A novel multi-target marker combination for gastric cancer detection and its application

Abstract

The present invention provides a novel multi-target combined marker for gastric cancer detection and its application. By finding a series of novel methylation sites that can effectively distinguish between gastric cancer and gastritis patients from high-depth whole genome methylation (WGBS) data of gastric cancer tissue, adjacent cancer tissue, and gastritis patient samples, the combined marker can be used for efficient detection of gastric cancer. Combining the novel gene methylation site with other gastric cancer detection markers (such as PGI, PGII, G17, and CEA) can further improve the AUC value and improve the sensitivity and specificity of gastric cancer screening.

Description

Technical Field

[0001] The present invention relates to the field of cancer screening, and in particular to a novel multi-target gastric cancer detection marker combination and application thereof. Background Art

[0002] Endoscopy and imaging examinations are commonly used in the diagnosis of gastric cancer. However, the incidence of gastric cancer in the general population is low (33 / 100,000). If gastroscopy is used for gastric cancer screening, it will consume a lot of manpower and material resources. In addition, due to its invasiveness, patients generally have a low degree of acceptance. Therefore, endoscopic examination has certain limitations in the early screening of gastric cancer. Although there are many imaging detection methods, they are generally only used for positioning and staging diagnosis. The diagnostic rate of CT for advanced gastric cancer is 65% to 90%, and only about 50% for early gastric cancer. CT is not recommended as the first choice for the initial diagnosis of gastric cancer. When the specificity of traditional serum tumor markers (CEA, CA19-9 and CA72-4, etc.) in the diagnosis of gastric cancer is maintained at 85.1% to 96.2%, its sensitivity is less than 27.6%. In addition, there is also great controversy about the diagnosis of gastric cancer by tests such as PG, G-17 and H.pylori. Endoscopic biopsy, which is the gold standard for the diagnosis of gastric cancer, is not only very invasive, but also has the risk of causing tumor spread. Moreover, due to the widespread intratumoral heterogeneity of tumors, puncture biopsies often fail to show the full picture of the tumor. Although the currently commonly used clinical diagnostic schemes have certain diagnostic capabilities in terms of gastric cancer characterization, location and staging, these diagnostic methods are slightly insufficient in terms of early diagnosis.

[0003] Liquid biopsy technology based on circulating tumor DNA (ctDNA) detection has become the main detection method for early cancer screening due to its non-invasive, real-time and sensitive characteristics. Among them, the methylation index of ctDNA is an ideal marker for early cancer screening due to its advantages such as tissue tracing, enhanced signal-to-noise ratio and large number of characteristic sites. Kandimalla et al. (Clinical Cancer Research 27.22(2021):6135-6144) conducted whole-genome DNA methylation analysis on a variety of gastrointestinal cancers and obtained a large number of differentially methylated regions (DMRs) by comparing cancer tissues and non-cancerous tissues. Based on these DMRs combined with machine learning algorithms, a panel for gastrointestinal-related cancers was developed, with an overall detection AUC value of 0.88. This study suggests that ctDNA methylation detection has reliable diagnostic accuracy in the detection of gastrointestinal cancers. Miao et al. (Cancer Control 27.2(2020):1073274820922559) compared the diagnostic ability of plasma methylated SFRP2 and tumor markers (CEA, CA72-4, CA19-9 and CA242) for gastric cancer. The results showed that the detection sensitivity of methylated SFRP2 was 60.9% and the specificity was 86%, which was significantly better than the sensitivity of tumor markers (11.4%-22.8%), and can be used as a potential biomarker for early gastric cancer screening. However, the AUC values ​​of existing methylation sites for early gastric cancer detection are generally low, with low sensitivity and specificity, and cannot distinguish between gastric cancer and gastritis patients well. Further examination is still needed for diagnosis.

[0004] On the other hand, due to the limitations of the technology, the performance of ctDNA methylation detection also has a certain ceiling. Combining multi-omics indicators such as genomics, epigenetics, and proteomics has become a development trend. A prospective cohort study (Epigenomics 13.19(2021):1557-1570.) was conducted in the First People's Hospital of Yunnan Province in China. A total of 151 gastric cancer patients, 56 atrophic gastritis patients, 87 other gastrointestinal disease patients, and 224 healthy controls were enrolled. This study verified the feasibility of mSEPT9, mRNF180, and CA724 combined for gastric cancer detection. Only mSEPT9, mRNF180, or CA724 detected 48.3%, 37.1%, and 43.1% of gastric cancer, respectively. The combination of mSEPT9 and mRNF180 detected 60.3% of GC, and the combination of all three markers detected 68.6% of GC. The combined detection of the three markers has a sensitivity and specificity of 68.6% and 85.1% for detecting patients with gastric cancer of all stages, respectively. Exact Sciences, an American cancer early screening company, announced data from its Multi-Cancer Early Detection (MCED) biomarker validation study at the 2022 European Society for Medical Oncology (ESMO) Congress. The study rigorously evaluated the performance of four different biomarkers in the blood (including aneuploidy, protein, DNA methylation, and gene mutations) for early detection of cancer. The study included nearly 600 cancer samples, and the non-cancer control cohort included samples of age-matched, potentially healthy individuals and individuals with non-cancer diseases to more effectively represent the intended user population. Exact Sciences, along with researchers from Johns Hopkins University and the Mayo Clinic, demonstrated the test’s ability to detect cancer signals from 15 organ sites (breast, bladder, colorectal, esophageal, kidney, stomach, lung, multiple myeloma, myelodysplastic syndrome, non-Hodgkin lymphoma, ovarian, pancreatic, prostate, gastric and uterine) with a combined sensitivity of 38.7% in Phase I and II. Data from the gastric cancer portion of the program have not yet been released.

[0005] Therefore, it is urgent to find a marker combination that can effectively distinguish gastric cancer from gastritis, so as to achieve high-sensitivity and high-specificity gastric cancer screening, promote early detection and early treatment of gastric cancer, and meet urgent clinical needs. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a novel multi-target gastric cancer detection combined marker and kit and its application in gastric cancer detection. By finding a series of novel methylation sites that can effectively distinguish gastric cancer and gastritis patients from the high-depth whole genome methylation (WGBS) data of gastric cancer tissue, paracancerous tissue, and gastritis patient samples, it can be used for efficient detection of gastric cancer; combining the novel gene methylation site with other gastric cancer detection markers (such as PGI, PGII, G17 and CEA) can further improve the AUC value and improve the sensitivity and specificity of gastric cancer screening.

[0007] The research team found 18 methylation sites with significantly different methylation levels in patients with gastric cancer and other gastric diseases, and further accurately screened a combination of 6 methylation sites, achieving efficient detection of gastric cancer with fewer markers.

[0008] In one aspect, the present invention provides a use of a marker for preparing a gastric cancer detection reagent, wherein the marker is any one or more nucleotide sequences selected from Seq ID NO.1 to Seq ID NO.18 as shown in Table 1 or a combination of their completely complementary sequences.

[0009] Table 1. Gastric cancer detection sites

[0010]

[0011]

[0012] The present invention screened out CpG-based methylation sites that distinguish gastric cancer from non-gastric cancer by performing methylation sequencing on gastric cancer tissue, paired paracancerous tissue, and plasma free DNA from gastritis patients, and then further verified them with a large number of gastric cancer and non-gastric cancer plasma samples, and finally discovered and identified 18 target sequences that were abnormally methylated in gastric cancer patients. Clinical verification has shown that the methylation levels of the 18 new target sequences provided by the present invention are significantly different in patients with gastric cancer and other gastric diseases.

[0013] The 18 target sequences are double-stranded DNA with complementary sequences. It is understandable that the methylation site target sequence provided by the present invention can be its sense strand or its antisense strand.

[0014] In some embodiments, the marker is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18 nucleotide sequences selected from the sequence listings Seq ID NO.1 to Seq ID NO.18, or a combination of their fully complementary sequences.

[0015] Furthermore, the marker is a combination of the nucleotide sequence of Seq ID NO.4 and / or Seq ID NO.18 or their completely complementary sequences.

[0016] The AUC value, detection sensitivity, and specificity of the methylation sites corresponding to Seq ID NO.4 or Seq ID NO.18 were significantly higher than those of the other 16 methylation sites.

[0017] Furthermore, the marker is a combination of 2, 6 or 18 nucleotide sequences comprising Seq ID NO.4 and / or Seq ID NO.18 or their completely complementary sequences.

[0018] Further, the marker comprises a combination of the nucleotide sequences shown in Seq ID NO.1 and Seq ID NO.4 or their completely complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.4 and Seq ID NO.18 or their completely complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.5 and Seq ID NO.18 or their completely complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18 or their completely complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.2, Seq ID NO.4, Seq ID NO.8, Seq ID NO.11, Seq ID NO.14 and Seq ID NO.15 or their completely complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.1, Seq ID NO.5, Seq ID NO.8, Seq ID NO.10, Seq ID NO.11 and Seq ID The nucleotide sequence shown in Seq ID NO.17 or a combination thereof of its completely complementary sequence, or a combination thereof of the nucleotide sequence shown in Seq ID NO.3, Seq ID NO.7, Seq ID NO.12, Seq ID NO.14, Seq ID NO.15 and Seq ID NO.17 or a combination thereof of its completely complementary sequence, or a combination thereof of the nucleotide sequence shown in Seq ID NO.4, Seq ID NO.5, Seq ID NO.8, Seq ID NO.12, Seq ID NO.13 and Seq ID NO.17 or a combination thereof of its completely complementary sequence, or a combination thereof of the nucleotide sequence shown in Seq ID NO.1, Seq ID NO.3, Seq ID NO.6, Seq ID NO.8, Seq ID NO.10 and Seq ID NO.11 or a combination thereof of its completely complementary sequence, or a combination thereof of the nucleotide sequence shown in Seq ID NO.1, Seq ID NO.4, Seq ID NO.5, Seq ID NO.8, Seq ID NO.9 and Seq ID NO.16 or a combination thereof of its completely complementary sequence, or a combination thereof of the nucleotide sequence shown in Seq ID NO.1, Seq ID NO.3, Seq ID NO.6, Seq ID NO.8, Seq ID NO.9 and Seq ID NO.17 or a combination thereof of its completely complementary sequence A combination of the nucleotide sequences shown in Seq ID NO.5, Seq ID NO.7, Seq ID NO.10, Seq ID NO.13 and Seq ID NO.15 or their completely complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.1 to Seq ID NO.18 or their completely complementary sequences.

[0019] Furthermore, the marker is a combination of the nucleotide sequences shown in Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18 or their completely complementary sequences.

[0020] After verification by a large number of clinical gastric cancer and gastritis samples, the present invention has found 6 new hypermethylated sites from the above 18 target sequences that can particularly sensitively and specifically distinguish gastric cancer patients, gastritis patients, and benign control individuals, respectively having sequences shown in the sequence table Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15, and Seq ID NO.18. Data from the detection of clinical plasma samples of gastric cancer and gastritis show that the AUC value of gastric cancer detection using only these 6 methylation sites can reach 0.957, the sensitivity reaches 88%, and the specificity reaches 89%; the study proves that the gastric cancer diagnostic performance of the 6 methylation sites is not lower than or even slightly higher than the combination of 18 methylation sites, and the detection process is simpler and more efficient, with the most application prospects.

[0021] Furthermore, the markers also include protein markers, and the protein markers are composed of pepsinogen I (PGI), pepsinogen II (PGII), gastrin (G17) and carcinoembryonic antigen (CEA).

[0022] Due to technical limitations, the performance of cancer detection through methylation sites alone also has a certain ceiling. Multi-omics combining multiple categories such as genomics, epigenetics, and proteomics has become a development trend. Several clinical studies have shown that the sensitivity and specificity of multi-omics marker detection are better than those of single-omics markers. Therefore, based on the methylation sites provided by the present invention, combined with protein level detection, the AUC value of gastric cancer detection is further improved, and the detection sensitivity and specificity are improved.

[0023] The levels of the protein markers PGI, PGII, G17 and CEA provided by the present invention in the serum of patients with gastric cancer are significantly different from those in the serum of patients with gastritis.

[0024] Studies have shown that by detecting the methylation levels of the series of novel methylation sites provided by the present invention, combined with the levels of PGI, PGII, G17 and CEA proteins in serum samples, it is possible to more sensitively and specifically distinguish between gastric cancer patients and non-cancer individuals. Data from the detection of clinical gastric cancer and gastritis samples showed that the combination provided by the present invention can very effectively distinguish between gastric cancer and non-cancer patients, with an AUC value of up to 0.971, a sensitivity of 92%, and a specificity of 90%.

[0025] On the other hand, the present invention provides a primer combination for detecting gastric cancer, wherein the primer combination is any one or more groups selected from the 18 groups of primer and probe combinations shown in Table 2.

[0026] Table 2. Primer and probe combinations

[0027]

[0028]

[0029] In some embodiments, the primer combination is selected from any two or six of the primer and probe combinations shown in Table 2.

[0030] Furthermore, the primer combination is the 6 sets of primer and probe combinations shown in Table 3.

[0031] Table 3. 6 sets of primers and probe combinations

[0032] Group Target sequence Forward primer Reverse primer Probe 4 Seq ID NO.4 Seq ID NO.32 Seq ID NO.34 Seq ID NO.33 10 Seq ID NO.10 Seq ID NO.50 Seq ID NO.52 Seq ID NO.51 12 Seq ID NO.12 Seq ID NO.56 Seq ID NO.58 Seq ID NO.57 13 Seq ID NO.13 Seq ID NO.59 Seq ID NO.61 Seq ID NO.60 15 Seq ID NO.15 Seq ID NO.65 Seq ID NO.67 Seq ID NO.66 18 Seq ID NO.18 Seq ID NO.74 Seq ID NO.76 Seq ID NO.75

[0033] Furthermore, the primer combination also includes a primer and a probe for an internal reference gene, the internal reference gene COL2A1 has a sequence as shown in Seq ID NO.19 in the sequence list, its forward primer has a sequence as shown in Seq ID NO.20 in the sequence list, the reverse primer has a sequence as shown in Seq ID NO.22 in the sequence list, and the probe has a sequence as shown in Seq ID NO.21 in the sequence list.

[0034] In yet another aspect, the present invention provides a kit for detecting gastric cancer, the kit comprising any one of the primer and probe combinations described above.

[0035] Furthermore, the kit or chip also includes reagents or materials for detecting proteins, and the proteins include PGI, PGII, G17 and CEA.

[0036] On the other hand, the present invention provides a marker combination for detecting gastric cancer, the marker combination comprising methylation sites and protein markers, the methylation sites being any one or more selected from the sequence table Seq ID NO.1 to Seq ID NO.18; the protein markers comprising PGI, PGII, G17 and CEA.

[0037] In some embodiments, the methylation sites in the marker combination are a combination of 2, 6 or 18 nucleotide sequences of Seq ID NO.4 and / or Seq ID NO.18 or their completely complementary sequences.

[0038] In some embodiments, the methylation site in the marker combination is a combination of the nucleotide sequences shown in Seq ID NO.1 and Seq ID NO.4 or their fully complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.4 and Seq ID NO.18 or their fully complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.5 and Seq ID NO.18 or their fully complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18 or their fully complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.2, Seq ID NO.4, Seq ID NO.8, Seq ID NO.11, Seq ID NO.14 and Seq ID NO.15 or their fully complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.1, Seq ID NO.5, Seq ID NO.8, Seq ID NO.10, Seq ID NO.11 and Seq ID The nucleotide sequence shown in Seq ID NO.17 or a combination thereof of its completely complementary sequence, or a combination of the nucleotide sequence shown in Seq ID NO.3, Seq ID NO.7, Seq ID NO.12, Seq ID NO.14, Seq ID NO.15 and Seq ID NO.17 or a combination thereof of its completely complementary sequence, or a combination of the nucleotide sequence shown in Seq ID NO.4, Seq ID NO.5, Seq ID NO.8, Seq ID NO.12, Seq ID NO.13 and Seq ID NO.17 or a combination thereof of its completely complementary sequence, or a combination of the nucleotide sequence shown in Seq ID NO.1, Seq ID NO.3, Seq ID NO.6, Seq ID NO.8, Seq ID NO.10 and Seq ID NO.11 or a combination thereof of its completely complementary sequence, or a combination of the nucleotide sequence shown in Seq ID NO.1, Seq ID NO.4, Seq ID NO.5, Seq ID NO.8, Seq ID NO.9 and Seq ID NO.16 or a combination thereof of its completely complementary sequence, or a combination of the nucleotide sequence shown in Seq ID NO.1, Seq ID NO.3, Seq ID NO.6, Seq ID NO.8, Seq ID NO.9 and Seq ID NO.17 or a combination thereof of its completely complementary sequence. A combination of the nucleotide sequences shown in Seq ID NO.5, Seq ID NO.7, Seq ID NO.10, Seq ID NO.13 and Seq ID NO.15 or their completely complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.1 to Seq ID NO.18 or their completely complementary sequences.

[0039] In some embodiments, the methylation sites in the marker combination are a combination of the nucleotide sequences shown in Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18 or their completely complementary sequences.

[0040] On the other hand, the present invention provides a marker combination for detecting gastric cancer, wherein the marker combination includes a combination of the nucleotide sequences shown in Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18 or their completely complementary sequences.

[0041] The method for detecting gastric cancer in vitro using the methylation sites provided by the present invention comprises the following steps:

[0042] 1) Separating genomic DNA or plasma free DNA from the biological sample to be tested;

[0043] 2) detecting the methylation status of the methylation site or the combination of methylation sites;

[0044] 3) The state of the biological sample is determined by the state of the target gene methylation site, and in vitro detection of gastric cancer is achieved.

[0045] The method for detecting gastric cancer in vitro using the methylation site and protein combination provided by the present invention comprises the following steps:

[0046] 1) Separating genomic DNA or plasma free DNA and serum from the biological sample to be tested;

[0047] 2) detecting the methylation status of the methylation site or the combination of methylation sites and serum PGI, PGII, G17 and CEA protein levels;

[0048] 3) The status of the biological sample is determined by the methylation site status of the target gene and the level of the protein marker, and in vitro detection of gastric cancer is achieved.

[0049] In some embodiments, the method further comprises the following steps:

[0050] 1) Separating serum and plasma of the biological sample to be tested, and extracting plasma free DNA of the biological sample to be tested;

[0051] 2) treating the DNA sample obtained in step 1) with a reagent to convert the unmethylated cytosine base at position 5 into uracil, wherein the base converted into uracil has a hybridization ability different from that of the unmethylated cytosine at position 5 and can be detected;

[0052] 3) combining the DNA sample treated in step 2) with a polymerase chain reaction system, wherein the polymerase chain reaction system comprises one or more of the following components: a DNA polymerase, a primer or primer combination of the target sequence, a corresponding probe or probe combination, and a polymerase chain reaction buffer, and generating an amplification product after a polymerase chain reaction occurs;

[0053] 4) Detecting the amplified product with a fluorescently labeled probe or a combination of several probes. If the probe binds to the amplified product, a fluorescent signal can be generated; if the probe cannot bind to the amplified product, no fluorescent signal can be generated;

[0054] 5) determining the methylation status of at least one CpG of the target sequence of the target gene based on whether a fluorescent signal is generated;

[0055] 6) Using magnetic particle chemiluminescent immunoassay sandwich method to determine the concentrations of PGI, PGII, G17 and CEA in human serum;

[0056] In some embodiments, in the polymerase chain reaction system, the DNA polymerase includes a thermostable DNA polymerase, a hot-start DNA polymerase, or a polymerase lacking 5'-3' exonuclease activity.

[0057] The methylation state of at least one CpG in the target gene target sequence is determined by the cycle threshold Ct value of the PCR reaction or the difference between the Ct values ​​of the target gene. By analyzing the methylation state of DNA in a biological sample using a PCR reaction, the methylation state of one or more target gene target sequences can be easily detected.

[0058] In some embodiments, the unmethylated cytosine at position 5 of DNA is converted into uracil, preferably using a reagent such as bisulfite.

[0059] Methylation modification of position 5 of cytosine is a type of DNA modification widely found in eukaryotic cells. Methylation modification on DNA not only plays an important role in the growth and development of organisms, but also plays a very important role in the process of cell proto-oncogenesis. Since 5-methylcytosine has the same base complementary pairing properties as cytosine, it cannot be directly measured by first-generation sequencing or high-throughput sequencing. Currently, the most commonly used method for detecting 5-methylcytosine is to convert the DNA to be tested by bisulfite. After alkaline hydrolysis, unmethylated cytosine is converted to uracil, while 5-methylcytosine does not undergo conversion. Uracil will complementarily pair with adenine during base complementary pairing, which is different from the complementary pairing of cytosine and guanine. Therefore, by detecting DNA treated with bisulfite, sequencing technology, polymerase chain reaction technology or DNA molecular hybridization-related technology can be used to determine the remaining unconverted cytosines, thereby determining which cytosines in the original DNA molecule have been methylated. Therefore, the present invention preferably uses bisulfite as a methylation conversion reagent, and after processing the DNA sample to be tested, the methylation status of the CpG dinucleotide sequence in the target gene target sequence is determined by sequencing, polymerase chain reaction or DNA molecular hybridization and other related technologies.

[0060] In some embodiments, the method of the present invention is suitable for analyzing samples in a mixed state, such as low-concentration tumor cells present in blood, feces or tissues. Therefore, when analyzing the methylation state of CpG dinucleotide sequences in such samples, those skilled in the art can use quantitative determination methods to determine the methylation level of CpG dinucleotide sequences, such as percentage, ratio, score or degree, etc., rather than the methylation modification state of single nucleotide molecules. Accordingly, the methylation state described in the present invention should be considered to include the methylation modification state of single nucleotide molecules, including the methylation state reflected by the quantitative methylation level.

[0061] In some embodiments, the present invention uses real-time fluorescence quantitative PCR to determine the methylation state, such as real-time fluorescence quantitative PCR using Taqman probes, real-time fluorescence quantitative PCR using fluorescent dyes, methylation-specific PCR (MSP), etc., to determine the methylation state of at least one CpG dinucleotide of the target gene target sequence. Since the base complementary pairing ability of the target sequences of genes with different methylation states is different, the quantitative test of the methylation state in the genomic DNA sample can be performed by real-time fluorescence quantitative PCR, wherein the sequence differentiation occurs at the probe hybridization level.

[0062] As a control, the COL2A1 gene was used in the present invention, and the primers and probes were designed not to cover any CpG dinucleotide positions, and the genomic DNA treated with the reagent was input for detection.

[0063] Real-time fluorescence quantitative PCR can be used with any suitable probe, such as Taqman probe, MGB probe, scorpion probe, etc. The fluorescent probe generally comprises a luminescent group, a nucleic acid sequence, a quenching group, and some chemical modifications or special nucleotides, such as thionucleotides, locked nucleic acids, etc., when necessary.

[0064] In general, during real-time fluorescence quantitative PCR detection, the probe is designed to have a melting temperature that is 10°C higher than that of the forward and reverse primers, so that the probe will be completely bound to the PCR product during annealing and extension. Typically, for example, Taqman probes will be hydrolyzed during the extension process under the action of DNA polymerase with 5'-3' exonuclease activity, causing the fluorescent group and quenching group in the probe to move away, thereby destroying the resonant energy transfer between the fluorescent group and the quenching group, so that the fluorescence emitted by the fluorescent group can be detected by the instrument. At the same time, as the number of PCR products gradually increases, the fluorescence signal will rise exponentially within a certain period of time, and finally present an "S"-shaped amplification curve on the fluorescence quantitative PCR instrument.

[0065] The reaction reagents used for real-time fluorescence quantitative PCR include, but are not limited to: forward and reverse primers of the target gene target sequence, Taqman fluorescent probe, optimized PCR buffer, deoxynucleotide triphosphates, and DNA polymerase with 5'-3' exonuclease activity.

[0066] The present invention detects the target protein level by using, but not limited to, the magnetic microparticle chemiluminescence immunoassay sandwich method, and other commonly used detection methods such as flow cytometry, enzyme-linked immunosorbent assay, etc.

[0067] In some methods, whether the detected biological sample is positive is determined by combining the methylation of the above-mentioned site or site combination and the status of the protein marker level.

[0068] The methylation sites for screening gastric cancer provided by the present invention have the following beneficial effects:

[0069] 1. It provides 18 new differentially methylated sites, whose methylation status in plasma free DNA of gastric cancer patients is significantly different from that of plasma free DNA of gastritis patients;

[0070] 2. Any combination of 2 or 6 methylation sites from the 18 methylation sites can be used to detect gastric cancer with high sensitivity and specificity;

[0071] 3. Among the 18 methylation sites, the two methylation sites Seq ID NO.4 and Seq ID NO.18 were found to have higher AUC values ​​for gastric cancer detection, with better sensitivity and specificity;

[0072] 4. Found 6 methylation sites (Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18) and combined them with protein markers PGI, PGII, G17 and CEA for non-invasive and rapid in vitro detection of gastric cancer, which can more effectively distinguish gastric cancer from other gastric disease patients. The AUC value can reach up to 0.971, the sensitivity reaches 92%, and the specificity reaches 90%; 5. It is convenient and fast, and the test results are highly consistent with the clinical gold standard test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 The heat map of WGBS sequencing data of gastric cancer, adjacent tissue and normal human blood in Example 1, wherein tumor is a gastric cancer tissue sample; adjacent is a adjacent tissue sample, and normal is a normal human cfDNA sample;

[0074] Figure 2 It is a box plot comparing the methylation levels of the 18 methylation sites in Example 1 in different types of samples;

[0075] Figure 3 This is a schematic diagram of the results of fluorescence quantitative PCR reaction detection of gastric cancer patient samples in Example 2;

[0076] Figure 4 This is a schematic diagram of the fluorescence quantitative PCR reaction test results of gastritis patient samples in Example 2;

[0077] Figure 5 The ROC curve obtained by combining the normalized results of the two methylation sites in Example 2 with the PGI, PGII, G17 and CEA protein detection;

[0078] Figure 6 The ROC curve is obtained by combining the normalized results of the 6 methylation sites in Example 3 with the PGI, PGII, G17 and CEA protein detection. DETAILED DESCRIPTION

[0079] The present invention is further described in detail below in conjunction with the accompanying drawings and examples. It should be noted that the following examples are intended to facilitate understanding of the present invention and do not have any limiting effect on it. The reagents used in this example are all known products and were obtained by purchasing commercially available products.

[0080] Example 1 Screening of gene methylation sites

[0081] Based on the actual clinical application of the product, this example discovered and identified 18 target sequences that were abnormally methylated in gastric cancer patients. The specific screening process for gastric cancer methylation qPCR candidate targets is as follows:

[0082] The screening process consisted of two stages. In the first stage, WGBS data of 9 pairs of gastric cancer tissues and adjacent tissue samples and WGBS data of 20 normal human cfDNA samples were collected. Figure 1 ) to screen the regions showing high methylation in gastric cancer tissues and demethylation in adjacent adjacent tissues (i.e., gastric cancer panel regions), and design and synthesize corresponding capture probes; the second phase: use the probes to capture and sequence 440 cfDNA samples (including 221 gastric cancer cfDNA samples, 177 gastritis cfDNA samples, and 42 normal human cfDNA samples), and by analyzing these data, screen out 32 CpG sites from the gastric cancer panel region that can best distinguish gastric cancer cfDNA samples from non-gastric cancer cfDNA samples, with good complementarity and continuity as candidate qPCR targets.

[0083] In the first stage, we first divided the human genome (hg19) into sliding windows of 100bp in length (50bp overlap), counted the number of CpG sites (continuous cytosine nucleotides and guanine nucleotides arranged in the 5' to 3' direction on the DNA) in each sliding window, and selected sliding windows containing at least 5 CpG sites for subsequent analysis; second, for the WGBS data of each tissue sample, the average methylation rate in each sliding window was calculated (the number of methylated CpGs on the reads aligned to the window was divided by the number of all CpGs on the reads aligned to the window) and the methylation rate was normalized (the number of methylated and demethylated CpG sites were calculated for every 100 CpG sites). Finally, for each pair of tumor tissue and its adjacent adjacent tissue, the number of methylated and demethylated CpG sites obtained after standardization in each sliding window (i.e., a 2×2 contingency table) was used to perform the Fisher exact test (FDR multiple hypothesis testing correction), and only the sliding windows with a p-value less than 0.01 were retained. In these sliding windows, those parts with an average methylation rate less than 0.01 in the WGBS data of normal human cfDNA samples and that could be annotated to gene promoters were selected as capture probe regions.

[0084] In the second stage of sequencing data analysis, considering that adjacent CpG sites often show similar methylation changes, in each candidate differentially methylated region, we took any five consecutive CpG sites as a marker (5CpG), and used the ratio of all five CpG sites in each marker to be methylated (methylation rate) as the characteristic value. Specifically, first, samples with a 5CpG maker coverage (at least 10X) greater than 90% were selected. Second, in these samples, the difference in methylation rate of each 5CpG marker in tumor cfDNA samples and non-tumor cfDNA samples was compared, and the 5CpG marker with the largest difference in methylation rate between the two samples, the best cumulative coverage of tumor cfDNA samples, and continuous was selected as the candidate qPCR target. After completing the target screening, samples with a 5CpG maker coverage (at least 10X) less than 90% but greater than 80% were used as training sets to independently verify the selected targets.

[0085] According to the regional settings, the sequences were downloaded from the NCBI database website, and the sequences were converted into methylated sequences using Methyl Primer Express v1.0 software. In order to ensure that the sequences in the designed regions have a relatively balanced GC content distribution, the antisense strand was also considered as a template for design when selecting the sequences. According to the universal primer design principles, corresponding detection primers and probes were designed for the 32 highly methylated regions. The designed primers were screened and verified using the following concentrations of templates:

[0086] 1) Select the pure positive template MJ, which is purified after SSSI enzyme treatment. All CG sites are methylated and it is the best positive substance template for methylation test verification. 100pg / μl and 10pg / μl are selected to represent the medium and low concentration levels.

[0087] 2) Select pure negative template WBC, which is taken from the peripheral blood leukocyte gDNA of healthy people, contains a complete set of gDNA background, and after ultrasonic interruption, it better simulates the state of human cfDNA. Select 1ng / μl as a simulation of the real high concentration sample.

[0088] 3) Low-abundance mixed template MJ / WBC, which is used to simulate the mixed state in real samples, and usually 5% is used as the state of simulating low-abundance samples.

[0089] According to the design combination of primer screening, each combination was prepared with a methylation detection system for comparison, and 100pg / μl and 10pg / μl methylation positive templates, 1ng / μl methylation negative templates and 5% abundance methylation mixed templates were used for comprehensive screening. The screening criteria are shown in Table 4. Finally, the primer combination was selected based on the specificity, detection rate and amplification effect Ct value as the judgment index, and the screening results are shown in Table 5.

[0090] Table 4. Sequence combination screening criteria

[0091] serial number Template Type concentration Performance requirements 1 WBC 1ng / μl Not detected 2 MJ 100pg / μl CT value between 29-31 3 MJ 10pg / μl Detectable 4 5%MJ / WBC 1ng / μl Detectable

[0092] Table 5. Screening results

[0093]

[0094]

[0095] (UD means not detected)

[0096] After screening, nonspecific amplification problems were found in regions 11, 12, 29, and 30, and insufficient amplification sensitivity was found in regions 7, 8, 23, and 24, so the above 8 regions were excluded. The remaining 24 regions all achieved a relatively good level of detection performance (see Table 6). Subsequently, the clinical performance of the target was verified in 30 gastric cancer and 30 gastritis clinical samples, and the single-region ROC analysis of the test results was performed, excluding the region with AUC < 0.7, which not only reduced the number of detection regions, but also made it more suitable for clinical application of qPCR detection, while maintaining better clinical performance.

[0097] Table 6. Detection performance of the 24 screened regions

[0098] serial number Number of regional combinations AUC 1 Region 1 0.722 2 Area 2 0.703 3 Area 3 0.848 4 Region 4 0.875 5 Area 5 0.806 6 Area 6 0.778 7 Area 9 0.814 8 Region 10 0.796 9 Area 13 0.802 10 Area 14 0.753 11 Area 15 0.52 12 Area 16 0.601 13 Area 17 0.769 14 Region 18 0.745 15 Region 19 0.638 16 Area 20 0.61 17 Area 21 0.743 18 Area 22 0.782 19 Area 25 0.74 20 Area 26 0.795 21 Area 27 0.843 22 Area 28 0.882 23 Area 31 0.622 24 Area 32 0.603

[0099] After the above screening, 18 target sequences with high methylation in gastric cancer patients were finally found and determined (having nucleotide sequences shown as Seq ID NO.1 to Seq ID NO.18, see Table 1 in the manual for details). The box plot of the methylation levels of these 18 abnormal methylation sites in five different types of samples, including gastric cancer tissue, paracancerous tissue, gastritis plasma, gastric cancer plasma, and normal plasma, is shown in the figure below: Figure 2 As shown, it can be seen that the methylation levels of the 18 methylation sites screened by the present invention are significantly different for different types of samples, and can effectively distinguish gastric cancer patients from other gastric disease patients and normal people.

[0100] Example 2 Screening for Gastric Cancer Using Two Methylation Sites (or Binding Protein Markers)

[0101] In this embodiment, two of the 18 methylation sites selected in Example 1 are selected for gastric cancer detection. Two methods are used for detection: 1. Detection of gastric cancer using two methylation sites; 2. Detection of gastric cancer using a combination of two methylation sites and protein markers.

[0102] 1. Using two methylation sites to detect gastric cancer

[0103] The specific method of using two methylation sites to detect gastric cancer includes the following steps:

[0104] In the first step, serum and plasma were separated from the blood samples, and the magnetic bead extraction reagent was used to extract plasma free DNA from the biological samples to be tested, of which 20 were from gastric cancer patients and 20 were from gastritis patients.

[0105] In step 2, a methylation conversion reagent with bisulfite as the main component is used to perform methylation conversion on the plasma free DNA sample extracted in step 1. 5 ng of plasma free DNA is added to convert unmethylated cytosine into uracil.

[0106] In step 3, the converted plasma free DNA is put into a real-time fluorescence quantitative PCR reaction system containing the target sequence of the detection gene. The fluorescent probes used to detect the two target sequences are labeled with FAM and ROX fluorescent dyes, respectively, and the fluorescent probe used to detect the internal reference gene COL2A1 is labeled with VIC fluorescent dye. The upstream primer, downstream primer and probe refer to the upstream primer, downstream primer and probe corresponding to the two target sequences, respectively, as shown in Table 2.

[0107] The system for fluorescence quantitative PCR detection is a mixture of primers and probes of multiple target genes and internal reference COL2A1 to form a multiplex PCR system, and multiple target genes are detected simultaneously with the internal reference COL2A1. A maximum of 4 target genes and internal references can be detected simultaneously in a single tube. When the number of target genes is greater, multiple tubes are used for detection. In the reaction system, the forward and reverse primers of the target gene sequence are input at a concentration of 0.167 μM, the probe input concentration is 0.167 μM, the real-time fluorescence quantitative PCR reaction system is 30 μL, and the forward and reverse primers of the internal reference gene sequence are input at a concentration of 0.083 μM.

[0108] Step 4: Set up the fluorescence quantitative PCR reaction detection program as shown in Table 7:

[0109] Table 7. Multiplex PCR reaction program

[0110]

[0111] Step 5: Obtain the fluorescence quantitative PCR reaction detection results.

[0112] In this embodiment, the following groups of combinations containing 2 methylation sites are selected for detection:

[0113] 1. Seq ID NO.1, Seq ID NO.4

[0114] 2. Seq ID NO.4, Seq ID NO.18

[0115] 3. Seq ID NO.5, Seq ID NO.18

[0116] When the two methylation sites corresponding to the nucleotide sequences shown in the second group Seq ID NO.4 and Seq ID NO.18 are selected, the detection results are as follows Figure 3 , 4 As shown, Figure 3 This is the result of the sample from a gastric cancer patient. The fluorescent signals of all the methylation sites to be tested were detected, which is positive. Figure 4 For samples from gastritis patients, only the fluorescence signal of the control gene COL2A1 was detected, and the fluorescence signals of the other sites to be tested were not detected, which was negative.

[0117] The result analysis and judgment method of gastric cancer detection using two methylation sites: 1) record the Ct value of each methylation site automatically output by the software; 2) calculate the Ct value of each site in the sample and the internal reference COL2A1 respectively, and then normalize the Ct: ΔCt(target sequence) = |Ct(COL2A1)-Ct(target sequence)|; 3) M methylation sites, the score of the i-th methylation site is Mi. The value of Mi is 0 or 1, and Mi is judged according to the ΔCt(target sequence) value and the corresponding Youden's index. If ΔCt(target sequence)>Youden's index, Mi=1, if ΔCt(target sequence)<Youden's index, Mi=0. M-score of methylation=sum_i^m(Mi). The detection results are shown in Table 5.

[0118] The results obtained by using the kit were corrected and analyzed according to logistic regression analysis. The threshold setting of M-score was set according to the ROC curve. And by combining the detection results of the two target methylation sites, the 40 samples were cross-validated by 10 times, and the average value was taken to obtain the classification ROC curve, and the AUC value, detection sensitivity and specificity were calculated.

[0119] The detection results of methylation of two target sequences SEQ ID NO.4 and SEQ ID NO.18 are shown in Table 8. A 10-fold cross validation is performed on these 40 samples, and the average value is taken to obtain the classification ROC curve.

[0120] Table 8. Results of methylation level detection at two methylation sites

[0121]

[0122]

[0123] According to the sample test results of this time, the gastric cancer detection level of the combination of the two methylation sites of SEQ ID NO.4 and SEQ ID NO.18 has an AUC of 0.913, a sensitivity of 74%, and a specificity of 91%, which is significantly better than the other two methylation site combinations. The test analysis results of Groups 1, 2, and 3 are detailed in the subsequent Example 5 and Table 11.

[0124] 2. Detection of gastric cancer using a combination of two methylation sites and protein markers

[0125] This embodiment also uses two methylation sites of groups 1, 2, and 3 respectively, and combines them with a protein marker method to detect gastric cancer, wherein the protein markers include PGI, PGII, G17, and CEA.

[0126] The detection method of the two methylation sites refers to the above steps 1 to 5. The detection of protein markers PGI, PGII, G17 and CEA requires the addition of step 6: taking the serum sample separated in step 1, and using the magnetic particle chemiluminescence immunoassay sandwich method to determine the concentration of protein markers in human serum.

[0127] Magnetic particle chemiluminescent immunoassay sandwich method process: R1 (using antibodies corresponding to PGI, PGII, G17 and CEA respectively), the sample to be tested, and M magnetic particles are mixed and incubated. Different sites of the protein marker in the sample bind to the antibodies coupled to the magnetic beads to form a solid-phase antibody-antigen complex; after washing, R2 reagent (using secondary antibodies corresponding to PGI, PGII, G17 and CEA respectively) is added and mixed and incubated, and the above complex binds to the labeled tumor marker antibody to form a solid-phase antibody-antigen-antibody sandwich complex; by washing, the unbound antibodies and other substances are removed. Chemiluminescent substrate 1 and chemiluminescent substrate 2 are added to the reaction complex, and the chemiluminescent reaction is determined by relative luminescence intensity. The luminescence intensity generated is proportional to the concentration of tumor markers in the sample. The protein markers in the sample are tested by magnetic particle chemiluminescent immunoassay sandwich method and the score is determined as the P value. The test results are shown in Table 9.

[0128] Analysis and judgment method: In combination with the above-mentioned analysis and judgment method of the results of gastric cancer detection using two methylation sites alone, it is also necessary to:

[0129] 1) Normalize the PGI, PGII, G17 and CEA test values ​​of each sample separately: P1 = log10P PGI / PGII ,P2=log10P G17 , P3=log10P CEA ; Protein P-score = sum(a*P1+b*P2+c*P3), where a = -3.22, b = 4.83, c = 1.24;

[0130] 2) The results obtained by using the kit are corrected and analyzed according to logistic regression analysis. The thresholds of M-score and P-score are set according to the ROC curve.

[0131] The detection performance is improved by integrating two complementary dimensions of methylation and protein markers. The integrated model is GC-score=M-score+P-score. In some embodiments, when the GC-score value is equal to or greater than the set threshold, the result indicates a positive detection of gastric cancer and / or early gastric cancer in the patient. In some embodiments, when the GC-score value is less than the threshold, the result indicates a negative detection of gastric cancer and / or early gastric cancer in the patient.

[0132] 3) By integrating the normalized results of the two target methylation sites and the PGI / PGII, G17 and CEA protein detection results, these 40 samples were cross-validated by 10 times and the average was taken to obtain the classification ROC curve.

[0133] When the two methylation sites corresponding to the nucleotide sequences shown in Seq ID NO.4 and Seq ID NO.18 of the second group were selected, the normalized results after PGI / PGII, G17 and CEA protein detection were combined, and 10 cross validations were performed on these 40 samples, and the average value was taken to obtain the classification ROC curve as shown in the figure. Figure 5 As shown, the detection results of PGI, PGII, G17 and CEA proteins are shown in Table 9.

[0134] Table 9. Detection results of protein levels in different samples

[0135]

[0136]

[0137] When the two methylation sites in group 2 were used and the results were normalized after combining the detection of PGI, PGII, G17 and CEA proteins, the gastric cancer detection effect could be further improved, with an AUC of 0.938, a sensitivity of 78% and a specificity of 94% ( Figure 5 ).

[0138] The two methylation sites of groups 1, 2, and 3 are combined with the normalized results after detection of PGI, PGII, G17, and CEA proteins, as detailed in the subsequent Example 5.

[0139] Example 3 Screening for Gastric Cancer Using Six Methylation Sites (or Binding Protein Markers)

[0140] This example selects 6 methylation sites from the 18 methylation sites screened in Example 1 for gastric cancer detection. Two methods are used for detection: 1. Detection of gastric cancer using 6 methylation sites; 2. Detection of gastric cancer using a combination of 6 methylation sites and protein markers.

[0141] 1. Using 6 methylation sites to detect gastric cancer

[0142] In this embodiment, the following groups of 6 methylation sites are selected for detection:

[0143] 4. Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18

[0144] 5. Seq ID NO.2, Seq ID NO.4, Seq ID NO.8, Seq ID NO.11, Seq ID NO.14 and Seq ID NO.15

[0145] 6. Seq ID NO.1, Seq ID NO.5, Seq ID NO.8, Seq ID NO.10, Seq ID NO.11 and Seq ID NO.17

[0146] 7. Seq ID NO.3, Seq ID NO.7, Seq ID NO.12, Seq ID NO.14, Seq ID NO.15 and Seq ID NO.17

[0147] 8. Seq ID NO.4, Seq ID NO.5, Seq ID NO.8, Seq ID NO.12, Seq ID NO.13 and Seq ID NO.17

[0148] 9. Seq ID NO.1, Seq ID NO.3, Seq ID NO.6, Seq ID NO.8, Seq ID NO.10 and Seq ID NO.11

[0149] 10. Seq ID NO.1, Seq ID NO.4, Seq ID NO.5, Seq ID NO.8, Seq ID NO.9 and Seq ID NO.16

[0150] 11. Seq ID NO.1, Seq ID NO.5, Seq ID NO.7, Seq ID NO.10, Seq ID NO.13, Seq ID NO.15

[0151] The specific method of using 6 methylation sites to detect gastric cancer includes the following steps:

[0152] In the first step, 40 gastritis and gastric cancer patients were obtained, including 20 gastritis samples and 20 gastric cancer samples. Serum and free plasma DNA of the samples were separated and extracted.

[0153] In the second step, a methylation conversion reagent with bisulfite as the main ingredient is used to perform methylation conversion on the plasma free DNA sample. The plasma free DNA is added to convert the unmethylated cytosine into uracil.

[0154] The third step is to perform real-time fluorescence quantitative PCR detection according to the method of Example 2 using the upstream primers, downstream primers and probes corresponding to the six target sequences as shown in Table 2.

[0155] According to the sample detection results of this time, the effect of using the 6 methylation sites in group 7 is significantly better than the gastric cancer detection level of other groups. According to the ROC curve, its AUC=0.957, sensitivity is 89%, and specificity is 89%.

[0156] The detection and analysis results of the 6 methylation sites in Groups 4-11 are detailed in the subsequent Example 5.

[0157] 2. Detection of gastric cancer using a combination of 6 methylation sites and protein markers

[0158] In addition to the above test using 6 methylation sites to detect gastric cancer, it is also necessary to add:

[0159] In the fourth step, the serum samples separated in the first step were taken, and the levels of PGI, PGII, G17 and CEA protein markers in human serum were measured using a magnetic particle chemiluminescence immunoassay sandwich method, and the results were analyzed.

[0160] In the fifth step, the methylation and protein values ​​of each gene in the detected results were normalized. By combining the ΔCt (target gene) results of the six methylation sites and the detection results of the three protein marker levels, the 40 samples were cross-validated by 10 times, and the average value was taken to obtain the classification ROC curve ( Figure 6).

[0161] The normalized results of the six methylation sites of groups 4-11 after combining the detection of PGI, PGII, G17 and CEA proteins are detailed in the subsequent Example 5.

[0162] When the 6 methylation sites of group 7 are used and combined with the normalized results after PGI, PGII, G17 and CEA protein detection, the gastric cancer detection effect can be further improved, with AUC = 0.971, sensitivity of 92% and specificity of 90%.

[0163] Example 4 Screening for Gastric Cancer Using 18 Methylation Sites (or Binding Protein Markers)

[0164] This embodiment uses the 18 methylation sites screened in Example 1 to detect gastric cancer. Two methods are used for detection: 1. Detect gastric cancer using 18 methylation sites; 2. Detect gastric cancer using a combination of 18 methylation sites and protein markers.

[0165] 1. Detection of gastric cancer using 18 methylation sites

[0166] The specific method of using 18 methylation sites to detect gastric cancer includes the following steps:

[0167] In the first step, 40 gastritis and gastric cancer patients were obtained, including 20 gastritis samples and 20 gastric cancer samples. Serum and free plasma DNA of the samples were separated and extracted.

[0168] In the second step, a methylation conversion reagent with bisulfite as the main ingredient is used to perform methylation conversion on the plasma free DNA sample. The plasma free DNA is added to convert the unmethylated cytosine into uracil.

[0169] The third step is to perform real-time fluorescence quantitative PCR detection according to the method of Example 2 using the upstream primers, downstream primers and probes of the 18 target sequences shown in Table 2.

[0170] The AUC for detection using 18 methylation sites reached 0.956, see Example 5 for details.

[0171] 2. Detection of gastric cancer using a combination of 18 methylation sites and protein markers

[0172] The specific method of detecting gastric cancer using a combination of 18 methylation sites and protein markers includes the following steps: Based on the above detection of gastric cancer using 18 methylation sites, it is also necessary to add:

[0173] In the fourth step, the serum samples separated in the first step were taken, and the levels of PGI, PGII, G17 and CEA protein markers in human serum were measured using a magnetic particle chemiluminescence immunoassay sandwich method, and the results were analyzed.

[0174] In the fifth step, the methylation and protein values ​​of each gene in the detected results were normalized. By integrating the ΔCt (target gene) results of the 18 target genes and the detection results of the three protein marker levels, the 40 samples were cross-validated by 10 times, and the average value was taken to obtain the classification ROC curve, whose AUC-methylation+protein=0.970, the sensitivity reached 91%, and the specificity reached 90%, see Example 5 for details.

[0175] Example 5 Comparative analysis of performance using different methylation site combinations

[0176] Mathematical modeling analysis of the relative cycle number ΔCT values ​​of the 18 methylation sites (SEQ ID NO. 1 to 18) of 40 gastric cancer and gastritis patient samples obtained in Example 1 for different site combinations was performed to explore the application of the 18 methylation sites and proteins as a biomarker combination for the detection of gastric cancer.

[0177] First, we evaluated the performance of the models of each of the 18 methylation sites for diagnosing gastric cancer and calculated their AUC values. The results are shown in Table 10.

[0178] Table 10. Comparison of performance of single methylation sites in diagnosing gastric cancer

[0179] SEQ ID NO AUC 1 0.732 2 0.713 3 0.760 4 0.880 5 0.837 6 0.862 7 0.813 8 0.811 9 0.832 10 0.815 11 0.843 12 0.839 13 0.837 14 0.838 15 0.834 16 0.827 17 0.819 18 0.871

[0180] As can be seen from Table 10, the 18 methylation sites provided in Example 1 have high AUC values ​​for diagnosing gastric cancer and have good diagnostic performance, especially the methylation sites corresponding to the nucleotide sequences shown in Seq ID NO.4 and Seq ID NO.18, which are newly discovered high methylation sites that can be used to efficiently distinguish gastric cancer from other gastric diseases.

[0181] Secondly, the diagnostic efficacy of different combinations of methylation sites or combinations of protein markers listed in Examples 2 to 4 was compared, and the results are shown in Table 11.

[0182] Table 11. Comparison of models with different combinations of methylation sites for the diagnosis of gastric cancer

[0183]

[0184]

[0185] It can be seen from Tables 10 and 11 that the diagnostic performance of using a single methylation site as a diagnostic model is lower than that of the combined model of multiple methylation sites. The diagnostic performance of using a combination of two methylation sites as a diagnostic model is lower than that of the combined model of six methylation sites. The diagnostic performance of the multi-omics model of methylation-binding protein markers is significantly better than that of a single omic marker.

[0186] When a combination of two methylation sites was selected as the diagnostic model, the AUC-methylation and AUC-methylation+protein of Group 2 were the highest. It can be seen that when Group 2 used the two methylation sites corresponding to the nucleotide sequences shown in Seq ID NO.4 and Seq ID NO.18, the diagnostic performance was significantly higher than that of the other two methylation site combinations.

[0187] When a combination of 6 methylation sites was selected as the diagnostic model, the AUC-methylation and AUC-methylation+protein of Group 4 were the highest. It can be seen that when Group 4 used the 6 methylation sites corresponding to the nucleotide sequences shown by Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18, the diagnostic performance was significantly higher than that of the other 6 methylation site combinations.

[0188] When the combination of 18 methylation sites is selected as the diagnostic model, its diagnostic performance is very close to that of the combination of 6 methylation sites in Group 4, so the combination of 6 methylation sites in Group 4 is most preferably used.

[0189] This example further analyzes the diagnostic performance of different methylation site combinations selected from the above, and the results are shown in Table 12.

[0190] Table 12. Comparison of the preferred models of multiple methylation sites for diagnosing gastric cancer

[0191] Combination (SEQ ID NO) AUC Sensitivity Specificity Youden Index 4+ Protein 0.880 71％ 93％ 0.64 18+ Protein 0.871 70％ 93％ 0.63 4+18+protein 0.938 78％ 94％ 0.72 4+10+12+13+15+18+protein 0.971 92％ 90％ 0.82 2+4+8+11+14+15+protein 0.957 90％ 89％ 0.79 1+5+8+10+11+17+protein 0.944 88％ 89％ 0.77 3+7+12+14+15+17+protein 0.958 90％ 89％ 0.79 4+5+8+12+13+17+protein 0.960 91％ 89％ 0.80 1+3+6+8+10+11+protein 0.942 87％ 89％ 0.76 1+4+5+8+9+16+protein 0.954 88％ 90％ 0.78 1+5+7+10+13+15+protein 0.940 85％ 90％ 0.75 1~18+ protein 0.970 91％ 90％ 0.81

[0192] As can be seen from Table 12, the several preferred combinations of methylation sites + proteins in this embodiment can be used for the efficient detection of gastric cancer, with the highest AUC reaching 0.971, the sensitivity reaching 92%, and the specificity reaching 90%, truly realizing non-invasive, global, gastric cancer screening with higher sensitivity and specificity, which fully meets clinical needs.

[0193] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. Use of a reagent for detecting a combination of markers for preparing a gastric cancer detection reagent, characterized in that: The marker is a combination of nucleotide sequences comprising Seq ID NO.4 and Seq ID NO.18 or their completely complementary sequences.

2. The use according to claim 1, characterized in that The marker is a combination of the nucleotide sequences shown in Seq ID NO.4 and Seq ID NO.18 or their completely complementary sequences, or a combination of the nucleotide sequences shown in Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18 or their completely complementary sequences.

3. The use according to claim 2, characterized in that The marker is a combination of the nucleotide sequences shown in Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18 or their completely complementary sequences.

4. The use according to any one of claims 1 to 3, characterized in that The markers also include protein markers, and the protein markers consist of PGI, PGII, G17 and CEA.

5. A primer and probe combination for detecting gastric cancer, characterized in that: The primer combination includes 2 sets of primers and probe combinations as shown in the following table: 。 6. The primer-probe combination according to claim 5, characterized in that: The primer combination is 6 sets of primers and probe combinations as shown in the following table: 。 7. A kit for detecting gastric cancer, characterized in that: Comprising the primer and probe combination as described in claim 5 or 6.

8. A marker combination for detecting gastric cancer, characterized in that: It includes methylation sites and protein markers, wherein the methylation sites are a combination of the nucleotide sequences shown in Seq ID NO.4 and Seq ID NO.18 or their completely complementary sequences; the protein markers include PGI, PGII, G17 and CEA.

9. A marker combination for detecting gastric cancer, characterized in that: It includes the nucleotide sequences shown in Seq ID NO.4, Seq ID NO.10, Seq ID NO.12, Seq ID NO.13, Seq ID NO.15 and Seq ID NO.18 or a combination of their completely complementary sequences.

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