Application of tool for detecting biomarker in preparation of product for diagnosing or predicting postoperative recurrence of prostate cancer

Abstract

The invention belongs to the technical field of biology, and particularly relates to application of a tool for detecting a biomarker in preparation of a product for diagnosing or predicting postoperative recurrence of prostatic cancer. And the biomarker is one or a combination of more of UBE2T, CDC20, FBXO25 and WDR5. By detecting the content of the biomarker, the postoperative recurrence risk of the prostate cancer can be accurately predicted.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and specifically relates to the use of a tool for detecting biomarkers in the preparation of a product for diagnosing or predicting postoperative recurrence of prostate cancer. Background Art

[0002] Prostate cancer refers to a malignant tumor that develops in the prostate gland of men and is one of the most common types of cancer in men. The cause of the disease is still unclear, but factors such as age, race, family history, obesity, poor diet, geographic location, hormonal abnormalities, sexually transmitted diseases, and prostatitis may increase the risk of prostate cancer. Patients may have no obvious symptoms in the early stages, but in the late stages, they may experience various symptoms such as abnormal urination, blood in semen, erectile dysfunction, pelvic discomfort, and bone pain. The principles of treatment are to control pain, improve the patient's quality of life, minimize tumor progression, and prolong patient survival. Common treatments include endocrine medication, chemotherapy, and surgery. Some patients with recurrence may choose cryosurgery.

[0003] Radical surgery is an important treatment for prostate cancer. However, the risk of biochemical recurrence after radical surgery is high, seriously affecting patients' quality of life and prognosis. However, existing methods for predicting postoperative recurrence risk have low accuracy and lack individualized assessment. Summary of the Invention

[0004] Based on the above technical problems, the present invention provides a tool for detecting biomarkers for use in the preparation of a product for diagnosing or predicting postoperative recurrence of prostate cancer. By detecting the content of the biomarker, the risk of postoperative recurrence of prostate cancer can be accurately predicted.

[0005] In order to achieve the above object, the technical solution provided by the present invention is as follows: In a first aspect, the present invention provides a method for detecting a biomarker for use in preparing a product for diagnosing or predicting postoperative recurrence of prostate cancer. UBE2T、CDC20、FBXO25、WDR5 One or a combination of several.

[0006] As a preferred embodiment of the present invention, the UBE2T、CDC20、WDR5 The expression level decreased or FBXO25 The increased expression level of β-actin B1 in prostate cancer indicates a reduced risk of recurrence after prostate cancer surgery.

[0007] As a preferred embodiment of the present invention, detecting the biomarker is detecting the biomarker content in a tumor tissue sample of a patient after prostate cancer surgery.

[0008] As a preferred embodiment of the present invention, by UBE2T、CDC20、FBXO25、WDR5The postoperative recurrence risk score was calculated based on the expression value of , and the postoperative recurrence risk of prostate cancer was determined by the postoperative recurrence risk score.

[0009] As a preferred embodiment of the present invention, the calculation formula for the postoperative recurrence risk score is: Postoperative recurrence risk score = 0.748473536191239× WDR5 The gene expression value of +0.140427752525457× UBE2T The gene expression value of +0.0169976936048349× CDC20 The gene expression value of -0.131695041503604× FBXO25 gene expression values.

[0010] As a preferred embodiment of the present invention, a cutoff critical value is calculated based on the postoperative recurrence risk score, and 7.09 is used as the cutoff critical value to judge the risk of postoperative recurrence of prostate cancer. A value greater than 7.09 is high risk and a value less than 7.09 is low risk.

[0011] As a preferred embodiment of the present invention, the product is a detection reagent, a kit or a chip.

[0012] In a second aspect, the present invention provides a method for screening a drug for preventing or treating postoperative recurrence of prostate cancer using a biomarker. UBE2T、CDC20、FBXO25、WDR5 One or a combination of several.

[0013] As a preferred embodiment of the present invention, a method for screening a drug for preventing or treating postoperative recurrence of prostate cancer comprises the following steps: The drug to be tested is administered to the prostate cancer model, and the expression levels of the biomarkers in the model before and after administration are detected. UBE2T、CDC20、WDR5 The expression level decreased or FBXO25 If the expression level of the gene increases, it is identified as a target drug.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This study screened potential prognostic biomarkers for prostate cancer after surgery by integrating ubiquitin-related gene expression data with genetic causal inference. UBE2T、CDC20、FBXO25、WDR5 , and based on this biomarker, a new prognostic model for prostate cancer postoperative recurrence was constructed, providing a more accurate treatment plan to improve the clinical prediction of prostate cancer postoperative recurrence.

[0015] By incorporating Mendelian randomization, causal inference, and multi-omics data integration, this study not only improves predictive capabilities but also deepens our understanding of the mechanisms of prostate cancer recurrence. This approach is adaptable to the ever-changing scientific and clinical landscape, laying the foundation for future research and applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 middle: A is a cross-validation curve diagram of lasso penalty on 63 independent influencing factors.

[0017] B is the regression coefficient path diagram of λ-1se corresponding to the lasso setting of seed=1215.

[0018] C is the survival curve of progression-free survival of high / low risk groups in the model1 training set.

[0019] D is the ROC curve of the training set of model1 for 1, 3, and 5 years.

[0020] E is the survival curve of progression-free survival of high / low risk groups in the model1 validation set.

[0021] F is the ROC curve of the validation set of model1 for 1, 3, and 5 years.

[0022] Figure 2 middle: A is a cross-validation curve diagram of lasso penalty for four independent influencing factors.

[0023] B is the regression coefficient path diagram of λ-min corresponding to the lasso setting of seed=1215.

[0024] C is the survival curve of progression-free survival of high / low risk groups in the model2 training set.

[0025] D is the ROC curve of the training set of model2 for 1, 3, and 5 years.

[0026] E is the survival curve of progression-free survival of high / low risk groups in the model2 validation set.

[0027] F is the ROC curve of the validation set of model2 for 1, 3, and 5 years.

[0028] Figure 3 middle: A is the cross-validation curve of elastic network regularization for 8 independent influencing factors during the establishment of model3.

[0029] B is the regression coefficient path diagram of λ-1se corresponding to seed=14 and alpha=0.51 during the establishment of model3.

[0030] C is the survival curve of progression-free survival of high / low risk groups in the model3 training set.

[0031] D is the ROC curve of the training set of model3 for 1, 3, and 5 years.

[0032] E is the survival curve of progression-free survival of high / low risk groups in the model3 validation set.

[0033] F is the ROC curve of the validation set of model3 for 1, 3, and 5 years.

[0034] Figure 4 A is the risk score distribution map of the model3 training set, B is the distribution map of the number of days of progression-free survival of the training set, and C is the key gene expression heat map of the training set.

[0035] Figure 5 A is the risk score distribution map of the model3 validation set, B is the progression-free survival day distribution map of the validation set, and C is the key gene expression heat map of the validation set.

[0036] Figure 6 This is the clinical cohort validation diagram for model1, model2, and model3.

[0037] Figure 7 The sample distribution of high-risk and low-risk groups with and without biochemical recurrence is shown in Figure 2.

[0038] Figure 8 The risk scores of the patients in each clinical group were compared, where A represents clinical T stage; B represents abdominal and pelvic CT results; C represents MRI results; D represents the presence of new tumors after initial treatment; E represents pathological stage N, where N0 indicates unaffected lymph nodes and N1 indicates regional lymph node metastasis; and F represents the successful outcome of initial treatment.

[0039] Figure 9 This is a correlation diagram between the gleason score and the risk score, which shows that the risk score has a consistent trend with the currently recognized prostate cancer severity score, the gleason score.

[0040] Figure 10 This is a correlation diagram between PSA value and risk score, indicating that the risk score is consistent with the patient's PSA value trend. PSA value is a specific marker of prostate lesions. The PSA value in normal people is low, but the PSA value in patients with prostate lesions is increased. Therefore, PSA value can be used for prostate cancer screening.

[0041] Figure 11 Comparison of the self-validation AUC values ​​of model1, model2 and model3 in the third year.

[0042] Figure 12 Comparison of the self-validation AUC values ​​of model1, model2, and model3 in the fifth year.

[0043] Figure 13 It is a comparison of clinical recurrence risk scores in postoperative patients. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below with reference to the accompanying drawings and examples. The following examples are intended to illustrate the present invention only and are not intended to limit the scope of the invention. Experimental methods in the examples where specific conditions are not specified are generally carried out under conventional conditions or as recommended by the manufacturer.

[0045] Prostate cancer refers to a malignant tumor that occurs in the prostate area of ​​men and is one of the most common types of cancer in men. Patients may have no obvious symptoms in the early stages, but in the late stages they may experience various symptoms such as abnormal urination, blood in semen, erectile dysfunction, and discomfort in the pelvic area. The principle of treatment is to control pain, improve the patient's quality of life, control tumor progression to the maximum extent, and prolong the patient's survival. Common treatment methods mainly include endocrine drug therapy, chemotherapy, surgical treatment, etc. Some patients with recurrence can choose cryosurgery. Radical surgery is one of the important means of treating prostate cancer. However, the risk of biochemical recurrence after radical surgery is high, which seriously affects the patient's quality of life and prognosis. However, the existing methods for predicting the risk of postoperative recurrence have low accuracy and lack individualized evaluation.

[0046] Based on this, the present invention screened out potential prognostic biomarkers for prostate cancer after surgery by integrating ubiquitin-related gene expression data with genetic causal inference. UBE2T、CDC20、FBXO25、WDR5 By detecting the content of this biomarker, the risk of recurrence of prostate cancer after surgery can be accurately predicted. At the same time, a prognostic model for prostate cancer recurrence after surgery was constructed based on this biomarker, providing a more accurate treatment plan for improving the clinical prediction of prostate cancer recurrence after surgery.

[0047] 1. Materials and Methods Based on 550 prostate cancer samples (including 498 tumor tissues) from the TCGA database, consensus clustering was used to divide the expression profiles of ubiquitin-proteasome system (UPS)-related genes into Cluster 1 and Cluster 2. Cluster 2 was further refined into three molecular subtypes (Cluster 2a, 2b, and 2c). The limma package was used to analyze differentially expressed genes between tumor tissues and adjacent adjacent tissues, screening for UPS-related differentially expressed genes.

[0048] Subsequently, the initial prognostic model was constructed by univariate Cox regression and LASSO penalized regression and the screened UPS-related differentially expressed genes, which was labeled model 1. At the same time, 60 UPS-related and 16 PCa-related GWAS datasets were obtained from the IEU OpenGWAS database and analyzed by two-way two-sample Mendelian randomization (MR) analysis (with p < 10-6 Screening SNPs, r 2 <0.001, kb=10,000 for linkage disequilibrium correction, and an F-statistic >10 to ensure instrumental variable strength. Inverse variance weighting (IVW) was used as the primary evaluation criterion to identify UPS genes with causal effects. An MR-COX prognostic model, designated model 2, was constructed. Finally, elastic network regression was used to integrate the characteristic genes of model 1 and model 2 to establish a composite prognostic model, designated model 3. The model was cross-validated using clinical cohort data from the PCaDB, and the correlation between the risk score and clinical indicators was systematically evaluated.

[0049] The IDs and related information of the GWAS datasets used in constructing the MR-COX model are shown in Tables 1 and 2 .

[0050] Table 1 Ubiquitin-related IEU IDs and related information Table 2 Prostate cancer-related IEU id and related information 2. Results The TCGA-PRAD dataset and its clinical trait data were downloaded from xena. The ubiquitin genes in the cancer samples (498 cases) were clustered using the ConsensusClusterPlus package to obtain cluster1 (95 cases) and cluster2 (403 cases). Cluster2 was further clustered by consensus to obtain cluster2a / 2b / 2c. The limma package was used to perform differential gene analysis between each cluster and adjacent adjacent cancer samples (52 cases). The results were intersected with the ubiquitin genes to obtain 248 ubiquitin genes.

[0051] Using p-value < 0.05 as the screening criterion, the Surv function in the survival package was used to predict the risk of each gene in the modeling sample, and the cox() function was used to construct a univariate model. The summary() function was used to extract the results of the univariate model. Univariate Cox analysis was performed on 248 ubiquitin genes, and 63 key ubiquitin genes that affect progression-free survival (PFI) were obtained.

[0052] Progression-free survival was used as the survival time indicator, and whether cancer progressed was used as the survival outcome indicator. The 63 statistically significant genes obtained from the single factor analysis were used as the modeling gene set 1. The modeling matrix 1 was constructed with 498 cancer samples for LASSO analysis.

[0053] 60% of the cancer samples were used as the training set, and LASSO regression was performed using the glmnet package. During the operation, the λ-1se corresponding to seed=1215 was set as the optimal penalty coefficient to penalize the variables with little contribution ( Figure 1 A in the middle), and the risk prediction gene set 1 was obtained ( Figure 1 (B in the middle), denoted as model1.

[0054] The cutoff ROC function in the survivalROC_NEW R package was used to calculate the cutoff value for distinguishing high and low risk, which was 2.14. A value above the cutoff value was considered high risk, and a value below the cutoff value was considered low risk. The Kaplan-Meier survival curve and the area under the ROC curve were used to evaluate the effectiveness of the risk assessment model ( Figure 1 C, D). Figure 1 As shown in Figure C, there is a significant difference in the survival probability between the high-risk and low-risk groups. Figure 1 As shown in Figure D, the prediction accuracy of the 1-, 3-, and 5-year recurrence probabilities is greater than 0.75, indicating that the constructed model has good predictive ability.

[0055] The remaining 40% of the samples were used as a validation set and the same method was used for prediction. In the test set, the cutoff value was also 2.14, and the corresponding survival curve and ROC results were obtained ( Figure 1 The results showed that although Figure 1 Middle E still shows significant differences in survival between high-risk and low-risk groups, but Figure 1 The AUC values ​​of F in the middle did not all reach 0.75, suggesting that the predictive ability of the model was limited in this cohort.

[0056] GWAS data for prostate cancer and ubiquitin were obtained from the IEU OpenGWAS. To ensure consistency in Mendelian analyses of exposure and outcome, GWAS data from mixed populations were excluded, and samples from European origin were primarily used. The IEU IDs and related information for the ubiquitin gene and prostate cancer GWAS datasets involved in the analysis are shown in Tables 1 and 2.

[0057] The TwoSampleMR R package version 0.5.7 was loaded in R for Mendelian randomization analysis of prostate cancer and ubiquitination outcomes.

[0058] To select appropriate instrumental variables, the same processing was performed on the 16 groups of prostate cancer and 60 groups of ubiquitin GWAS data: to ensure a strong correlation between SNPs—P < 5×10 -6 , commonly 5×10 -8 Since too few SNPs were selected in this case, which was not conducive to subsequent analysis, the threshold was relaxed to P < 5 × 10 -6; Remove weak instrumental variables - the strength of the association between the instrumental variable and the exposure factor is usually assessed using the F statistic in the regression model, using F statistic = (β / SE) 2 To evaluate the instrument strength of each SNP, an F statistic > 10 can effectively avoid the bias caused by weak instrumental variables, so SNPs with F < 10 are discarded; linkage disequilibrium is removed - r 2 The size of is between 0 and 1. The smaller it is, the weaker the association between the two SNPs (relative independence), that is, the distribution of the two SNPs is completely random. kb refers to the length of the region considering linkage disequilibrium, so removing r 2 >0.001, SNPs in the kb=10000 range.

[0059] Two-sample Mendelian randomization was performed on the 60 ubiquitin protein data sets for which instrumental variable selection had been completed, along with the 16 prostate cancer data sets, with 16 × 60 statistical runs. To improve the accuracy of the results, only results from analyses using instrumental variables with >5 SNPs are reported. In this process, IVW was used to assess the causal relationship between the exposure factor and the outcome, supplemented by MR-Egger regression, WM, and WME.

[0060] To assess the robustness of the results of Mendelian randomization, determine the reliability of the conclusions, and detect potential bias and the impact of instrumental variables on outcome variables, sensitivity analyses were performed: horizontal pleiotropy—referring to the multiple pathways through which SNPs affect traits—was tested using MR-Egger regression analysis. The P value of the MR-Egger intercept indicated horizontal pleiotropy, and P>0.05 indicated the absence of pleiotropy. Heterogeneity analysis—A Cochran's Q test was performed on the main IVW method and MR-Egger regression method. If P>0.05, the included instrumental variables were considered to be free of heterogeneity, and the impact of heterogeneity on the causal effect estimate could be ignored.

[0061] The results screened for 42 IVW results with p-values ​​less than 0.05. All results had p-values ​​greater than 0.05 for heterogeneity and horizontal pleiotropy tests, demonstrating the robustness of the present findings. The results revealed a causal relationship between ubiquitination and prostate cancer and identified 20 ubiquitin genes with altered expression in prostate cancer.

[0062] Univariate Cox analysis was performed on 20 ubiquitin genes, and 4 key causal ubiquitin genes that affected progression-free survival (PFI) were obtained as risk prediction gene set 2, denoted as model2.

[0063] The TCGA-PRAD dataset downloaded from xena and 60% of its clinical trait data were used as training sets for modeling. Figure 2A represents the process of LASSO on the training set. Figure 2 Where B is the coefficient change of all genes under different λ obtained by modeling. Figure 2 The best λ obtained by A in Figure 2 When B reaches the optimal λ, all four genes are retained and their coefficients are extracted to build the model.

[0064] The model in the training set showed significant differences in survival between high-risk and low-risk groups and had certain predictive ability, such as Figure 2 As shown in C and D. Figure 2 As shown in Figure C, there is a significant difference in the survival probability between the high-risk and low-risk groups. Figure 2 As shown in Figure D, the prediction accuracy of the 1-, 3-, and 5-year recurrence probabilities is not all greater than 0.7, suggesting that the predictive ability of the model is limited.

[0065] This model is then used to predict the remaining 40% of samples. Figure 2 Middle E still shows significant differences in survival between high-risk and low-risk groups, but Figure 2 The AUC values ​​of F in the middle did not all reach 0.7, suggesting that the model had poor predictive ability in this cohort.

[0066] Risk prediction gene set 1 and risk prediction gene set 2 were merged into risk prediction gene set 3 (denoted as model3). 498 cancer samples were combined into modeling matrix 3. Elastic network regularization based on COX regression was performed using the glmnet R package to construct a prostate cancer ubiquitination recurrence risk assessment model. During the operation, the λ-1se corresponding to seed = 14 and alpha = 0.51 was set as the optimal penalty coefficient. Figure 3 Middle A~F.

[0067] Comparing the ROC curves of the three models, we can find that model 3 has the best AUC value: The 1-, 3-, and 5-year AUC values ​​for the model1 training set were 0.814, 0.764, and 0.763, respectively; and the validation set values ​​were 0.744, 0.759, and 0.719, respectively; The 1-, 3-, and 5-year AUC values ​​of the model2 training set were 0.733, 0.688, and 0.702, respectively; and the validation set values ​​were 0.755, 0.692, and 0.669, respectively; The 1-, 3-, and 5-year AUC values ​​of the model3 training set were 0.794, 0.783, and 0.754, respectively; and those of the validation set were 0.760, 0.754, and 0.752, respectively.

[0068] It can be seen that model1 and model3 are better than model2 in all aspects. Although the training set of model3 is slightly worse than that of model1, the validation set of model3 is better than that of model1, especially in long-term prediction (0.752>0.719).

[0069] The cutoff ROC function in the survivalROC_NEW R package was used to calculate the cutoff value for model 3 to distinguish between high and low risk. Values ​​above the cutoff value were considered high risk, while values ​​below the cutoff value were considered low risk. Kaplan-Meier survival curves and the area under the receiver operating characteristic (ROC) curve were used to evaluate the performance of model 3.

[0070] Figure 4 A represents the risk score of each sample in the model3 training set. The horizontal axis is the sample number and the vertical axis is the risk score value, which is used to represent the calculated cutoff value. The cutoff value is used as the dividing point to distinguish the high and low risk groups divided by the score. Figure 4 From A, we can see that the cutoff value is 7.09, that is, the group is divided into high-risk group and low-risk group according to the cutoff value of 7.09. Greater than 7.09 is high risk, and less than 7.09 is low risk. Figure 4 The icon "progression" in middle B indicates whether the patient relapsed or not. The horizontal axis is still the sample number, and the vertical axis is the progression-free survival (years). It can be seen that the shorter the progression-free survival, the higher the probability of progression. Figure 4 The C in the middle represents the range of gene expression values, indicating the trend of the expression of these genes and the risk score.

[0071] Figure 5 A represents the risk score of each sample in the model3 validation set, the horizontal axis is the sample number, and the vertical axis is the risk score value. Figure 5 From A, we can see that the cutoff value is 7.09, that is, the group is divided into high-risk group and low-risk group according to the cutoff value of 7.09. Greater than 7.09 is high risk, and less than 7.09 is low risk. Figure 5 The icon "progression" in middle B indicates whether the disease has relapsed or not. It can be seen that the shorter the progression-free survival period, the higher the probability of progression; Figure 5 The C in the middle represents the range of gene expression values, indicating the trend of the expression of these genes and the risk score.

[0072] The PCaDB online database was used to perform RFS modeling on the genes of the three models. The results were used to verify whether the selected genes could be used to establish the model and whether there was a difference in survival probability after being divided into high-risk and low-risk groups. The website has 10 prostate cancer-related data sets. After setting the genes in each data set, the CoxPH, CoxLasso and CoxRidge algorithms were used to model the model. A total of 30 validations were performed. The final validation results were as follows: model 1 achieved significant stratification in 4 validations (log-rank P < 0.05); model 2 achieved significant stratification in 3 validations; and model 3 achieved significant stratification in 6 validations ( Figure 6 ). Thus, model 3 has been confirmed in more independent cohorts and has better generalization capabilities. The dataset sources and results are shown in Table 3.

[0073] Table 3 PCaDB validation data Model3 is used to calculate the risk score for prostate cancer recurrence after surgery. The calculation formula is: Postoperative recurrence risk score = [(0.748473536191239) × WDR5 Gene expression value + (0.140427752525457) × UBE2T Gene expression value + (0.0169976936048349) × CDC20 Gene expression value + (-0.131695041503604) × FBXO25 gene expression values] The postoperative recurrence risk score of each sample was calculated and divided into high-risk group and low-risk group according to the cutoff value of 7.09.

[0074] The present invention screened out four ubiquitination markers through a composite prognostic model: UBE2T、CDC20、FBXO25、WDR5 , showing the best predictive performance. The model showed significant discriminatory power in multiple important clinical dimensions: Figure 7 For biochemical recurrence validation of the sample, the low-risk group accounted for 68% of individuals without biochemical recurrence (p = 1.04e-11), showing good predictive ability.

[0075] Figure 8 The relationship between each clinical trait and risk score is shown in the present invention. The T1 stage of clinical T stage is recorded as the early stage and T2 / 3 / 4 as the advanced stage. It is found that the risk score of the advanced stage is significantly higher than that of the early stage ( Figure 8 Middle A, p=0.00017), showing good differentiation of tumor size before surgery; abdominal and pelvic CT ( Figure 8 Middle B, p=0.0047) and MRI ( Figure 8In the scan results of C, p=0.02), it was found that the risk score of the group with cancer extension or metastasis was significantly higher than that of the samples without extension or metastasis, showing a good distinction between whether the tumor has metastasized; the risk score of the samples with new tumors after initial treatment was significantly higher than that of the samples without new tumors ( Figure 8 D, p = 4e-11), showing good predictive ability for the occurrence of new tumors after surgery; the risk score of samples with pathological stage N1 was also significantly higher than that of samples with N0 ( Figure 8 E, p=1.7e-07), showing excellent discrimination between the presence or absence of lymph node metastasis; in the outcomes of the first treatment of prostate cancer patients, the risk score of complete remission / response, that is, successful efficacy, was significantly lower than that of other situations, showing good predictive ability for the outcome of the first treatment.

[0076] Figure 9 This is a correlation plot between Gleason score and risk score, demonstrating that the risk score follows the patient's Gleason score (R=0.45, p<2.2e-16). Gleason score is a key indicator for assessing the malignancy of prostate cancer, with higher scores generally indicating a higher degree of malignancy. This correlation further validates the clinical validity of the model.

[0077] Figure 10 The following is a correlation plot between PSA values ​​and risk score, demonstrating that the risk score and the patient's PSA value are consistent (R=0.12, p=0.0098). PSA is a specific marker of prostate disease. While PSA levels are low in healthy individuals, they are elevated in patients with prostate disease. Therefore, PSA can be used for prostate cancer screening. This consistency further validates the clinical relevance of the model.

[0078] In order to evaluate the robustness of the model, the present invention further conducted internal validation. Figure 11 As shown, the present invention compares the AUC values ​​of model1, model2 and model3 after randomly sampling 500 samples with a proportion of 60%. Figure 12 The AUC values ​​of model 1, model 2, and model 3 in the fifth year are compared. These results clearly show that model 3 has stronger predictive robustness at different time points and in repeated sampling validation.

[0079] 3. Application of biomarker-based prediction models in predicting the risk of recurrence after prostate cancer surgery 3.1 Research subjects This study included 41 patients with prostate cancer (PCa) who underwent radical prostatectomy (RP) before March 2022. All 41 patients were from the Affiliated Hospital of the Fourth Military Medical University. Relevant clinical data of the patients, including age, PSA, Gleason score, and tumor stage, were collected during the same period. Tissue samples from all patients were obtained during surgery and immediately snap-frozen in liquid nitrogen, then transferred to a -80°C ultra-low temperature freezer for storage. The protocol of this invention has been reviewed and approved by the Ethics Committee of the Fourth Military Medical University (Ethics Approval No.: KY20224210), and all patients signed written informed consent before enrollment.

[0080] 3.2 Patient inclusion and exclusion criteria Inclusion criteria: (1) The patient was diagnosed with prostate adenocarcinoma by postoperative histopathological examination.

[0081] (2) Clinical stage is localized prostate cancer (TNM stage is cT1-T3N0M0), which meets the surgical indications for radical prostatectomy. The TNM stage is based on the 8th edition of the AJCC standard. cT1-T3N0M0 means that the tumor is confined to the prostate or locally invades, but there is no lymph node or distant metastasis.

[0082] (3) Aged between 45 and 75 years old.

[0083] (4) Patients who are visiting the hospital for the first time and are diagnosed for the first time have not received any form of tumor treatment before surgery, including endocrine therapy, radiotherapy, chemotherapy or other targeted / immunotherapy.

[0084] (5) Complete clinical pathological data such as preoperative serum total prostate-specific antigen (tPSA) level, Gleason score, and clinical stage.

[0085] (6) The postoperative follow-up data are complete and can clearly determine whether biochemical recurrence has occurred.

[0086] Exclusion criteria: (1) Combined with malignant tumors of other systems.

[0087] (2) Distant metastasis has occurred before surgery (stage M1).

[0088] (3) Received any anti-tumor treatment before surgery.

[0089] (4) Clinical or follow-up data are incomplete, making effective evaluation impossible.

[0090] (5) Suffering from severe, uncontrolled dysfunction of important organs such as the heart, liver, and kidney.

[0091] 3.3 Group Definition According to the postoperative follow-up results, the 41 patients were divided into two groups: Non-recurrence group (n=20): patients did not experience biochemical recurrence during the postoperative follow-up period.

[0092] Recurrence group (n=21): Patients were diagnosed with biochemical recurrence during postoperative follow-up. Biochemical recurrence was defined as two or more consecutive detections of serum PSA levels ≥0.2 ng / mL (three weeks or more apart) after radical prostatectomy.

[0093] Basis: The European Association of Urology (EAU) guidelines define PSA ≥ 0.2 ng / mL (two consecutive times) as the biochemical recurrence standard, and the guidelines of the Urology Branch of the Chinese Medical Association also generally adopt this threshold.

[0094] 3.4 Sample Preparation and RNA Extraction Samples were removed from -80°C, ground into a fine powder, and then TRIzol lysis buffer was added to release RNA. RNA was then extracted and eluted using the Qiagen RNeasy Mini Kit. RNA concentration and purity were determined using a NanoDrop assay, and integrity was assessed using an Agilent 2100 Bioanalyzer. For detailed procedures, please refer to the Qiagen RNeasy Mini Kit manual at https: / / www.qiagen.com / us / products / discovery-and-translational-research / dna-rna-purification / rna-purification / total-rna / rneasy-kits?catno=74104.

[0095] 3.5 mRNA enrichment and library construction mRNA was enriched using Oligo(dT) magnetic beads with the Illumina TruSeq Stranded mRNA Kit. mRNA was fragmented and reverse transcribed into cDNA, barcoded adapters were added, and PCR amplification was performed to construct libraries. For detailed procedures, see the Illumina TruSeq Stranded mRNA Reference Guide at https: / / support.illumina.com / content / dam / illumina-support / documents / documentation / chemistry_documentation / samplepreps_truseq / truseq-stranded-mrna-workflow / truseq-stranded-mrna-workflow-reference-1000000040498-00.pdf.

[0096] 3.6 Library Quality Control and Quantification The library fragment size distribution was determined using an Agilent 2100 Bioanalyzer, and the concentration was quantified using a Qubit 2.0 assay to ensure suitability for sequencing. For detailed procedures, see the Agilent 2100 Bioanalyzer RNA Analysis Guide at https: / / www.manuallib.com / download / pdf11 / AGILENT-2100-BIOANALYZER-MANUAL.PDF.

[0097] 3.7 High-throughput sequencing Paired-end 150 bp sequencing was performed using the Illumina NovaSeq 6000, with approximately 30 million reads per sample. For detailed procedures, see the Illumina NovaSeq 6000 System Guide: https: / / www.illumina.com / systems / sequencing-platforms / novaseq.html

[0098] 3.8 Data processing and transcriptome matrix construction Data quality was assessed using FastQC, low-quality reads were removed using Trimmomatic, and expression matrices were quantitatively constructed using STAR. For detailed procedures, see the STAR and featureCounts documentation. The STAR documentation is available at https: / / github.com / alexdobin / STAR, and the featureCounts documentation is available at https: / / subread.sourceforge.net / featureCounts.html.

[0099] 3.9 Results The basic characteristics of the included patients are shown in Table 4.

[0100] Table 4 Basic characteristics of patients The patient clinical information, expression levels of biomarkers in the samples, and the results of postoperative recurrence risk scoring for each sample using model3 are shown in Table 5 and Supplementary Table 5.

[0101] Table 5 Clinical information of patients Supplementary Table 5 Marker expression levels and risk scores Risk score comparison chart Figure 13 As shown. Figure 13 As shown in Table 5 and Supplementary Table 5, there was a significant difference in the risk scores between the recurrence group and the non-recurrence group (p=3.7e-05), which once again verified that model 3 has the ability to significantly distinguish between recurrence and non-recurrence in a newly collected real-world clinical cohort, and has a good ability to predict the risk of recurrence after prostate cancer surgery.

[0102] The above embodiments are only provided for understanding the method and core concept of the present invention. It should be noted that, without departing from the principles of the present invention, a number of improvements and modifications may be made to the present invention by a person skilled in the art, and such improvements and modifications shall fall within the scope of protection of the claims of the present invention.

Claims

1. Use of a tool for detecting biomarkers in the preparation of a product for diagnosing or predicting postoperative recurrence of prostate cancer, characterized in that: The biomarkers are UBE2T、CDC20、FBXO25、WDR5 One or a combination of several.

2. The use according to claim 1, characterized in that described UBE2T、CDC20、WDR5 The expression level decreased or FBXO25 The increased expression level of β-actin B1 in prostate cancer indicates a reduced risk of recurrence after prostate cancer surgery.

3. The use according to claim 1, characterized in that Biomarker detection is the detection of biomarker levels in tumor tissue samples from patients after prostate cancer surgery.

4. The use according to claim 1, characterized in that pass UBE2T、CDC20、FBXO25、WDR5 The postoperative recurrence risk score was calculated based on the expression value of , and the postoperative recurrence risk of prostate cancer was determined by the postoperative recurrence risk score.

5. The use according to claim 4, characterized in that The formula for calculating the postoperative recurrence risk score is: Postoperative recurrence risk score = 0.748473536191239× WDR5 The gene expression value of +0.140427752525457× UBE2T The gene expression value of +0.0169976936048349× CDC20 The gene expression value of -0.131695041503604× FBXO25 gene expression values.

6. The use according to claim 5, characterized in that The cutoff value was calculated based on the postoperative recurrence risk score, and 7.09 was used as the cutoff value to judge the risk of postoperative recurrence of prostate cancer. A value greater than 7.09 was considered high risk, and a value less than 7.09 was considered low risk.

7. The use according to claim 1, characterized in that The product is a detection reagent, a kit or a chip.

8. Use of the biomarker according to claim 1 in screening drugs for preventing or treating postoperative recurrence of prostate cancer, characterized in that: The biomarkers are UBE2T、CDC20、FBXO25、WDR5 One or a combination of several.

9. The use according to claim 8, characterized in that The method for screening drugs for preventing or treating recurrence of prostate cancer after surgery comprises the following steps: The drug to be tested is administered to the prostate cancer model, and the expression levels of the biomarkers in the model before and after administration are detected. UBE2T、CDC20、WDR5 The expression level decreased or FBXO25 If the expression level of the gene increases, it is identified as a target drug.

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