Methylated gene marker combination for early detection of endometrial cancer and use thereof, and multiplex PCR detection kit and use thereof

Abstract

Disclosed in the present invention are a methylated gene marker for early detection of endometrial cancer and a use thereof, and a multiplex PCR detection kit and a use thereof. The methylated gene marker for early detection of endometrial cancer of the present invention is selected from at least one methylated region of at least one target gene among ADARB2, ADCYAP1, ADHFE1, C17orf64, CDO1, GAD2, GRM7, GYPC, KCNA1, KCNQ5, NETO1, RYR2, SORCS3, STX16, TCTEX1D1, TMEM101, and ZSCAN12, and endometrial cancer is diagnosed by detecting a corresponding methylated region, thereby achieving sensitivity and specificity; and therefore, the diagnosis rate of high-risk groups suffering from endometrial cancer can be improved, early intervention and treatment can be realized, and the fatality rate can be reduced.

Description

Methylation gene marker combination for early detection of endometrial cancer, multiplex PCR detection kit and its application Technical Field

[0001] The present invention belongs to the field of biotechnology, and particularly relates to a methylation gene marker for early detection of endometrial cancer, a multiplex PCR detection kit and applications thereof. Background Art

[0002] Endometrial cancer, also known as uterine corpus cancer, is an epithelial malignant tumor that arises in the endometrium. It accounts for approximately 20-30% of gynecological malignancies. In recent years, its incidence has been increasing, with a clear trend of younger patients. In some developed regions, its incidence has reached the top position among gynecological malignancies.

[0003] Because endometrial cancer presents with early symptoms of irregular vaginal bleeding and discharge, approximately 70% of endometrial cancer patients are diagnosed with early-stage lesions confined to the uterine corpus. However, due to the nonspecific nature of these symptoms, a significant portion of patients miss the opportunity for early diagnosis. Early diagnosis is crucial for endometrial cancer. Early diagnosis and treatment can significantly improve patient survival and quality of life. Furthermore, with the trend of younger patients presenting with this cancer, more endometrial cancer patients are seeking fertility, and early detection offers the potential for child-conserving treatment.

[0004] Currently, there is no recommended means for routine screening of endometrial cancer. According to the current guidelines for the diagnosis and treatment of endometrial cancer, vaginal ultrasound and serum tumor markers such as CA125 are mainly used for auxiliary diagnosis in clinical practice. However, the above methods have problems with low sensitivity and / or specificity and are not suitable for population-based routine early screening of endometrial cancer. Liquid-based cytology using endometrial cell collectors can be used to screen endometrial lesions in symptomatic and high-risk populations, but it is easily affected by the operator, has limited sensitivity, and sampling is somewhat traumatic. For early screening of endometrial cancer, more accurate and non-invasive markers still need to be developed.

[0005] DNA methylation, a form of epigenetic modification, involves the addition of a methyl group to the 5th carbon atom of a cytosine base. This modification is often associated with gene silencing. DNA methylation is a key epigenetic regulator of gene expression and often leads to defective gene expression. Increased methylation of tumor suppressor genes is an early event in many tumors. Currently, methylation marker detection is a widely accepted source of cancer screening markers. Furthermore, DNA methylation testing offers the advantages of high sensitivity and specificity as a cancer screening tool. However, there is a lack of detection technologies, methods, and products for methylation detection in endometrial cancer. Therefore, there is an urgent need for a methylation-derived early screening marker that can distinguish endometrial cancer from non-cancer individuals. Such a marker should be applicable to samples of body fluids such as urine and blood, exfoliated cell samples such as swabs, swabs, and lavage fluids, and tumor tissue samples. Such a marker should be able to eliminate background signal interference in all types of non-invasively collected samples and accurately distinguish endometrial cancer from other diseased and healthy individuals. Summary of the Invention

[0006] In order to overcome the above-mentioned shortcomings and deficiencies of the prior art, the primary purpose of the present invention is to provide a methylation gene marker for early detection of endometrial cancer.

[0007] The present invention has discovered new methylated genes that can be used to diagnose endometrial cancer or its precancerous lesions: ADARB2, ADCYAP1, ADHFE1, C17orf64, CDO1, GAD2, GRM7, GYPC, KCNA1, KCNQ5, NETO1, RYR2, SORCS3, STX16, TCTEX1D1, TMEM101, and ZSCAN12. Detecting the methylation of these genes can diagnose endometrial cancer or its precancerous lesions with higher sensitivity and specificity. The present invention provides new markers and diagnostic strategies for the diagnosis of endometrial cancer and its precancerous lesions.

[0008] Another object of the present invention is to provide a multiplex PCR detection kit for early screening and diagnosis of endometrial cancer.

[0009] Another object of the present invention is to provide the use of the above-mentioned methylation gene marker in the preparation of a diagnostic reagent for endometrial cancer.

[0010] Another object of the present invention is to provide a use of the above-mentioned multiplex PCR detection kit in the preparation of endometrial cancer diagnostic products.

[0011] The purpose of the present invention is achieved through the following solutions:

[0012] A methylation gene marker for early detection of endometrial cancer, comprising at least one of the following methylation regions in at least one of the target genes ADARB2, ADCYAP1, ADHFE1, C17orf64, CDO1, GAD2, GRM7, GYPC, KCNA1, KCNQ5, NETO1, RYR2, SORCS3, STX16, TCTEX1D1, TMEM101, and ZSCAN12:

[0013] ADARB2 gene: chr10:1779052-1779183, chr10:1779756-1779910;

[0014] ADCYAP1 gene: chr18:907522-907672, chr18:908206-908385;

[0015] ADHFE1 gene: chr8:67344466-67344599;

[0016] C17orf64 gene: chr17:58498643-58498859, chr17:58498879-58499081;

[0017] CDO1 gene: chr5:115152259-115152414;

[0018] GAD2 gene: chr10:26504914-26505063; chr10:26506264-26506424; chr10:26506907-26507068;

[0019] GRM7 gene: chr3:6904068-6904274;

[0020] GYPC gene: chr2:127413900-127414152;

[0021] KCNA1 gene: chr12:5018675-5018893; chr12:5019386-5019578;

[0022] KCNQ5 gene: chr6:73331034-73331188; chr6:73331252-73331411;

[0023] NETO1 gene: chr18:70535858-70535996;

[0024] RYR2 gene: chr1:237205894-237206034;

[0025] SORCS3 gene: chr10:106400776-106400914, chr10:106401432-106401587;

[0026] STX16 gene: chr20:57225138-57225270;

[0027] TCTEX1D1 gene: chr1:67218010-67218198;

[0028] TMEM101 gene: chr17:42092213-42092341;

[0029] ZSCAN12 gene: chr6:28367209-28367379, chr6:28367431-28367676.

[0030] The present invention screens the types of genes related to endometrial cancer and the methylation regions of each gene, and ultimately screens out the above-mentioned 17 marker genes (or target genes) and their corresponding functionally optimal methylation regions. The interpretation threshold can be determined based on the methylation results of each marker gene. The results of each methylation region can be used for the early detection of endometrial cancer. The results are very accurate and can provide clinicians with auxiliary diagnostic references.

[0031] The present invention proposes new markers that can be used as diagnostic markers for endometrial cancer or its precancerous lesions, namely the above-mentioned methylated genes. Specifically, the present invention uses at least one of the target genes ADARB2, ADCYAP1, ADHFE1, C17orf64, CDO1, GAD2, GRM7, GYPC, KCNA1, KCNQ5, NETO1, RYR2, SORCS3, STX16, TCTEX1D1, TMEM101 and ZSCAN12 as a target, and diagnoses endometrial cancer by detecting the corresponding methylated regions. It has higher sensitivity and specificity and has the potential and prospects of being a marker for diagnosing or screening endometrial cancer. Therefore, at least one of the above genes can be used as a biomarker for endometrial cancer, which is of great significance for improving the diagnosis rate of high-risk populations for endometrial cancer, achieving early intervention treatment and reducing mortality.

[0032] Furthermore, endometrial cancer can be diagnosed by in vitro detection of methylation regions corresponding to at least one target gene in a sample. The sample may include body fluid samples such as urine and blood, exfoliated cell samples such as swabs, swabs, lavage fluids, and tumor tissue samples.

[0033] Furthermore, when the sample is a sample collected from exfoliated cells such as a swab, a brush, or an lavage fluid, and at least one of the above-mentioned target genes is used as a target, endometrial cancer can be diagnosed by detecting the corresponding methylation region, with a sensitivity and specificity of up to 100%.

[0034] Furthermore, when the sample is urine, using a single target gene as a diagnostic target and detecting the corresponding methylated region can still achieve a sensitivity of up to 91% and a specificity of up to 95%. Existing technologies often utilize endometrial tissue and cervical smear cells for testing. The present invention only requires a single molecular marker (i.e., a single gene) for testing urine samples, achieving high sensitivity and specificity, providing a simpler and more accurate method for detecting endometrial cancer.

[0035] Furthermore, diagnosing endometrial cancer or precancerous lesions in the form of a combination of two or more gene methylation markers can significantly improve sensitivity and specificity.

[0036] The present invention also provides detection primers for early detection of endometrial cancer, which are used to detect the methylation status of the methylation region of the above-mentioned marker gene. The nucleotide sequence of the detection primer is as follows:

[0037] ADARB2 gene:

[0038] The detection primers corresponding to chr10:1779052-1779183 are SEQ ID NOs:1-2;

[0039] The detection primers corresponding to chr10:1779756-1779910 are SEQ ID NOs:3-4;

[0040] ADCYAP1 gene:

[0041] The detection primers corresponding to chr18:907522-907672 are SEQ ID NOs:5-6;

[0042] The detection primers corresponding to chr18:908206-908385 are SEQ ID NOs:7-8;

[0043] ADHFE1 gene:

[0044] The detection primers corresponding to chr8:67344466-67344599 are SEQ ID NOs:9-10;

[0045] C17orf64 gene:

[0046] The detection primers corresponding to chr17:58498643-58498859 are SEQ ID NOs:11-12;

[0047] The detection primers corresponding to chr17:58498879-58499081 are SEQ ID NOs:13-14;

[0048] CDO1 gene:

[0049] The detection primers corresponding to chr5:115152259-115152414 are SEQ ID NOs:15-16;

[0050] GAD2 gene:

[0051] The detection primers corresponding to chr10:26504914-26505063 are SEQ ID NOs:17-18;

[0052] The detection primers corresponding to chr10:26506264-26506424 are SEQ ID NOs:19-20;

[0053] The detection primers corresponding to chr10:26506907-26507068 are SEQ ID NOs:21-22;

[0054] GRM7 gene:

[0055] The detection primers corresponding to chr3:6904068-6904274 are SEQ ID NOs:23-24;

[0056] GYPC gene:

[0057] The detection primers corresponding to chr2:127413900-127414152 are SEQ ID NOs:25-26;

[0058] KCNA1 gene:

[0059] The detection primers corresponding to chr12:5018675-5018893 are SEQ ID NOs:27-28;

[0060] The detection primers corresponding to chr12:5019386-5019578 are SEQ ID NOs:29-30;

[0061] KCNQ5 gene:

[0062] The detection primers corresponding to chr6:73331034-73331188 are SEQ ID NOs:31-32;

[0063] The detection primers corresponding to chr6:73331252-73331411 are SEQ ID NOs:33-34;

[0064] NETO1 gene:

[0065] The detection primers corresponding to chr18:70535858-70535996 are SEQ ID NOs:35-36;

[0066] RYR2 gene:

[0067] The detection primers corresponding to chr1:237205894-237206034 are SEQ ID NOs:37-38;

[0068] SORCS3 gene:

[0069] The detection primers corresponding to chr10:106400776-106400914 are SEQ ID NOs:39-40;

[0070] The corresponding detection primers for chr10:106401432-106401587 are SEQ ID NOs:41-42;

[0071] STX16 gene:

[0072] The detection primers corresponding to chr20:57225138-57225270 are SEQ ID NOs:43-44;

[0073] TCTEX1D1 gene:

[0074] The detection primers corresponding to chr1:67218010-67218198 are SEQ ID NOs:45-46;

[0075] TMEM101 gene:

[0076] The detection primers corresponding to chr17:42092213-42092341 are SEQ ID NOs:47-48;

[0077] ZSCAN12 gene:

[0078] The detection primers corresponding to chr6:28367209-28367379 are SEQ ID NOs:49-50;

[0079] The detection primers corresponding to chr6:28367431-28367676 are SEQ ID NOs:51-52.

[0080] The present invention also provides a multiplex PCR detection kit for early screening and diagnosis of endometrial cancer, which includes reagents for detecting target gene methylation;

[0081] The target gene is selected from at least one of the following genes: ADARB2, ADCYAP1, ADHFE1, C17orf64, CDO1, GAD2, GRM7, GYPC, KCNA1, KCNQ5, NETO1, RYR2, SORCS3, STX16, TCTEX1D1, TMEM101 and ZSCAN12.

[0082] The reagent includes at least one of the detection primers mentioned above.

[0083] Furthermore, the working process of the multiplex PCR detection kit may be:

[0084] (1) extracting DNA from the sample to be tested and transforming it to obtain modified DNA;

[0085] (2) The modified DNA is detected using a multiplex PCR detection kit, and the detection procedure includes two rounds of PCR reactions; the primers for the first round of multiplex PCR include a target region amplification primer fragment, a forward sequencing primer, and a sequencing connector primer fragment of a reverse sequencing primer; the primers for the second round of multiplex PCR include a fragment complementary to the sequencing primer connector fragment in the first round of PCR primers and another primer fragment containing a sequencing connector.

[0086] In the above working process, the conversion refers to achieving DNA methylation conversion; the reagents used can be conventional methylation conversion reagents, such as but not limited to bisulfite, bisulfite or hydrazine salt treatment, enzymatic conversion, etc.

[0087] Furthermore, the amplified products obtained by two rounds of PCR reactions were subjected to second-generation sequencing.

[0088] Furthermore, the length of the primer fragment for amplifying the targeted region is 26-38 bp.

[0089] Furthermore, the forward detection primer obtained in the first round of multiplex PCR includes forward sequencing primer + NNNNNNNN + forward amplification primer, and its length can be 63-75bp; the reverse detection primer includes reverse sequencing primer + NNNNNNNN + reverse amplification primer, and its length can be 69-72bp; it can also be adjusted according to the tester.

[0090] Second-generation sequencing can be performed directly using an Illumina sequencer (all sequencing modes are available, including PE150, PE250, SE150, and SE250).

[0091] The present invention also provides the use of the above-mentioned methylation gene marker in the preparation of endometrial cancer diagnosis products.

[0092] The present invention also provides the use of the multiplex PCR detection kit in preparing a diagnostic product for endometrial cancer.

[0093] The diagnostic product includes any one of a kit, a preparation and a chip.

[0094] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0095] The present invention is to screen the types of endometrial cancer-related genes and each gene methylation region, and finally screen out the above-mentioned 17 marker genes (or target genes), and their corresponding functional optimal methylation regions, which can significantly improve the sensitivity and specificity of early endometrial cancer detection. The methylation gene markers of the present invention can be used for body fluid samples such as urine and blood, exfoliated cell collection samples such as swabs, brushes, and lavage fluids, and tumor tissue samples, and can eliminate background signal interference in all types of samples and accurately distinguish endometrial cancer and other disease / healthy individual detection targets. For urine samples collected non-invasively, even if the DNA template concentration is low, the methylation gene markers of the present invention can also achieve excellent detection sensitivity and specificity, reduce unnecessary trauma sampling, have the advantages of non-invasive sampling, higher sensitivity and specificity, not only optimize the allocation of medical resources, improve the acceptance of subjects, improve the screening experience, and can ensure the accuracy of the results, to help achieve the purpose of early detection, early diagnosis, and early treatment of endometrial cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0097] FIG1 is a flow chart of a kit for multiplex PCR amplification methylation sequencing for sequencing multiple methylation regions.

[0098] Figure 2 is a flowchart of library construction for multiplex PCR amplification and methylation sequencing.

[0099] FIG3 is a diagram showing the effect of DNA fragmentation on the methylation sequencing of multiple targeted amplicon.

[0100] FIG4 is a graph showing the effect of sample starting DNA loading amount on the effect of multiplex targeted amplicon methylation sequencing.

[0101] Figure 5 shows the consistency results of retesting between multiplex targeted amplicon methylation sequencing and DNA methylation chip technology.

[0102] FIG6 is a diagram showing the methylation rate levels of gene methylation markers in endometrial cancer tissue, adjacent tissue, and non-cancerous normal tissue.

[0103] FIG7 is a diagram showing the changes in methylation rates of gene methylation markers in paired samples before and after endometrial cancer surgery. DETAILED DESCRIPTION

[0104] The present invention will be further described in detail below with reference to the examples, but the embodiments of the present invention are not limited thereto. Unless otherwise specified, the materials involved in the following examples can be obtained from commercial channels. The methods described are conventional methods unless otherwise specified.

[0105] Example 1: Screening of methylation gene markers for endometrial cancer

[0106] The present invention uses the TCGA public database to obtain 450K methylation chip data from 524 endometrial cancer tissues and 34 adjacent normal tissues, and screens for sites with significantly higher methylation in endometrial cancer tissues. The CHAMP.DMP method is then used to screen for differential sites, with the FDR-corrected P value < 0.05 and the average methylation rate of cancer tissue minus the average methylation rate of adjacent normal tissue > 0.35 as the screening criteria. A total of 8730 differential sites were screened.

[0107] In addition to collecting cells or DNA from tumors, sampling methods such as urine, cervical swabs, vaginal swabs, and blood inevitably involve the incorporation of components such as white blood cells or urothelial cells. The incorporation of these non-target exogenous DNAs may interfere with the detection of target markers as background signals. Therefore, the interference of background signals should be carefully handled when screening markers. Taking the above into account, the present invention further incorporated 78 cases of urinary tract cancer para-tissues and 338 peripheral blood leukocyte 450k methylation chip data from females in the TCGA and GEO databases, and performed CHAMP.DMP difference tests with 425 cases of endometrial cancer tissue data. Sites that simultaneously met the following conditions were included in the screening range: (1) P value after FDR correction of two difference tests < 0.05; (2) average methylation rate of cancer tissue minus average methylation rate of urinary tract adjacent tissue > 0; (3) average methylation rate of cancer tissue minus average methylation rate of peripheral blood leukocytes > 0; (4) average methylation rate of urinary tract adjacent tissue < 0.1; (5) average methylation rate of peripheral blood leukocytes < 0.1. Sites that met the above requirements were intersected with the differential sites in the endometrial cancer / adjacent tissue differential screening, and a total of 1464 sites were included in the next screening process.

[0108] To further evaluate the methylation levels of these 1,464 candidate loci in real-world samples, the present invention performed Illumina Epic 850k microarray analysis on exfoliated cell samples collected from 12 endometrial cancer patients and 15 controls at the Sun Yat-sen University Cancer Center and Foshan First People's Hospital. This microarray is an upgraded version of the 450k microarray, retaining most of the 450k microarray sites while adding nearly 400,000 new methylation sites. Ultimately, 17 genes were selected, including C17orf64, CDO1, KCNQ5, STX16, ZSCAN12, KCNA1, ADARB2, SORCS3, GAD2, RYR2, GRM7, ADCYAP1, NETO1, GYPC, TMEM101, ADHFE1, and TCTEX1D1. The methylation sites with the best diagnostic efficacy for endometrial cancer and their effectiveness are shown in Table 1.

[0109] The areas under the curve (AUCs) for the optimal sites of the 17 genes ranged from 0.867 to 0.983. Seven genes, including C17orf64, CDO1, KCNQ5, STX16, ZSCAN12, KCNA1, and ADARB2, achieved AUCs greater than 0.98, while seven genes, including SORCS3, GAD2, RYR2, GRM7, ADCYAP1, NETO1, and GYPC, achieved AUCs between 0.90 and 0.95. Different genes exhibited varying strengths in sensitivity and specificity. Sensitivity reached 100% for CDO1, KCNA1, and TCTEX1D1, while specificity reached 100% for C17orf64, KCNQ5, STX16, and ZSCAN12. Sensitivity of these 17 gene methylation markers, with the exception of GRM7 and GAD2, exceeded 80%, and specificity exceeded 80% for all but TCTEX1D1. Overall, the sensitivity of C17orf64, KCNQ5, STX16 and ZSCAN12 genes can reach 91.7% when the specificity is 100%; the specificity of KCNA1 gene can reach 93.3% when the sensitivity is 100%. The above genes show excellent discrimination ability between endometrial cancer and controls.

[0110] In addition, sites with strong discrimination ability appear in clusters around the optimal site. Within 500bp upstream and downstream of the optimal site, the above genes have 1-8 sites with AUC greater than 0.8, of which 14 genes have at least 3 or more sites with AUC higher than 0.8. Genes with less than 3 sites with AUC>0.8 are also very likely due to the low probe coverage density and too few detection sites when the Epic 850k chip was designed for these genes. The appearance of multiple site clusters further demonstrates the certainty of the above gene methylation as a diagnostic marker for endometrial cancer and is conducive to the development of methylation site detection methods. The results show that the 17 genes of the present invention have excellent discrimination ability in endometrial cancer and control populations.

[0111] Table 1

[0112] Note: A* is the number of sites with AUC>0.8 within 500 bp upstream and downstream of the optimal site.

[0113] Example 2: Construction of multiplex PCR amplification methylation sequencing technology to detect marker methylation signals

[0114] For the 17 genes screened in Example 1, a DNA amplicon targeted methylation sequencing method was established. Targeted PCR amplification was performed on bisulfite-converted DNA and detected by next-generation sequencing to obtain the methylation levels of candidate markers in the sample. The flow chart is shown in Figure 1. The specific steps are as follows:

[0115] 2.1 Design of multiplex PCR primers for target regions

[0116] Determination of target regions: Sites located on CpG islands often appear continuously. To determine the optimal range for sequence amplification, the present invention first selects the optimal amplification region for the 17 candidate differentially methylated sites on a gene-by-gene basis. Using the site with the best area under the curve (AUC) of the receiver operating characteristic curve for that gene as the center, the methylation rates of all 850k microarray-contained methylated sites on that gene in exfoliated cells and endometrial cancer tissue data are extracted. The region with the highest density of methylated sites (150-350bp) and the most significant differences in exfoliated cells and tissues is selected for the next step of primer design.

[0117] Design of target region PCR primers: DNA methylation-specific PCR primer design software, such as MethPrimer, was used to design primers for each sequence. Designed primers were paired and compared for compatibility to minimize primer dimers and hairpin structures between multiple primer pairs and improve the success rate of primer selection. After repeated adjustments, a total of 26 primer pairs were designed for multiplex PCR testing of 17 target genes. Primer sequences, chromosomal locations of the amplified regions, and target gene information are shown in Table 2. "F" denotes a forward amplification primer, and "R" denotes a reverse amplification primer.

[0118] Table 2

[0119] 2.2 Two rounds of PCR amplification to construct amplicon library of target fragment and sequence it

[0120] Urine sample pretreatment and DNA extraction were performed using ZYMO Quick-DNA TM Urine Kit, and DNA bisulfite conversion was performed using the ZYMO EZ DNA Methylation-Gold Kit. For DNA samples after bisulfite conversion, the present invention established a multiplex PCR primer system targeting 26 fragments of 17 target genes, which can cumulatively detect 280 methylation sites. The method is divided into two rounds of PCR: the primers for the first round of multiplex PCR are divided into two parts: a 26-38bp target region amplification primer fragment and a 29bp (forward primer) and 26bp (reverse primer) sequencing adapter primer fragment; the length of the obtained first-round multiplex forward PCR primers is 63-75bp, and the length of the reverse PCR primers is 69-72bp. The primers for the second round of multiplex PCR contain a fragment complementary to the sequencing primer adapter fragment in the first-round PCR primers and another primer fragment containing a sequencing adapter. The amplification products after two rounds of PCR can be directly sequenced using an Illumina sequencer for second-generation sequencing (PE150, PE250, SE150, SE250 and other sequencing modes are all acceptable). The design principle of the two-round PCR primers is shown in Figure 2.

[0121] The specific steps are as follows:

[0122] (1) First round of PCR amplification

[0123] The first round of PCR uses a multiplex PCR amplification system, with two amplification primers: a sequencing primer fragment + 8 free bases + an amplification primer fragment. This kit covers the detection of 28 methylation regions, including the 17 genes described in this application. The sequences of the amplification primer fragments for each amplification region are shown in Table 2. Basic information about the methylation detection regions for each gene, the reference genomic location of the detection sequence, the detection sequence length, and the reference genomic sequence of the detection region are shown in Table 3.

[0124] Table 3

[0125] The design ideas of the primer sequences for the first round of multiplex PCR primers are shown in Table 4. The overall composition of the primers is "sequencing primer + NNNNNNNN + amplification primer". The forward sequencing primer sequence is 5'-AATGATACGGCGACCACCGAGATCTACAC-3'; eight variable bases "NNNNNNNN" are added in the middle as the barcode sequence to distinguish the sequence sample information obtained by sequencing; and then the forward amplification primers for methylation of each gene are connected, see the F primers in Table 2 for details. The reverse sequencing primer sequence is 5'-CAAGCAGAAGACGGCATACGAGATTC-3'; eight variable bases "NNNNNNNN" are added in the middle as the barcode to distinguish the sequence sample information obtained by sequencing; and then the reverse amplification primers for methylation of each gene are connected, see the R primers in Table 2 for details. The present invention mixes the first round of multiplex PCR primers of each amplification region in equal proportions as PCR primers, uses bisulfite-converted DNA as template DNA, and uses KAPA HiFi Mix as DNA polymerase for amplification. The number of amplification cycles depends on the quality and concentration of the DNA template and should be kept within 15-35 cycles. The PCR reaction system consists of (per well): 2 μL 5x KAPA HiFi buffer; 0.3 μL 10 mM dNTPs; 0.5 μL DMSO; 0.2 μL KAPA HiFi Polymerase; 1 μL forward sequencing primer (5 μM); 1 μL reaction sequencing primer (5 μM); 3 μL enzyme-free water; 2 μL converted DNA. PCR reaction conditions: 95°C for 5 minutes; then 98°C for 20 seconds, 50-60°C for 30 seconds, and 72°C for 1 minute for 15-35 cycles; finally, hold at 4°C.

[0126] Table 4

[0127] (2) Second round of PCR amplification and purification

[0128] The first-round PCR amplification product was purified using AMPure XP magnetic beads, and the purified nucleic acid was used as the template DNA for the second-round PCR. A second round of amplification was performed using KAPA HiFi Mix as the DNA polymerase for amplification. The amplification annealing temperature and extension time were determined according to the specific situation of the product, and the number of cycles was controlled within 10-20 cycles. The forward primer sequence for the second round of amplification is CAAGCAGAAGACGGCATACGAGATNNNNNNNNGTCTCGTGGGCTCGG, and the reverse primer sequence is AATGATACGGCGACCACCGAGATCTACACNNNNNNNNTCGTCGGCAGCGTC, where "NNNNNNNN" is the sequencer index sequence. It should be noted that the first-round PCR sequencing primer and the second-round sequencing primer can be formulated and modified according to the specific requirements of different brands and specifications of sequencers. The sequencing primers provided in the present invention only provide an example of one sequencing primer; the use of other sequencing primers that meet the requirements of the corresponding sequencer will not affect the detection results of this kit. The PCR reaction system composition (per well): 2 μL 5x KAPA HiFi buffer; 0.3 μL 10 mM dNTPs; 0.5 μL DMSO; 0.2 μL KAPA HiFi Polymerase; 1 μL forward sequencing primer (5 μM); 1 μL reaction sequencing primer (5 μM); 3 μL enzyme-free water; 2 μL diluted DNA from the first-round PCR product. PCR reaction conditions: 95°C for 5 minutes, followed by 10-20 cycles of 98°C for 20 seconds, 50-60°C for 30 seconds, and 72°C for 1 minute; and finally, a 4°C hold.

[0129] (3) Amplification product mixing and quality evaluation

[0130] The second-round PCR amplification products were purified using AMPure XP magnetic beads, and the purified DNA was quantified using the Qubit dsDNA high sensitivity assay kit and the qPCR method. After the amplification products of different samples were mixed with equal molar mass, second-generation sequencing was performed on the corresponding sequencer.

[0131] (4) Bioinformatics analysis of sequencing data

[0132] After the sequencing data is downloaded, fastqc software is used to check the data quality, and the software Trimmomatic is used to remove sequencing adapters and low-quality bases and sequences to obtain qualified sequences after quality control. Subsequently, basmap methylation sequencing data comparison software is used to compare and analyze the qualified sequences and the target sequences in the amplified region. Then, the methylation level of the CG base on each target sequence of each sample is calculated. The methylation level calculation formula is: methylation level of C site = number of sequences supporting methylation / (number of sequences supporting methylation + number of sequences supporting non-methylation); According to the above formula, the methylation rate level of all methylation sites in the detection area of ​​each sample can be obtained. For sequences where the sequencing depth of a certain site is less than 50X, the methylation rate of the site in the sample is defined as a missing value NA and is no longer included in the calculation.

[0133] (5) Evaluation of the effect of targeted amplicon methylation sequencing

[0134] ①The effect of DNA fragmentation on multiplex PCR amplification

[0135] Because DNA from samples such as exfoliated cells often carries the risk of nucleic acid degradation and fragmentation, the present invention tested five samples with varying degrees of fragmentation to observe the impact of DNA fragmentation on the test results of the kit. The results showed that for severely fragmented samples, the percentage of qualified sites detected by the targeted methylation sequencing technology of the present invention exceeded 99%, demonstrating that the detection method of the present invention is largely unaffected by DNA fragmentation and can still produce reliable detection data even in the presence of severe fragmentation (see Figure 3 and Table 5).

[0136] Table 5

[0137] ②The impact of sample loading on multiplex PCR amplification and sequencing

[0138] Since samples collected non-invasively such as exfoliated cells have large individual sampling heterogeneity, there is often a situation where the amount of nucleic acid obtained by the sample is insufficient. Therefore, the present invention tests the detection effect under different sample starting DNA loading amounts. The results show that even when the DNA input amount is less than 200ng, the average percentage of qualified sites detected by the targeted methylation sequencing technology can still reach 98%, and there is no statistical difference between the percentage of qualified sites grouped with a higher sample starting DNA loading amount (Figure 4). This shows that the detection method of the present invention can still obtain sufficient data when only a small amount of nucleic acid is obtained from the sample to be tested.

[0139] ③Retest consistency between multiplex targeted amplicon methylation sequencing and DNA methylation array technology

[0140] There will be certain deviations in the detection results of methylation sites by different detection methods. In order to evaluate the effect of the multiplex targeted amplicon methylation sequencing constructed in this application on the true methylation level in the sample, 3 samples that have been tested for multiplex targeted amplicon methylation sequencing were tested simultaneously using the Illumina Epic 850K methylation chip. The Epic 850K methylation chip is one of the widely used and highly recognized DNA methylation detection methods. By comparing the methylation rates of the methylation sites detected by both methods, the correlation between the methylation rates of the two detection methods of the three retested samples was calculated. The results showed that the retest consistency of the two detection methods for the three samples was good, and the correlation coefficient R 2 The values ​​of 0.878, 0.941 and 0.851 were respectively, showing a high positive correlation ( FIG5 ), which indicates that the detection method of the present invention can reflect the true methylation signal in the sample.

[0141] In summary, the multiplex targeted amplicon methylation sequencing detection method of the present invention is compatible with common sample problems such as severe DNA fragmentation and low DNA input, can reflect the true methylation rate level in the sample, and has the feasibility of being developed into a detection kit, especially when detecting samples with large individual heterogeneity such as exfoliated cells and body fluid samples.

[0142] Example 3: Diagnostic Effect of Methylation Markers in Urine Samples for Endometrial Cancer

[0143] Compared to blood and tissue samples, urine samples are completely non-invasive. Compared to cervical swabs and vaginal swabs, urine samples are less susceptible to individual sampling techniques and do not require medical assistance. This sampling method is highly accepted by the public and can be collected at home. The test samples in this example were urine collected from 107 endometrial cancer patients and 111 control subjects at the Sun Yat-sen University Cancer Center and the Affiliated Cancer Hospital of Guangzhou Medical University.

[0144] The sample was extracted using the detection method constructed in Example 2 of the present application, subjected to DNA bisulfite modification, constructed into a targeted methylation amplification library, and sequenced on the Illumina platform. The sequencing data was subjected to the above-mentioned biological information analysis process to obtain the methylation levels of the methylation sites within the amplification region of the 17 genes. After the data quality control process, 280 site information from 17 genes was included in the analysis. Since the data was quality controlled and filtered according to the sequencing depth of each gene, sites with a sequencing depth of less than 50 sequences were defined as missing. After this filtration, the number of cases ultimately included in the analysis of each site ranged from 97 to 106 cases, and the number of controls ranged from 105 to 111 cases.

[0145] Table 6 shows the most effective methylation sites for diagnosing endometrial cancer in independent samples using targeted amplification methylation sequencing for the genes covered by the present invention. The number of methylation sites detected in the 17 genes ranged from 3 to 35. Logistic modeling of methylation sites within each gene yielded an area under the curve (AUC) between 0.788 and 0.886. Three gene methylation markers, including GAD2, ADCYAP1, and C17orf64, had an AUC greater than 0.85; 16 markers, excluding CDO1, had an AUC greater than 0.8. Similar to the results in Example 1, CDO1 demonstrated excellent sensitivity of 91.3%, while GAD2 and GRM7 exhibited sensitivities exceeding 80%, and KCNA1 and ADCYAP1 exhibited specificities exceeding 90%. The AUC for the most effective methylation sites in the 17 gene methylation detection regions ranged from 0.730 to 0.814.

[0146] Table 6

[0147] Note: A is the sample size of the case group included in the modeling; B is the sample size of the control group included in the modeling; AUC 1 The area under the curve for modeling all sites of the gene; C is the number of methylation sites identified in the amplified region; AUC 2 is the area under the curve of the methylation site with the best prediction effect.

[0148] Among the 17 gene methylation markers of the present invention, GAD2, ADCYAP1 and C17orf64 have the best comprehensive performance. GAD2 and others have advantages in sensitivity, and KCNA1 and ADCYAP1 and others have advantages in specificity. Therefore, the diagnostic efficacy of the combination markers for 107 cases of endometrial cancer and 111 controls was further evaluated. Table 7 shows the discrimination effect of the combination of two-gene, three-gene and four-gene markers. Specifically, when the two gene methylation markers are combined, the AUC of the combination of GAD2 and GYPC is the highest, reaching 0.928, with a sensitivity of 82.2% and a specificity of 86.8%, which is significantly improved compared to the effect of a single gene methylation marker. The remaining genes can reach 0.9 when used in pairs, showing excellent combined diagnostic performance. When combining three gene methylation markers, the combined C17orf64, GAD2, and GYPC markers achieved the highest AUC, reaching 0.948, with a sensitivity of 85.4% and a specificity of 91.2%. This model significantly improved sensitivity and accuracy compared to the two-marker model. Specifically, the AUC for combining GAD2 and GYPC with C17orf64, ADCYAP1, TMEM101, and ZSCAN12 reached over 0.94. Combining ADARB2, CDO1, GRM7, KCNA1, NETO1, SORCS3, STX16, and TCTEX1D1 with C17orf64, GAD2, or GYPC also reached an AUC of 0.93. The diagnostic performance of the four-marker combination was further improved. The ADCYAP1, GAD2, GYPC, and TMEM101 combination achieved an AUC of 0.959, a sensitivity of 93.9%, and a specificity of 86.1%. In the optimal four-marker combination model, GAD2, GYPC, and TMEM101 genes appeared most frequently and were key genes in the combination model. The AUC for the combination of GAD2, GYPC, ADCYAP1, TMEM101, C17orf64, ADHFE1, ZSCAN12, TCTEX1D1, STX16, RYR2, KCNQ5, SORCS3, RYR2, and KCNA1 was close to 0.95, demonstrating complementarity among the genes.

[0149] Table 7

[0150] When five methylation gene markers were included, the AUCs of all combinations were higher than 0.857. The optimal combination was GAD2, GYPC, ADCYAP1, TMEM101 and STX16, with an AUC of 0.960. The AUC of the GAD2, GYPC, ADCYAP1, C17orf64 and ZSCAN12 gene combination was also 0.960. A total of 130 combinations had AUCs higher than 0.95, and 17 gene markers among the 130 combinations were included in the model multiple times, fully reflecting the importance of each gene in the high-precision model.

[0151] When the six-gene combination markers were used, the AUCs of all combinations were higher than 0.872. The optimal combination was ADCYAP1, C17orf64, GAD2, GYPC, TCTEX1D1, and ZSCAN12, with an AUC of 0.962. Among them, the AUCs of all the top 100 combinations were higher than 0.956. All 17 gene markers were included in the model multiple times, fully reflecting the importance of each gene in the high-precision model.

[0152] When the seven-gene combination markers were used, the AUCs of all combinations were higher than 0.884. The optimal combination was ADCYAP1, C17orf64, GAD2, GYPC, TCTEX1D1, TMEM101, and ZSCAN12, with an AUC of 0.963. The combination of ADARB2, ADCYAP1, GAD2, GYPC, STX16, TCTEX1D1, and TMEM101 also achieved the same diagnostic effect. Among the seven-gene combinations, the AUCs of all the top 100 combinations were higher than 0.960. All 17 gene markers were included in the model multiple times, fully reflecting the importance of each gene in the high-precision model.

[0153] When the eight-gene combination markers were used, the AUCs of all combinations were higher than 0.888. The optimal combination was ADCYAP1, C17orf64, GAD2, GYPC, RYR2, TCTEX1D1, TMEM101 and ZSCAN12, with an AUC of 0.964. Among the eight-gene combinations, the AUCs of all the top 100 combinations were higher than 0.961. All 17 gene markers were included in the model multiple times, fully reflecting the importance of each gene in the high-precision model.

[0154] In summary, the methylated gene markers of the present invention, or combinations thereof, can effectively differentiate between endometrial cancer patients and controls and can be used to prepare diagnostic kits for endometrial cancer. Furthermore, using a combination of two or more gene methylation markers as described above can significantly improve sensitivity and specificity in diagnosing endometrial cancer or precancerous lesions.

[0155] It is worth noting that among the 100 cases of endometrial cancer with clear pathological staging in the case group, 73% were in stage I (very early stage), 19% were in stage II, and only 8% were in stages III and IV (middle and late stages). This demonstrates that the above-mentioned markers have excellent performance in detecting early endometrial cancer and can be used for the early diagnosis of endometrial cancer.

[0156] Example 4: Diagnostic Effect of Methylation Markers on Endometrial Cancer in Cervical Swab Samples

[0157] To evaluate the diagnostic value of the 17 markers included in Example 1 for endometrial cancer in cervical swab samples, the test samples in this example were cervical swabs collected from 17 endometrial cancer patients and 14 control participants at the Sun Yat-sen University Cancer Center and Foshan First People's Hospital. Cervical swabs are a relatively non-invasive sampling method that can be self-sampled, and have the advantage of being a non-invasive diagnostic sampling sample. The samples were extracted using the detection method constructed in Example 2 of the present application, subjected to DNA bisulfite modification, and constructed into a targeted methylation amplification library, which was then sequenced on the Illumina platform. The sequencing data was subjected to the above-mentioned bioinformatics analysis process to obtain the methylation levels of the methylation sites within the amplification regions of the 17 genes. After the data quality control process, information on 280 sites from 17 genes was included in the analysis. Since the data was quality controlled and filtered according to the sequencing depth of each gene, sites with a sequencing depth of less than 50 sequences were defined as missing. After this filtering, the number of cases ultimately included in the analysis for each site ranged from 14 to 17 cases, and the number of controls ranged from 13 to 14 cases.

[0158] The diagnostic effects of the genes included in this application on endometrial cancer in cervical swab samples are shown in Table 8. The number of methylation sites detected in the 17 genes is between 3 and 35. By performing logistic model modeling on the methylation sites in the same gene, the optimal cutoff value is taken to evaluate the discrimination accuracy of each gene for the case group and the control group individuals. Among them, 11 gene methylation markers can achieve completely accurate discrimination between endometrial cancer and control individuals. These genes include ADARB2, ADCYAP1, C17orf64, GAD2, GYPC, KCNA1, KCNQ5, SORCS3, STX16, TMEM101 and ZSCAN12. In addition, GRM7, TCTEX1D1, CDO1 and NETO1 are completely correct in discriminating the control group, and RYR2 is completely correct in discriminating the case group. However, the specificity of these two gene methylation markers can reach 100%, which has a very good effect in identifying the control group. The above results show that when cervical swab samples are used as test samples, the 17 gene methylation markers in this application have excellent diagnostic ability for endometrial cancer.

[0159] Table 8

[0160] Note: Item A is the sample size of the case group included in the modeling; Item B is the sample size of the control group included in the modeling; Item C is the correct identification of the case group, samples / total samples; Item D is the correct identification of the control group, samples / total samples; Item E is the number of methylation sites identified in the amplified region; AUC 1 The area under the curve for modeling all loci of the gene; AUC 2 is the area under the curve of the methylation site with the best prediction effect.

[0161] Example 5: Diagnostic Effect of Methylation Markers on Endometrial Cancer in Vaginal Swab Samples

[0162] In order to evaluate the diagnostic value of the 17 markers included in Example 1 for endometrial cancer in cervical swab samples, the test samples in this example are vaginal swabs collected from 17 endometrial cancer patients and 14 control participants at the Sun Yat-sen University Cancer Center and Foshan First People's Hospital. Compared with cervical swabs, vaginal swab sampling is shallower and requires less assistance from medical staff. It is one of the more non-invasive and simpler sampling methods. The samples were extracted using the detection method constructed in Example 2 of the present application, DNA was modified with bisulfite, a targeted methylation amplification library was constructed, and sequencing was performed on the Illumina platform. The sequencing data was subjected to the above-mentioned bioinformatics analysis process to obtain the methylation levels of the methylation sites within the amplification regions of the 17 genes. After the data quality control process, 280 site information from 17 genes was included in the analysis. Since the data were quality-controlled and filtered based on the sequencing depth of each gene, sites with a sequencing depth of less than 50 sequences were defined as missing. After this filtering, the number of cases finally included in the analysis for each site ranged from 15 to 17, and the number of controls ranged from 11 to 14.

[0163] The diagnostic effects of the genes included in this application on endometrial cancer in cervical swab samples are shown in Table 9. The number of methylation sites detected in the 17 genes is between 3 and 35. By performing logistic modeling on the methylation sites in the same gene, the area under the curve is between 0.874 and 1. Among them, 13 gene methylation markers can achieve completely accurate discrimination between endometrial cancer and control individuals, with an area under the curve of 1, and a sensitivity and specificity of 100%. These gene methylation markers include ADARB2, ADCYAP1, C17orf64, GAD2, GYPC, KCNA1, KCNQ5, RYR2, SORCS3, STX16, TCTEX1D1, TMEM101 and ZSCAN12. In addition, the AUC of GRM7 and CDO1 genes is greater than 0.95, showing excellent diagnostic efficacy. The AUCs for NETO1 and ADHFE1 were 0.929 and 0.874, respectively. The specificity of these two gene methylation markers reached 100%, demonstrating excellent diagnostic ability for controls. These results demonstrate that the 17 gene methylation markers in this application have excellent diagnostic capabilities for endometrial cancer when vaginal swab samples are used as test samples.

[0164] Table 9

[0165] Note: Item A is the sample size of the case group included in the modeling; Item B is the sample size of the control group included in the modeling; Item C is the correct identification of the case group, samples / total samples; Item D is the correct identification of the control group, samples / total samples; Item E is the number of methylation sites identified in the amplified region; AUC 1 The area under the curve for modeling all loci of the gene; AUC 2 is the area under the curve of the methylation site with the best prediction effect.

[0166] Example 6: Detection Effect of Methylation Markers in Endometrial Cancer Tissue and Para-Cancerous Specimens

[0167] In order to evaluate the signals of the 17 markers included in Example 1 in endometrial cancer tissue, the test samples of this embodiment are 88 cases of endometrial cancer tissue from endometrial cancer patients, 34 cases of paracancerous tissue from endometrial cancer patients, and 20 cases of normal endometrial tissue from patients with benign fibroids collected at the Cancer Prevention and Treatment Center of Sun Yat-sen University and the First People's Hospital of Foshan. The methylation signal in the endometrial cancer tissue can reflect the true situation of the primary lesion and provide reliable verification for the marker signal in this application. The sample was extracted using the detection method constructed in Example 2 of this application, DNA was modified with bisulfite, a targeted methylation amplification library was constructed, and sequencing was performed on the Illumina platform. The sequencing data was subjected to the above-mentioned bioinformatics analysis process to obtain the methylation levels of the methylation sites within the amplification region of the 17 genes. After the data quality control process, 280 site information from 17 genes was included in the analysis.

[0168] The present invention comprehensively compared the methylation rate differences of the 17 gene methylation markers in this application between cancer and adjacent tissues, as well as between cancer and normal tissues, and used the signed rank sum test to analyze the differences between the groups. The results showed that among the 280 methylation sites covered by the 17 genes, the methylation rate of cancer tissues was significantly higher than that of normal tissues (P value range: 6.4×10 -11 -6.8×10 -3 , median P value: 5.4×10 -9 ); 246 of these sites had significantly higher methylation rates in cancer tissue than in adjacent tissues, accounting for 84%. Figure 6 shows the distribution of methylation rates of representative methylation sites in 17 genes in normal, adjacent, and cancer tissues. As shown in the figure, the methylation rates of each methylation site in cancer tissue were significantly higher than those in normal and adjacent tissues, especially in genes such as GAD2, GRM7, SORCS3, TMEM101, KCNA1, GYPC, CDO1, and C17orf64, where the P value of the difference between cancer and adjacent tissues was less than 1×10 -9 .

[0169] Example 7: Comparison of Changing Trends of Methylation Markers Before and After Surgery

[0170] If the markers in this application can accurately reflect the presence of endometrial cancer lesions, then the methylation signal will theoretically decrease significantly or even disappear after the lesions are surgically removed. In order to further increase the authenticity evidence of the markers at this level, this embodiment collected urine samples from 36 patients before and 3-5 days after surgery at the Sun Yat-sen University Cancer Center and Foshan First People's Hospital for dynamic monitoring of the markers in this application. Since endometrial cancer surgery often involves complete hysterectomy, it would be inappropriate to collect cervical swabs and vaginal swabs from postoperative patients. Therefore, a more non-invasive urine sample is used as the sample type for monitoring. Because the tumor-related methylation signal released by the lesion after tumor resection will be terminated, the methylation signal of the postoperative patient should theoretically be reduced.

[0171] The methylation rate levels of the 17 gene methylation markers in this application in the preoperative and postoperative paired samples of the same patient were comprehensively compared, and the signed rank and paired test was used to analyze the differences between the groups. The results showed that among the 280 methylation sites covered by the detection of 17 gene methylation markers, the methylation rate of cancer tissues at 235 sites was significantly higher than that of adjacent cancer tissues, accounting for 80%. Figure 7 shows the distribution of methylation rates of representative methylation sites in 17 genes in preoperative and postoperative samples. As shown in the figure, compared with preoperative samples, the methylation rates of various methylation sites in postoperative samples were significantly decreased. Except for STX16, the representative sites of the remaining 15 genes all reached a significant level, with a P value of 1.7×10 -3 ~1.7×10 -5 Overall, the differences in methylation sites such as ADHFE1, TMEM101, KCNA1, ADCYAP1, and GYPC before and after surgery were particularly significant, with P values ​​less than 1×10 -4 level.

[0172] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A methylation gene marker for early detection of endometrial cancer, characterized in that At least one of the following methylation regions in at least one of the target genes ADARB2, ADCYAP1, ADHFE1, C17orf64, CDO1, GAD2, GRM7, GYPC, KCNA1, KCNQ5, NETO1, RYR2, SORCS3, STX16, TCTEX1D1, TMEM101 and ZSCAN12: ADARB2 gene: chr10:1779052-1779183, chr10:1779756-1779910; ADCYAP1 gene: chr18:907522-907672, chr18:908206-908385; ADHFE1 gene: chr8:67344466-67344599; C17orf64 gene: chr17:58498643-58498859, chr17:58498879-58499081; CDO1 gene: chr5:115152259-115152414; GAD2 gene: chr10:26504914-26505063; chr10:26506264-26506424; chr10:26506907-26507068; GRM7 gene: chr3:6904068-6904274; GYPC gene: chr2:127413900-127414152; KCNA1 gene: chr12:5018675-5018893; chr12:5019386-5019578; KCNQ5 gene: chr6:73331034-73331188; chr6:73331252-73331411; NETO1 gene: chr18:70535858-70535996; RYR2 gene: chr1:237205894-237206034; SORCS3 gene: chr10:106400776-106400914, chr10:106401432-106401587; STX16 gene: chr20:57225138-57225270; TCTEX1D1 gene: chr1:67218010-67218198; TMEM101 gene: chr17:42092213-42092341; ZSCAN12 gene: chr6:28367209-28367379, chr6:28367431-28367676.

2. A detection primer for early detection of endometrial cancer, characterized by: For detecting the methylation status of the marker gene methylation region according to claim 1, the nucleotide sequence of the detection primer is as follows: ADARB2 gene: The detection primers corresponding to chr10:1779052-1779183 are SEQ ID NOs:1-2; The detection primers corresponding to chr10:1779756-1779910 are SEQ ID NOs:3-4; ADCYAP1 gene: The detection primers corresponding to chr18:907522-907672 are SEQ ID NOs:5-6; The detection primers corresponding to chr18:908206-908385 are SEQ ID NOs:7-8; ADHFE1 gene: The detection primers corresponding to chr8:67344466-67344599 are SEQ ID NOs:9-10; C17orf64 gene: The detection primers corresponding to chr17:58498643-58498859 are SEQ ID NOs:11-12; The detection primers corresponding to chr17:58498879-58499081 are SEQ ID NOs:13-14; CDO1 gene: The detection primers corresponding to chr5:115152259-115152414 are SEQ ID NOs:15-16; GAD2 gene: The detection primers corresponding to chr10:26504914-26505063 are SEQ ID NOs:17-18; The detection primers corresponding to chr10:26506264-26506424 are SEQ ID NOs:19-20; The detection primers corresponding to chr10:26506907-26507068 are SEQ ID NOs:21-22; GRM7 gene: The detection primers corresponding to chr3:6904068-6904274 are SEQ ID NOs:23-24; GYPC gene: The detection primers corresponding to chr2:127413900-127414152 are SEQ ID NOs:25-26; KCNA1 gene: The detection primers corresponding to chr12:5018675-5018893 are SEQ ID NOs:27-28; The detection primers corresponding to chr12:5019386-5019578 are SEQ ID NOs:29-30; KCNQ5 gene: The detection primers corresponding to chr6:73331034-73331188 are SEQ ID NOs:31-32; The detection primers corresponding to chr6:73331252-73331411 are SEQ ID NOs:33-34; NETO1 gene: The detection primers corresponding to chr18:70535858-70535996 are SEQ ID NOs:35-36; RYR2 gene: The detection primers corresponding to chr1:237205894-237206034 are SEQ ID NOs:37-38; SORCS3 gene: The detection primers corresponding to chr10:106400776-106400914 are SEQ ID NOs:39-40; The corresponding detection primers for chr10:106401432-106401587 are SEQ ID NOs:41-42; STX16 gene: The detection primers corresponding to chr20:57225138-57225270 are SEQ ID NOs:43-44; TCTEX1D1 gene: The detection primers corresponding to chr1:67218010-67218198 are SEQ ID NOs:45-46; TMEM101 gene: The detection primers corresponding to chr17:42092213-42092341 are SEQ ID NOs:47-48; ZSCAN12 gene: The detection primers corresponding to chr6:28367209-28367379 are SEQ ID NOs:49-50; The detection primers corresponding to chr6:28367431-28367676 are SEQ ID NOs:51-52.

3. Use of a reagent for detecting methylation regions of marker genes in the preparation of a detection kit or device, characterized in that The detection kit or device is used for detecting, screening or diagnosing endometrial cancer; the marker gene methylation region is the marker gene methylation region according to claim 1.

4. The use according to claim 3, characterized in that: The reagent comprises at least one of an antibody, a probe, a primer and a mass spectrometry detection reagent specifically for detecting the methylation region of the marker gene, wherein the primer comprises the detection primer according to claim 2.

5. The use according to claim 3 or 4, characterized in that: The detection sample of the detection kit or device includes at least one of a body fluid sample, an exfoliated cell collection sample and a tumor tissue sample.

6. The use according to claim 5, characterized in that: The body fluid sample includes at least one of urine and blood; the exfoliated cell collection sample includes at least one of a swab, a brush and an lavage fluid.

7. A multiplex PCR detection kit for early screening and diagnosis of endometrial cancer, characterized in that comprising a reagent for detecting the methylation region of the marker gene according to claim 1; The reagent comprises at least one of an antibody, a probe, a primer and a mass spectrometry detection reagent specifically for detecting the methylation region of the marker gene, wherein the primer comprises the detection primer according to claim 2.

8. Use of the methylation gene marker according to claim 1 in the preparation of a diagnostic product for endometrial cancer.

9. Use of the multiplex PCR detection kit according to claim 7 in the preparation of a diagnostic product for endometrial cancer.

10. The use according to claim 8 or 9, characterized in that: The diagnostic product includes any one of a kit, a preparation and a chip.

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