOXB3, LOC645323, FGF14, KCNA3, HOXB6, IRF4, SEPTIN9_2, VWC2, SEPTIN9, FGF2, ADCY1, and RNF180.

9. The kit according to any one of claims 6 - 8, characterized in that The sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids, and combinations thereof; preferably, the sample is selected from gastric tissues, plasma, serum, whole blood, isolated blood cells, and combinations thereof.

10. The kit according to any one of claims 6 - 8, characterized in that The reagent is used to detect the methylation level of at least one target region of the at least one biomarker, and the target region is selected from: region chr17:75369558-75369622, chr17:75369603-75369691, chr6:392282-392377, chr9:71736209-71870124, chr1:111217074-111217181, chr5:63461942-63462020, chr7:25896423-25896507, chr7:49813254-49813323, chr8:70982125-70982184, chr17:46673901-46674018, chr13:103046952-103047051, chr7:45613861-45613949, chr17:46671415-46671501, chr17:48042492-48042581, chr4:123748602-123748663 or their complementary sequences or processed sequences; or processed sequences of the complementary sequences; or any combination of the foregoing sequences and / or regions. ​

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