A polymethylated urothelial carcinoma early screening system and model construction method

Abstract

The present invention relates to the field of gene detection technology, and specifically to a polymethylated urothelial carcinoma early screening system and a model construction method, comprising an acquisition module for acquiring tissue specimens to be screened and adjacent cancer specimens; a gene screening module for performing RRBS comparative sequencing on the specimens acquired by the acquisition module to screen out specific marker genes with high specificity and high methylation levels; a gene verification module for verifying the marker genes screened initially by using BSP sequencing / MSP amplification technology; a urothelial carcinoma screening module for automatically outputting screening results by constructing a multi-gene methylation detection kit and combining it with a statistical model, thereby realizing urothelial carcinoma screening; and further improving its sensitivity and specificity by combining multi-gene methylation with a specific algorithm model, thereby overcoming the shortcomings of low sensitivity of a single gene marker and low specificity after combining multiple gene markers.

Description

Technical Field

[0001] The present invention relates to the technical field of early screening detection of urothelial cancer, and specifically to a polymethylated urothelial cancer early screening system and a model construction method. Background Art

[0002] Urothelial carcinoma (UC) is one of the most common malignant tumors in the urogenital system. It can occur in a wide range of areas and can occur in any area covered with urothelium, including the renal pelvis, ureter, bladder, urethra, and urachal tube. Regarding the classification of urothelial carcinoma, it is worth noting that bladder cancer accounts for the vast majority of urothelial carcinomas. More than 90% of lower tract urothelial carcinoma cases are bladder cancer, while the remaining 5%-10% belong to upper tract urothelial carcinoma, mainly including ureteral cancer and renal pelvic cancer.

[0003] Bladder cancer can be divided into bladder urothelial carcinoma, bladder squamous cell carcinoma, bladder adenocarcinoma, etc. according to tissue origin, among which bladder urothelial carcinoma accounts for more than 90% and is the most common pathological type; according to pathological type, it can be divided into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). NMIBC patients account for the majority (75-85%), including carcinoma in situ (5%-10%), Ta stage (70%-75%), and T1 stage (20%-25%). For NMIBC patients, the best clinical treatment options are urethral bladder tumor transurethral resection (TURBt), radical cystectomy or auxiliary bladder instillation. According to statistics, the five-year recurrence and metastasis rate of patients after resection is about 30%-70%, 15% of patients will be graded and staged, and the prognosis of advanced patients is poor. Therefore, patients diagnosed with NMIBC need to receive frequent treatment and monitoring, regular follow-up, and high treatment costs. Early diagnosis and treatment, reducing postoperative recurrence rate, and predicting prognosis are of great significance for improving the overall survival rate of patients with urothelial carcinoma.

[0004] Cystoscopy and urine exfoliated cytology are the gold standards for clinical diagnosis of urothelial carcinoma. Cystoscopy mainly uses cystoscopes to observe whether the patient has tumors, as well as the number, size, morphology, location, etc. For suspected sites, biopsy tissue can be taken for further pathological examination. It is an accurate way to diagnose urothelial carcinoma and monitor recurrence after surgery. In addition to ordinary cystoscopes, fluorescent and narrow-band imaging cystoscopes also have many clinical applications. Cystoscopy is highly sensitive, but has low specificity, and is traumatic and invasive, accompanied by discomfort, and is prone to cause subsequent complications such as urinary tract infection and bleeding in patients. Traditional urine cytology is to collect urine from patients as samples, observe under an optical microscope after staining and preparation, and perform cytological diagnosis. Because it is a non-invasive examination and sample collection is convenient, it is used as one of the main methods for early diagnosis of bladder cancer and regular follow-up after surgery. Although it has high specificity, it is greatly affected by the subjectivity of the examiner, and it is also very easy to miss the diagnosis of small tumors, which has caused limitations in the early screening and diagnosis of urothelial carcinoma. In clinical practice, urine cytology is often used in combination with cystoscopy to improve the accuracy of diagnosing urothelial carcinoma. New screening methods such as ultrasound, CT examination, tumor marker screening, and fluorescence in situ hybridization (FISH) are also widely used in clinical practice, but due to their low sensitivity (25%-35%), they still cannot meet the needs of early urothelial carcinoma screening. In addition, ultrasound (UC) and fluorescence in situ hybridization techniques require high operator experience, so they can only be used as supplementary screening technologies and have not yet completely replaced standard detection methods.

[0005] DNA methylation is a key epigenetic regulator of gene expression and is closely related to the occurrence of cancer. Abnormal methylation of CpG island regions is an early event in many tumors, which can cause transcriptional silencing of tumor suppressor genes, leading to tumorigenesis. Changes in DNA methylation patterns can be one of the first tumor changes associated with tumorigenesis to be detected, so it is widely used in the diagnosis and prognosis of common cancers. Relevant studies have shown that urothelial carcinoma gene mutations are highly individualized, and ctDNA methylation sites can serve as an ideal biomarker for early screening and diagnosis of urothelial carcinoma. At present, the main methylation products for early diagnosis of urothelial carcinoma include AssureMDx™ developed by MDxHealth, which uses multiplex SNaPshot® targeted detection PCR technology to detect DNA methylation mutations in the three genes of OTX1, ONECUT2 and TWIST1, with a sensitivity and specificity of 93% and 86% respectively; Benchmark Medical's UriFind® product uses multiplex real-time fluorescence quantitative PCR technology to detect CpG methylation sites of VIM and ONECUT2 genes in the urine of patients with urothelial carcinoma, with a sensitivity and specificity of 89.74% and 92.46% respectively. Although the above-mentioned detection products have high detection sensitivity and specificity, their positive predictive value and negative predictive value cannot meet the early screening needs of cancer patients. Summary of the invention

[0006] In order to solve the deficiencies of the prior art, the present invention provides a polymethylated urothelial carcinoma early screening system and a model construction method, which are used to solve at least one of the above technical problems.

[0007] The technical solution adopted by the present invention to solve the technical problem is a polymethylated urothelial carcinoma early screening system, comprising:

[0008] An acquisition module is used to obtain tissue specimens to be screened and adjacent cancer specimens;

[0009] The gene screening module performs comparative sequencing based on simplified methylation sequencing technology on the samples obtained by the acquisition module to screen out specific marker genes with high specificity and high methylation levels;

[0010] Gene verification module, which verifies the marker genes screened initially through BSP sequencing / MSP amplification technology;

[0011] The urothelial carcinoma screening module automatically outputs the screening results through a multi-gene methylation detection kit combined with a statistical model, thereby realizing urothelial carcinoma screening, wherein the statistical model is obtained by training a logistic regression model based on a training set and a validation set, and the training set and the validation set include normal samples and cancer samples; the multi-gene methylation detection kit includes:

[0012] POU4F2 gene detection primer probe sequences SEQ ID NO: 1 to SEQ ID NO: 3;

[0013] Vimentin gene detection primer probe sequence SEQ ID NO: 7 to SEQ ID NO: 9;

[0014] The SOX1 gene detection primer probe sequences are SEQ ID NO: 25 to SEQ ID NO: 27 and the ACTB gene detection primer probe sequences are SEQ ID NO: 31 to SEQ ID NO: 33.

[0015] Furthermore, the screening of specific marker genes with high specificity and high methylation level includes: RRBS sequencing hierarchical filtering module, sequence alignment module, DMR detection module and DMR screening module;

[0016] RRBS sequencing hierarchical filtering module, used to identify / cut / remove high-throughput sequencing data;

[0017] A sequence alignment module to map clean reads back to the genome and output extracted methylation ratios, retaining cytosines in CpG contexts with a sequencing depth greater than or equal to 10x coverage;

[0018] The DMR detection filter module uses the binary separation algorithm in the Metilene software combined with double statistical tests to re-detect DMRs between paired samples or two groups of samples. Finally, the differentially methylated regions are obtained through multiple test correction and CpG sites.

[0019] The DMR screening module is used to further filter the DMRs that do not meet the rules, perform ROC curve analysis on the filtered DMRs, and sort and screen them based on methylation level differences, ROC, and TCGA database to screen out several differential genes.

[0020] Furthermore, the DMR detection and filtering module locates the relevant elements and genes of DMRs in the genome through the genome gff file, and performs genome GO and KEGG annotations based on the orgdb database.

[0021] Furthermore, the filtering condition of the DMR detection filtering module is:

[0022] (1) The Q values ​​of the double statistical tests were all less than 0.05;

[0023] (2) The difference in methylation level is greater than 0.25;

[0024] (3) The CpG value contained in the DMR is greater than 5;

[0025] (4) The length of DMR is greater than 50 bp.

[0026] Furthermore, the filtering rules of the DMR screening module are as follows:

[0027] (a) CpG density is 0.05-0.3;

[0028] (b) Gene promoter and gene body annotations that are both empty are removed;

[0029] (c) X, Y chromosome gene removal, chrUn removal.

[0030] The gene verification module realizes gene verification through the BSP sequencing verification module of differentiated genes, the MSP verification module of differentiated genes and the real-time fluorescence quantitative nucleic acid amplification detection system verification module of differentiated genes.

[0031] Furthermore, the steps of the BSP sequencing verification module for differential genes are as follows:

[0032] (1) Design BSP sequencing primers with a fragment size of 200 bp for several differentially expressed genes screened;

[0033] (2) Select tumor tissues and corresponding adjacent tissues from several patients with urothelial carcinoma for BSP sequencing, compare the methylation differences of the promoters of differentially expressed genes in tumor tissues and adjacent tissues, and further filter out candidate target genes;

[0034] (3) Based on the methylation sequencing results, differential analysis was performed to identify 5 to 10 marker genes as genes to be evaluated in the preliminary model of multi-gene detection.

[0035] Furthermore, the MSP verification module of the differential genes is as follows: the differential gene fragments screened out in the BSP are combined with methylation primers to design MSP primers, and urine samples of tumor patients and healthy people are tested based on dye-based fluorescent quantitative PCR to further determine the target gene segment.

[0036] Furthermore, the verification module of the real-time fluorescent quantitative nucleic acid amplification detection system for differential genes is as follows: primer probes are designed for the promoter region of the target gene screened by MSP, and a dual detection system is constructed with the ACTB housekeeping gene. The bisDNA of the positive cell T24 cell DNA is diluted 10 times as a template, and a primer probe combination is selected. Several clinically diagnosed negative samples and several clinically diagnosed positive urine samples are tested, and the final primer probe combination is based on the complementary sensitivity of the clinical sample combination and the target substance primer probe combination.

[0037] The present invention also provides a model construction method, which is applied to the above-mentioned polymethylated urothelial carcinoma early screening system, and the method comprises the following steps:

[0038] Acquiring a large number of test samples, wherein the large number of test samples include a number of normal population samples and a number of urothelial cancer patient samples;

[0039] For each test sample, comparative sequencing is performed on the test samples to screen out specific marker genes;

[0040] Validation of marker genes;

[0041] Based on the labeled and verified test samples, the training set and the validation set are divided, and the model is trained to obtain a statistical model for urothelial carcinoma screening;

[0042] The test results are output based on the statistical model and the methylation detection kit.

[0043] Further, the determined threshold is obtained and marked as M.

[0044] The kit logistic regression value is labeled as CRA (BCA), which means urothelial carcinoma logistic regression value;

[0045] The formula for determining positive and negative sex through the logistic regression model is:

[0046] CRA(BCA)=XA*SB*VC*P;

[0047] Among them, CRA (BCA) is a comprehensive score, P is the difference between the CT values ​​of POU4F2 and ACTB genes; V is the difference between the CT values ​​of VIM and ACTB genes; S is the difference between the CT values ​​of SOX1 and ACTB genes; X is a constant, A, B, and C are the coefficients of SOX1, Vimentin, and POU4F2, respectively. The larger the absolute value of the coefficient, the greater its influence on the judgment of the result;

[0048] When the CRA (BCA) score is greater than the threshold value M, the test result is positive and the subject is likely to have urothelial carcinoma;

[0049] When the CRA score is less than the threshold value M, the test result is negative and the subject is unlikely to have urothelial carcinoma.

[0050] The present application also provides an electronic device, which adopts the following technical solution:

[0051] at least one processor;

[0052] Memory;

[0053] At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the method as described in any one of the first aspects.

[0054] The present application provides a computer-readable storage medium, which adopts the following technical solution:

[0055] A computer-readable storage medium storing at least one instruction, at least one program, a code set or an instruction set, wherein the at least one instruction, the at least one program, the code set or the instruction set is loaded by a processor and executed by the model building method as described in any one of the second aspects.

[0056] Beneficial effects of the present invention:

[0057] (1) The present invention can make the early screening results of urothelial carcinoma more accurate by combining the detection kit with the detection model. The detection kit adopts multi-gene marker detection technology, and the detection model is obtained by training with a large number of test samples.

[0058] (2) The system combined with a specific algorithm makes it easier to screen out differential genes, which have extremely high sensitivity and specificity in specific tumors, thereby providing feasibility for the development of specific tumor screening kits. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0060] Figure 1 It is a schematic diagram of the overall module of the present invention. DETAILED DESCRIPTION

[0061] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0062] like Figure 1 As shown, the embodiment of the present invention provides a polymethylated urothelial carcinoma early screening system, comprising:

[0063] An acquisition module is used to obtain tissue specimens to be screened and adjacent cancer specimens;

[0064] The gene screening module performs comparative sequencing based on the bisulfite sequencing technology (RRBS) on the samples obtained by the acquisition module to screen out specific marker genes with high specificity and high methylation level;

[0065] Gene verification module, which verifies the marker genes screened initially through BSP (bisulfite sequencing PCR) sequencing / MSP (methylation-specific PCR) amplification technology;

[0066] The urothelial carcinoma screening module realizes urothelial carcinoma screening by constructing a multi-gene methylation detection kit combined with a statistical model to automatically output screening results, wherein the statistical model is obtained by training a logistic regression model based on a training set and a validation set, and the training set and the validation set include normal samples and cancer samples.

[0067] The present invention also provides a model construction method, which is applied to the above-mentioned polymethylated urothelial carcinoma early screening system, and the method comprises the following steps:

[0068] Acquiring a large number of test samples, wherein the large number of test samples include a number of normal population samples and a number of urothelial cancer patient samples;

[0069] For each test sample, comparative sequencing is performed on the test samples to screen out specific marker genes;

[0070] Validation of marker genes;

[0071] Based on the labeled and verified test samples, the training set and the validation set are divided, and the model is trained to obtain a statistical model for urothelial carcinoma screening;

[0072] The test results are output based on the statistical model and the methylation detection kit.

[0073] 1. Screening of differential genes in urothelial carcinoma based on reduced representation bisulfite sequencing (RRBS) technology.

[0074] By performing RRBS sequencing on several urothelial carcinoma tissues and adjacent tissue control samples, bioinformatics analysis of the sequencing results was performed to screen for abnormal methylation genes related to urothelial carcinoma. The sequencing data was processed and differential gene loci for urothelial carcinoma were screened according to the following process. The specific steps include the following.

[0075] RRBS sequencing layered filtering (Raw data filtration): Use cutadapt (identification / cutting / removal of high-throughput sequencing data) to trim the adapter sequence. The parameter used is set to "-aAGATCGGAAGAGC-m35-n2".

[0076] Sequence alignment: Cleaned reads were mapped back to the genome using BSMAP software version 2.90. The parameter settings used were “-n0-v0.08-g1”. Methylation ratios were extracted from BSMAP output (SAM) using a Python script (methratio.py) distributed with the BSMAP package. Only uniquely mapped reads were used to calculate methylation ratios. Only cytosines in CpG contexts with sufficient sequencing depth (greater than or equal to 10× coverage) were retained for further analysis.

[0077] DMR detection: DMR (differential methylation region) detection uses metilene software published in authoritative journals. The software uses a binary separation algorithm combined with double statistical tests (such as MWU-test and 2DKS-test) to quickly realize DMR de novo detection between paired samples or two groups of samples. Finally, through multiple test correction, differential methylation regions are obtained. CpG sites are used to find differential methylation regions.

[0078] Metilene was used to detect differentially methylated regions (DMRs) in de novo mode in CpG sites with at least 10-fold coverage. The parameters used were set to "--mincpgs5 --minMethDiff0.1 --mtc1-X1-Y1-v0.7". The detected DMRs were then filtered according to the criteria:

[0079] (1) The MWU-testq-value and 2DKS-test Q-value are both less than 0.05;

[0080] (2) The difference in methylation levels must be greater than 0.25;

[0081] (3) The CpG value contained in the DMR must be greater than 5;

[0082] (4) The length of DMR must be greater than 50 bp.

[0083] The related elements and genes of DMRs were located in the genome through the genome gff file. The genome GO and KEGG annotations were performed based on the orgdb database. Then, the enrichment analysis of the above related genes was performed on GO and KEGG using Allenricher (v1.0).

[0084] DMR screening: DMR detected by DMR is further filtered, and the filtering rules are as follows:

[0085] (1) CpG density 0.05-0.3, CpG density criteria (5-30 CpGs / 100 bp);

[0086] (2) Gene promoter and gene body annotations that are both empty are removed;

[0087] (3) X, Y chromosome genes removed, chrUn removed.

[0088] The DMRs filtered by the above rules were subjected to ROC (receiver operating characteristic curve) curve analysis, and sorted and screened based on methylation level differences, ROC and TCGA database to screen out several differential genes.

[0089] 2. Verification of differentiated genes.

[0090] BSP sequencing verification of differential genes: For several differential genes screened by the above RRBS, BSP sequencing primers were designed in their regions with a target amplification fragment size of 200 bp;

[0091] Several tumor tissues and corresponding adjacent tissues of urothelial carcinoma patients were selected for BSP sequencing to compare the methylation differences of promoters of differential genes between tumor tissues and adjacent tissues, and further filter and screen candidate target genes;

[0092] Based on the methylation sequencing results, differential analysis was performed to determine 5 to 10 marker genes as genes to be evaluated in the preliminary model of multi-gene detection.

[0093] MSP verification of differential genes: The differential gene fragments screened out in BSP were combined with methylation primers to design MSP primers, and urine samples of tumor patients and healthy people were tested based on dye-based fluorescent quantitative PCR to further determine the target gene segments and further determine the differential genes as the target genes for the kit screening system construction.

[0094] qPCR verification of differential genes: Primer probes were designed for the promoter region of the target gene screened by MSP, and a dual detection system was constructed with the ACTB housekeeping gene. The bisDNA of the positive cell T24 cell DNA diluted 10 times was used as a template, and a primer probe combination with high amplification efficiency, good detection sensitivity, and excellent amplification curve was selected. Several clinically diagnosed negative samples and several clinically diagnosed positive urine samples were tested. The final primer probe combination was selected with high sensitivity, good specificity, high accuracy, and complementary sensitivity of the target substance primer probe combination.

[0095] 3. Multi-gene methylation detection kit.

[0096] The components of the multi-gene methylation detection kit are shown in Table 1, the POU4F2 gene detection primer probe sequence is SEQ ID NO: 1 to SEQ ID NO: 3; the Vimentin gene detection primer probe sequence is SEQ ID NO: 7 to SEQ ID NO: 9; the SOX1 gene detection primer probe sequence is SEQ ID NO: 25 to SEQ ID NO: 27 and the ACTB gene detection primer probe sequence is SEQ ID NO: 31 to SEQ ID NO: 33.

[0097] Table 1 Composition of multi-gene methylation detection kit

[0098]

[0099] The multiplex test kit also provides a method for determining positive and negative based on a logistic regression model, and the determination formula is CRA (BCA) = XA*SB*VC*P;

[0100] Among them, CRA (BCA) is a comprehensive score.

[0101] P is the difference between the CT values ​​of POU4F2 and ACTB genes;

[0102] V is the difference between the CT values ​​of VIM and ACTB genes;

[0103] S is the difference between the CT values ​​of SOX1 and ACTB genes;

[0104] X is a constant, A, B, and C are the coefficients of SOX1, Vimentin, and POU4F2, respectively. The larger the absolute value of the coefficient, the greater its influence on the result. The final judgment formula is:

[0105] CRA(BCA)=6.808-0.304*S-0.175*V-0.143*P.

[0106] This system combined with a specific algorithm makes it easier to screen out differential genes that have extremely high sensitivity and specificity in specific tumors, thereby providing feasibility for the development of specific tumor screening kits.

[0107] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of the present invention is defined by the attached claims and their equivalents.

Claims

1. A polymethylated urothelial carcinoma early screening system, characterized in that: include: An acquisition module is used to obtain tissue specimens to be screened and adjacent cancer specimens; The gene screening module performs comparative sequencing based on simplified methylation sequencing technology on the samples obtained by the acquisition module to screen out specific marker genes with high specificity and high methylation levels; Gene verification module, which verifies the marker genes screened initially through BSP sequencing / MSP amplification technology; The urothelial carcinoma screening module automatically outputs the screening results through a multi-gene methylation detection kit combined with a statistical model, thereby realizing urothelial carcinoma screening, wherein the statistical model is obtained by training a logistic regression model based on a training set and a validation set, and the training set and the validation set include normal samples and cancer samples; the multi-gene methylation detection kit includes: POU4F2 gene detection primer probe sequences SEQ ID NO: 1 to SEQ ID NO: 3; Vimentin gene detection primer probe sequence SEQ ID NO: 7 to SEQ ID NO: 9; The primer and probe sequences for detecting the SOX1 gene are SEQ ID NO: 25 to SEQ ID NO: 27 and the primer and probe sequences for detecting the ACTB gene are SEQ ID NO: 31 to SEQ ID NO: 33; The screening of specific marker genes with high specificity and high methylation level includes: RRBS sequencing hierarchical filtering module, sequence alignment module, DMR detection module and DMR screening module; RRBS sequencing hierarchical filtering module, used to identify / cut / remove high-throughput sequencing data; A sequence alignment module to map clean reads back to the genome and output extracted methylation ratios, retaining cytosines in CpG contexts with a sequencing depth greater than or equal to 10x coverage; The DMR detection filter module uses the binary separation algorithm in the Metilene software combined with double statistical tests to re-detect DMRs between paired samples or two groups of samples. Finally, the differentially methylated regions are obtained through multiple test correction and CpG sites. The DMR screening module is used to further filter the DMRs that do not meet the rules, perform ROC curve analysis on the filtered DMRs, and sort and screen them based on methylation level differences, ROC, and TCGA database to screen out several differential genes.

2. A polymethylated urothelial carcinoma early screening system as claimed in claim 1, characterized in that: The DMR detection filter module locates the related elements and genes of DMRs in the genome through the genome gff file, and performs genome GO and KEGG annotations based on the orgdb database.

3. The polymethylated urothelial carcinoma early screening system according to claim 1, characterized in that: The filtering conditions of the DMR detection filter module are: (1) The Q values ​​of the double statistical tests were all less than 0.05; (2) The difference in methylation level is greater than 0.25; (3) The CpG value contained in the DMR is greater than 5; (4) The length of DMR is greater than 50 bp.

4. The polymethylated urothelial carcinoma early screening system according to claim 1, characterized in that: The filtering rules of the DMR screening module are as follows: (a) CpG density is 0.05-0.3; (b) Gene promoter and gene body annotations that are both empty are removed; (c) X, Y chromosome gene removal, chrUn removal.

5. The polymethylated urothelial carcinoma early screening system according to claim 1, characterized in that: The gene verification module realizes gene verification through the BSP sequencing verification module of differentiated genes, the MSP verification module of differentiated genes and the real-time fluorescence quantitative nucleic acid amplification detection system verification module of differentiated genes.

6. A polymethylated urothelial carcinoma early screening system as claimed in claim 5, characterized in that: The steps of the BSP sequencing verification module for differential genes are: (1) Design BSP sequencing primers with a fragment size of 200 bp for several differentially expressed genes screened; (2) Select tumor tissues and corresponding adjacent tissues from several patients with urothelial carcinoma for BSP sequencing, compare the methylation differences of the promoters of differentially expressed genes in tumor tissues and adjacent tissues, and further filter out candidate target genes; (3) Perform differential analysis based on the methylation sequencing results and identify 5 to 10 marker genes as genes to be evaluated in the preliminary model of multi-gene detection.

7. The polymethylated urothelial carcinoma early screening system according to claim 5, characterized in that: The MSP verification module of differential genes is: to design MSP primers based on the differential gene fragments screened out in BSP and methylation primers, and to test urine samples of tumor patients and healthy people based on dye-based fluorescence quantitative PCR to further determine the target gene segment.

8. The polymethylated urothelial carcinoma early screening system according to claim 5, characterized in that: The verification module of the real-time fluorescent quantitative nucleic acid amplification detection system for differential genes is as follows: primer probes are designed for the promoter region of the target gene screened by MSP, and a dual detection system is constructed with the ACTB housekeeping gene. The bisDNA of the positive cell T24 cell DNA is diluted 10 times as a template, and the primer probe combination is selected. Several clinically diagnosed negative samples and several clinically diagnosed positive urine samples are tested, and the final primer probe combination is based on the complementary sensitivity of the clinical sample combination and the target substance primer probe combination.

9. A model construction method, applied to a polymethylated urothelial carcinoma early screening system according to any one of claims 1 to 8, characterized in that: The method comprises the following steps: Acquiring a large number of test samples, wherein the large number of test samples include a number of normal population samples and a number of urothelial cancer patient samples; For each test sample, comparative sequencing is performed on the test samples to screen out specific marker genes; Validation of marker genes; Based on the labeled and verified test samples, the training set and the validation set are divided, and the model is trained to obtain a statistical model for urothelial carcinoma screening; The test results are output based on the statistical model and the methylation detection kit.

10. A model building method according to claim 9, characterized in that: Get the determined threshold value and mark it as M. The kit logistic regression value is labeled as CRA (BCA), which means urothelial carcinoma logistic regression value; The formula for determining positive and negative sex through the logistic regression model is: CRA(BCA)=XA*SB*VC*P; Among them, CRA (BCA) is a comprehensive score. P is the difference between the CT values ​​of POU4F2 and ACTB genes; V is the difference between the CT values ​​of VIM and ACTB genes; S is the difference between the CT values ​​of SOX1 and ACTB genes; X is a constant, A, B, and C are the coefficients of SOX1, Vimentin, and POU4F2, respectively. The larger the absolute value of the coefficient, the greater its influence on the judgment of the result; When the CRA (BCA) score is greater than the threshold value M, the test result is positive and the subject is likely to have urothelial carcinoma; When the CRA score is less than the threshold value M, the test result is negative and the subject is unlikely to have urothelial carcinoma.

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