A bladder cancer biomarker based on urine non-cellular rnas and application thereof

Abstract

The application discloses a bladder cancer biomarker based on urine non-cell RNAs and application thereof, and belongs to the field of biological medicine.The biomarker comprises KRT17 genes, GPRC5A genes and DHRS2 genes in urine.A model is established according to the biomarker, the biomarker expression level is taken as an input variable, the bladder cancer prediction risk value is calculated, and the specificity can reach 90.3% and the sensitivity can reach 98.2%.The whole transcriptome sequencing is carried out on the urine RNA of BCa patients and normal people, the biomarker of the urine non-cell RNAs is screened and verified, the model is established, the bladder cancer is diagnosed and evaluated through the non-invasive method, the method is simple, non-invasive and painless, and a new strategy and means are provided for the evaluation and diagnosis of the bladder cancer.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, and in particular to a bladder cancer biomarker based on urinary noncellular RNAs and its application. Background Technology

[0002] Bladder cancer (BCa) is the 10th most common cancer worldwide, with approximately 573,000 new cases and 213,000 deaths annually. The incidence and mortality rates for men are 9.5 per 100,000 and 3.3 per 100,000, respectively. Bladder urothelial carcinoma accounts for about 90% of all bladder malignancies. Based on whether it invades the bladder muscle layer, it can be divided into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). Approximately 75% of BCa patients are initially diagnosed with NMIBC, of ​​which 10%-25% eventually progress to MIBC, with a 5-year survival rate of only 5%. BCa ranks 13th in incidence among all malignant tumors in my country, posing a serious threat to the physical and mental health of the Chinese population.

[0003] Currently, the clinical diagnostic criteria for bladder cancer (BCa) are cystoscopy and urine cytology. The main advantage of cystoscopy is that it allows visualization of the bladder wall, including the presence of abnormalities in the bladder mucosa, the presence of tumors, and their size, number, and location. It also allows for biopsy. However, cystoscopy also has some drawbacks: ① It is invasive, carrying a risk of infection, and can cause pain or hematuria; ② It is expensive; ③ The skill level of the operator varies, leading to a degree of subjectivity in the interpretation of the results; ④ Cystoscopy is not sensitive to flat tumors. While urine cytology is non-invasive, it lacks sufficient sensitivity for low-grade bladder cancer, and studies have shown that it does not reduce the frequency of cystoscopy in BCa patients. Therefore, developing novel, non-invasive, painless, economical, and accurate diagnostic methods for bladder cancer is crucial.

[0004] Liquid biopsy is a promising method due to its non-invasive nature and frequent testing capabilities, showing great potential for development in tumor diagnosis. Compared to other bodily fluids, urine has the advantage of being completely non-invasive in sample acquisition. In the case of bladder cancer (BCa), the bladder's physiological function is to store urine, and the tumor in BCa patients can directly contact the urine, resulting in a large number of naturally infiltrated tumor-related biomarkers (including DNA, RNA, proteins, extracellular vesicles, and metabolites). Therefore, the detection of urinary molecular markers is the best choice for bladder cancer diagnosis and disease monitoring. Currently discovered and applied urinary molecular markers generally suffer from poor sensitivity and specificity, and a high false positive rate. Therefore, exploring more valuable urinary molecular markers to improve the accuracy of bladder cancer prognosis, diagnosis, and disease monitoring is of great significance for advancing bladder cancer treatment. Summary of the Invention

[0005] The purpose of this invention is to provide a bladder cancer biomarker based on urinary non-cellular RNAs and its application, thereby addressing the problems existing in the prior art. This invention involves enriching urinary RNA from BCa patients and healthy individuals, performing whole-transcriptome sequencing, screening and validating urinary non-cellular RNA biomarkers, and constructing a bladder cancer prognostic risk assessment model based on these biomarkers, providing a new strategy and method for the assessment and diagnosis of bladder cancer.

[0006] To achieve the above objectives, the present invention provides the following solution: This invention provides the application of reagents for detecting the expression levels of biomarkers in the preparation of products for the diagnosis of bladder cancer, wherein the biomarkers include the KRT17 gene, GPRC5A gene, and DHRS2 gene in urine. The present invention also provides a urine non-cellular RNAs detection kit for bladder cancer diagnosis, the urine non-cellular RNAs detection kit comprising reagents for detecting the expression levels of the aforementioned biomarkers.

[0007] The present invention also provides a system for bladder cancer diagnosis prediction and assessment, the system including a computing unit, the computing unit using a bladder cancer diagnosis and assessment model to calculate a diagnostic prediction value; The bladder cancer diagnostic assessment model uses biomarker expression levels as input variables. These biomarkers include the KRT17 gene, GPRC5A gene, and DHRS2 gene in urine. The model calculates risk prediction values ​​using the following equation: Risk prediction value = exp(Predict1) / (1 + exp(Predict1)). Wherein, Predict1 = 1.2927 + 0.7394 × DHRS2 expression level + 0.3203 × GPRC5A expression level - 0.9536 × KRT17 expression level.

[0008] Furthermore, the system also includes a detection unit for detecting the expression level of the biomarker.

[0009] Furthermore, the system also includes an information acquisition unit, which is used to perform the operation of acquiring subject detection information, including the expression level of the biomarker.

[0010] Furthermore, the system also includes an evaluation unit, which is used to determine the diagnostic prediction results of the subject's bladder cancer based on the calculation results of the calculation unit, and to provide rational prevention and treatment suggestions.

[0011] Furthermore, the system also includes a result display unit, which is used to display the conclusions reached by the evaluation unit.

[0012] Furthermore, the result display unit displays the results via screen display, sound broadcast, or printing.

[0013] The present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, the device containing the computer-readable storage medium performs the system for bladder cancer diagnosis and prediction assessment.

[0014] The present invention also provides the application of biomarkers in constructing bladder cancer diagnostic models, wherein the biomarkers include the KRT17 gene, GPRC5A gene and DHRS2 gene in urine.

[0015] The present invention discloses the following technical effects: This invention provides a bladder cancer biomarker based on noncellular RNA in urine, offering a new strategy for noninvasive liquid biopsy of bladder cancer.

[0016] This invention constructs a bladder cancer prognostic risk assessment system based on urinary non-cellular RNA biomarkers. By modeling bladder cancer biomarkers based on urinary non-cellular RNA and detecting the expression of mRNA in urine using non-invasive methods, prognostic assessment of bladder cancer can be performed with a specificity of 90.3% and a sensitivity of 98.2%. The method is simple, non-invasive, painless, and cost-effective. Validation has shown that the constructed model has high specificity and sensitivity and can be effectively applied to the assessment and diagnosis of bladder cancer. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a diagram of the assembled vacuum filtration device; Figure 2 Sequencing results of non-cellular mRNAs in urine that were differentially expressed in BCa patients compared to normal controls; Figure 3 The expression of mRNA of target genes GPRC5A (A), GALNT1 (B), DUSP5 (C), STMN1 (D) and KRT17 (E) in urine samples from 24 BCa patients and 17 normal controls; Figure 4 Subject operating curves based on urine samples from 24 BCa patients and 17 normal controls for mRNA targeting genes GPRC5A (A), GALNT1 (B), DUSP5 (C), STMN1 (D), and KRT17 (E). Figure 5 The expression of mRNAs of target genes KRT17 (A), GPRC5A (B), DUSP5 (C), GALNT1 (D), and STMN1 (E) in a training set consisting of urine samples from 61 patients with BCa, 59 healthy individuals, and 57 interfering samples (including patients with benign urinary tract diseases such as bladder stones, kidney cysts, and ureteral stones). Figure 6 The receiver operating curves for mRNA targeting genes KRT17 (A), GPRC5A (B), and DUSP5 (C) are based on a training set consisting of urine samples from 61 BCa patients, 59 healthy individuals, and 57 interfering samples (including patients with benign urinary tract diseases such as bladder stones, kidney cysts, and ureteral stones). Figure 7 The receiver operating characteristic curves for mRNA of dual-target KRT17+GPRC5A (A), DUSP5+KRT17 (B) and DUSP5+GPRC5A (C) and tri-target KRT17+GPRC5A+DUSP5 (D) are based on a training set consisting of urine samples from 61 BCa patients, 59 healthy individuals, and 57 interfering samples (including patients with benign urinary tract diseases such as bladder stones, kidney cysts, and ureteral stones). Figure 8The subject operating curve of the diagnostic model for noncellular mRNAs in urine constructed based on the training set; Figure 9 The expression of target genes KRT17 (A) and GPRC5A (B) mRNA in a validation set consisting of urine samples from 85 BCa patients, 83 healthy individuals, and 76 interfering samples (including patients with benign urinary tract diseases such as bladder stones, kidney cysts, and ureteral stones). Figure 10 This is a subject operating curve for a urinary non-cellular mRNA diagnostic model based on a validation set. Detailed Implementation

[0019] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0020] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0021] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0022] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be readily apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0023] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0024] Example 1: Screening of target genes 1. Research Subjects Urine samples from newly diagnosed bladder cancer (BCa) patients who visited Shanghai Tenth People's Hospital between January 2025 and December 2025, who had not received any drug or surgical treatment, and healthy patients with normal physical examination results.

[0025] 2. Urine pretreatment Collect freshly collected morning midstream urine and place it in a 50 mL centrifuge tube. Centrifuge at 4000 rpm for 15 min, discard the sediment such as bottom cells and debris, filter the supernatant through a 0.45 μm filter membrane and place it in a new 50 mL centrifuge tube. Store at -80℃ for later use.

[0026] 3. Urine nucleic acid enrichment Take 15 mL of the treated urine and add it to a 50 mL centrifuge tube. Add 30 mL of lysis buffer BL (Wuhan Haijili Biotechnology Co., Ltd., catalog number HGN-tq0610) at a ratio of 1:2. Slowly invert and mix well. Let stand at room temperature for 20 min to ensure complete lysis.

[0027] Assemble a vacuum filtration device ( Figure 1 This ensures the airtightness of the device. Transfer the sample lysis mixture to the syringe of the vacuum filtration device, perform vacuum filtration, discard the waste liquid, syringe, and adapter, and place the nucleic acid adsorption column into a clean waste collection tube; add 500 μL of washing buffer WB1 (Wuhan Haijili Biotechnology Co., Ltd., catalog number HGN-tq0610) to the nucleic acid adsorption column, centrifuge at 9000 rpm for 1 min, discard the waste liquid, and place the nucleic acid adsorption column into the waste collection tube; then add 500 μL of washing buffer WB2 (Wuhan Haijili Biotechnology Co., Ltd., catalog number HGN-tq0610) to the nucleic acid adsorption column, centrifuge at 9000 rpm for 1 min, discard the waste liquid, and place the nucleic acid adsorption column into the waste collection tube, centrifuge at 9000 rpm for 2 min, and discard the waste collection tube; place the nucleic acid adsorption column into a new 1.5 mL nuclease-free centrifuge tube and incubate at room temperature for 3 min to completely evaporate residual ethanol; add 55 μL of TE elution buffer to the center of the nucleic acid adsorption column membrane (do not let the pipette tip touch the column membrane), and incubate at room temperature for 3 min. Centrifuge at 9000 rpm for 1 min, and the eluent is the urine nucleic acid sample. The enriched urine nucleic acid is stored at -80℃ for later use.

[0028] 4. Whole transcriptome sequencing Whole transcriptome sequencing was performed on urinary RNA samples (DNase-treated samples) from 55 bladder cancer patients and 35 healthy controls. The whole transcriptome sequencing and subsequent bioinformatics analysis were performed by Guangzhou Gedio Biotechnology Co., Ltd. Based on the sequencing results, a total of 1869 differentially expressed mRNAs were obtained, of which 1799 were upregulated and 70 were downregulated compared to the healthy control group. Figure 2 ). mRNAs (GPRC5A, GALNT1, DUSP5, STMN1 and KRT17 mRNAs) that were detected and significantly upregulated in all 90 sequencing samples (55 BCa and 35 normal controls) were selected for one-step qRT-PCR (Taqman method) verification.

[0029] Example 2: Preliminary Validation of Target Gene mRNAs Urine nucleic acid samples from 24 patients with bladder cancer (BCa) and 17 normal controls were enriched to screen mRNAs for validation using a one-step qRT-PCR (Taqman method), and the diagnostic potential of candidate mRNAs for BCa was analyzed. Referring to the content described in patent application CN112739832A, "Method for Developing Urine Biomarkers and for Detecting Bladder Cancer," DHRS2 was selected as the bladder-specific internal reference gene. The specific operation of the one-step qRT-PCR (Taqman method) is as follows: A. Calculate the number of reactions (N) required for the experiment, based on the principle of 3 replicates per sample per gene. N = (Number of negative controls (1T) + Number of positive controls (1T) + Error allowance (1T) + Number of samples (n)) × 3; B. Prepare the premixed solution for amplification of the required internal reference gene DHRS2 and target gene mRNA according to Tables 3 and 4 (excluding urine nucleic acid; primer and probe sequences for the target gene and internal reference gene DHRS2 are shown in Tables 1 and 2). C. After mixing thoroughly, centrifuge briefly and dispense 5 µl / well into the corresponding 384-well plate; D. Add urine nucleic acid, negative control, or positive control at 5 µL / well to the reaction wells corresponding to the target gene and the internal reference gene DHRS2. The total reaction volume per well after sample addition is 10 µL. E. Place the 384-well plate in a 4°C refrigerator and centrifuge at 3000 rpm for 5 min; F. Remove the 384-well plate and place it into the reaction chamber of the real-time quantitative PCR instrument. Set up the positive control, negative control, and test samples in the corresponding order, and set the name of the gene to be detected. At the same time, select the fluorescent reporter group FAM and the quencher group NFQ-MGB or None. Select ROX as the passive reference and set the reaction volume parameter to 10 μL. The reaction procedure is shown in Table 5.

[0030] Note: Select the "FAST" mode under the "Experiment Properties" module for the real-time quantitative PCR instrument. The reaction time and temperature rise / fall rate for Stage II and III can be adjusted according to the actual usage of the real-time quantitative PCR instrument's own program.

[0031] Table 1: Primer sequences for amplification of internal reference gene and target gene mRNA Table 2: Probe sequences for amplification of internal reference gene and target gene mRNA Table 3: Preparation method of single-well reaction system for internal reference gene mRNA amplification Table 4: Preparation methods for single-well reaction systems for target gene mRNA amplification Table 5: Amplification program for one-step qRT-PCR (Taqman method) Based on the qRT-PCR results, the ΔCT value was obtained by subtracting the CT value of the internal reference gene DHRS2 mRNA from the CT value of the candidate gene mRNA. Then, 2... -△△CT The relative expression levels of candidate gene mRNAs were calculated. The results showed that the mRNAs of five genes—GPRC5A (P=0.0027), GALNT1 (P=0.0031), DUSP5 (P=0.0153), STMN1 (P=0.0156), and KRT17 (P=0.0278)—were significantly upregulated in the BCa group, with statistically significant differences. Figure 3 This is consistent with the sequencing results.

[0032] To preliminarily assess the clinical diagnostic value of these five mRNAs, receiver operating characteristic (ROC) curves were plotted using IBM SPSS Statistics 26.0 based on their relative expression levels. The results are as follows: Figure 4 See Table 6.

[0033] Table 6: Diagnostic efficacy of target gene mRNA in BCa patients Depend on Figure 4As shown in Table 6, the area under the curve (AUC) of GPRC5A was 0.914 (95% CI: 0.827–1.000, sensitivity: 85.5%, specificity: 88.2%), the area under the curve (AUC) of GALNT1 was 0.841 (95% CI: 0.715–0.968, sensitivity: 73.9%, specificity: 88.2%), and the area under the curve (AUC) of DUSP5 was 0.917 (95% CI: 0.715–0.968, sensitivity: 73.9%, specificity: 88.2%). I: 0.828-1.000, sensitivity: 95.8%, specificity: 76.5%), STMN1 area under the curve (AUC) was 0.895 (95% CI: 0.799-0.992, sensitivity: 69.6%, specificity: 100%), KRT17 area under the curve (AUC) was 0.970 (95% CI: 0.926-1.000, sensitivity: 91.7%, specificity: 92.9%).

[0034] The above results preliminarily indicate that the mRNAs of candidate genes GPRC5A, GALNT1, DUSP5, STMN1, and KRT17 have certain diagnostic value for BCa. Therefore, these five mRNAs were preliminarily identified as target genes for constructing a BCa diagnostic model based on urinary non-cellular mRNAs.

[0035] Example 3: Construction of a BCa diagnostic model based on urinary non-cellular mRNAs Urine nucleic acid samples from 59 healthy individuals, 57 interfering samples (including patients with benign urinary tract diseases such as bladder stones, renal cysts, and ureteral stones), and 61 patients with benign urinary tract infections (BCAs) were enriched as the training set. The expression of target genes KRT17, GPRC5A, DUSP5, STMN1, and GALNT1 in urine samples from BCA patients and normal controls was examined using qRT-PCR. The results showed that KRT17 (P=0.0012), GPRC5A (P<0.0001), and DUSP5 (P=0.0021) were significantly overexpressed in the BCA group, and the differences were statistically significant. Figure 5 ).

[0036] The diagnostic efficacy of these three target gene mRNAs for bladder cancer was analyzed using a logistic regression model. The results are as follows: Figure 6 As shown in Table 7.

[0037] Table 7: Diagnostic efficacy of single-target, dual-target, and triple-target mRNAs against BCa (training set) Depend on Figure 6As shown in Table 7, the area under the curve (AUC) for KRT17 was 0.965 (95% CI: 0.901–0.992, sensitivity: 90.3%, specificity: 96.1%); for GPRC5A, it was 0.920 (95% CI: 0.842–0.967, sensitivity: 74.2%, specificity: 96.4%); and for DUSP5, it was 0.718 (95% CI: 0.624–0.812, sensitivity: 47.4%, specificity: 84.9%). These results indicate that the mRNAs of these three target genes have certain diagnostic value.

[0038] Subsequently, multivariate regression models for dual-target combined diagnosis (KRT17+GPRC5A, KRT17+DUSP5, GPRC5A+DUSP5) and tri-target combined diagnosis (KRT17+GPRC5A+DUSP5) were established using logistic regression. The results showed that the area under the curve (AUC) for dual-target KRT17+GPRC5A was 0.976 (95% CI: 0.918-0.997, sensitivity: 90.3%, specificity: 98.2%); the area under the curve (AUC) for KRT17+DUSP5 was 0.889 (95% CI: 0.827-0.952, sensitivity: 73.7%, specificity: 98.1%); and the area under the curve (AUC) for GPRC5A+DUSP5 was 0.753 (95% CI: 0.662-0.844, sensitivity: 49.1%, specificity: 96.2%). The area under the curve (AUC) for the three-target KRT17+GPRC5A+DUSP5 was 0.881 (95% CI: 0.813-0.948, sensitivity: 75.4%, specificity: 98.1%) (Table 7). Figure 7 This result indicates that the KRT17 and GPRC5A dual-target combined diagnostic model outperforms other combined target schemes and single-target independent diagnostics, and has the highest diagnostic specificity and sensitivity.

[0039] Based on the above results, the average CT value for each gene in each sample was calculated and denoted as CT. x 'x' represents a specific gene. A bladder cancer diagnostic model based on urinary non-cellular mRNAs is constructed using a logistic regression model. Predict final=exp(Predict1) / (1+exp(Predict1)), Where, Predict1 = 1.2927 + 0.7394 × CT DHRS2 +0.3203×CT GPRC5A -0.9536×CT KRT17 .

[0040] The area under the curve (AUC) of this model for bladder cancer diagnosis was 0.978 (95% CI: 0.920-0.998, sensitivity: 90.3%, specificity: 98.2%), the Youden index was 0.885, the positive predictive value was 95.1%, and the negative predictive value was 92.2%. Figure 8 According to the model, the threshold for judgment is 0.5. If the sample's Predict final value is ≥0.5, it is judged as positive for bladder cancer, and further confirmation is required by combining other laboratory testing methods; if the sample's Predict final value is <0.5, it is judged as negative for bladder cancer, and follow-up monitoring can be carried out.

[0041] Example 4: Validation of the BCa diagnostic model based on urinary non-cellular mRNAs To verify the effectiveness of the urinary non-cellular mRNA-based diagnostic model for urinary cysts (BCa) constructed in Example 3, urine nucleic acid samples from 83 healthy individuals, 76 interfering samples (including patients with benign urinary tract diseases such as bladder stones, renal cysts, and ureteral stones), and 85 BCA patients were enriched as a validation set. The expression of target genes KRT17, GPRC5A, and the internal reference gene DHRS2 in urine samples from BCA patients and normal controls was detected by qRT-PCR. The relative expression results showed that KRT17 (P=0.0005) and GPRC5A (P=0.0049) were significantly highly expressed in the BCA group, consistent with the previous results. Figure 9 ).

[0042] Then, the average CT value for each gene in each sample was calculated and denoted as CT. x , where x represents a specific gene, is substituted into the BCa diagnostic model constructed in Example 3: Predict final=exp(Predict1) / (1+exp(Predict1)), Where, Predict1 = 1.2927 + 0.7394 × CT DHRS2 +0.3203×CT GPRC5A -0.9536×CT KRT17 .

[0043] The area under the curve (AUC) was 0.976 (95% CI: 0.932–0.995, sensitivity: 90.2%, specificity: 95.6%), the Youden index was 0.858, the positive predictive value was 94.12%, and the negative predictive value was 96.86%. Figure 10 This result is basically consistent with the training set results, indicating that the constructed BCa diagnostic model based on non-cellular mRNAs in urine has good diagnostic efficacy for bladder cancer.

[0044] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. The application of a reagent for detecting the expression level of a biomarker in the preparation of products for bladder cancer diagnosis, characterized in that, The biomarkers include the KRT17 gene, GPRC5A gene, and DHRS2 gene in urine.

2. A urine non-cellular RNAs detection kit for bladder cancer diagnosis, characterized in that, The urine noncellular RNAs detection kit includes reagents for detecting the expression levels of the biomarkers described in claim 1.

3. A system for bladder cancer diagnosis, prediction, and assessment, characterized in that, The system includes a computing unit that uses a bladder cancer diagnostic assessment model to calculate diagnostic prediction values. The bladder cancer diagnostic assessment model uses biomarker expression levels as input variables. These biomarkers include the KRT17 gene, GPRC5A gene, and DHRS2 gene in urine. The model calculates risk prediction values ​​using the following equation: Risk prediction value = exp(Predict1) / (1 + exp(Predict1)). Wherein, Predict1 = 1.2927 + 0.7394 × DHRS2 expression level + 0.3203 × GPRC5A expression level - 0.9536 × KRT17 expression level.

4. The system according to claim 3, characterized in that, The system also includes a detection unit for detecting the expression level of the biomarker.

5. The system according to claim 3, characterized in that, The system further includes an information acquisition unit, which is used to perform the operation of acquiring subject detection information, including the expression level of the biomarker.

6. The system according to claim 3, characterized in that, The system also includes an evaluation unit, which is used to determine the diagnostic prediction results of the subject's bladder cancer based on the calculation results of the calculation unit, and to provide rational prevention and treatment suggestions.

7. The system according to claim 6, characterized in that, The system also includes a result display unit, which is used to display the conclusions reached by the evaluation unit.

8. The system according to claim 7, characterized in that, The result display unit displays the results via screen display, sound broadcast, or printing.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the bladder cancer diagnostic prediction and assessment system of claim 3.

10. The application of biomarkers in constructing a diagnostic model for bladder cancer, characterized in that, The biomarkers include the KRT17 gene, GPRC5A gene, and DHRS2 gene in urine.

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