Application of a biomarker in assessing the risk of prostate cancer recurrence

Abstract

This invention discloses the application of a biomarker in assessing the risk of prostate cancer recurrence, belonging to the field of tumor molecular diagnostics and bioinformatics technology. The biomarker includes at least one of the TACR2 and LPIN3 genes, and RCCD1, PRSS27, and NAP1L5, with the core detection focusing on the methylation-transcriptional combined characteristics of the TACR2 gene. This invention obtains patient samples, uses RT-qPCR to detect the expression levels of multiple genes, and methylation-specific PCR to detect the methylation level of the TACR2 promoter region. A risk scoring model is constructed using LASSO regression, and patient prognosis is stratified based on the score. This invention solves the problems of traditional assessment indicators being affected by tumor heterogeneity and having limited predictive accuracy. It is the first to use TACR2-related multi-omics characteristics for prostate cancer recurrence assessment. The multi-gene combined model improves predictive accuracy and can be developed into a standardized testing kit, providing a scientific basis for clinical recurrence risk assessment and personalized treatment, with significant industrial and clinical application value.

Description

Technical Field

[0001] This invention relates to the fields of tumor molecular diagnostics and bioinformatics technology, specifically to the application of a biomarker in assessing the risk of prostate cancer recurrence. Background Technology

[0002] Prostate cancer is a malignant tumor with a high incidence and mortality rate in the male urinary system. Its incidence is increasing year by year and is affecting younger men, making it a significant public health problem threatening men's health. Prostate cancer exhibits significant tumor heterogeneity; the rate of tumor progression, invasiveness, and recurrence probability vary greatly among different patients. Some patients have slow tumor progression and can live with the tumor for a long time, while others are diagnosed at an advanced stage, with a high risk of recurrence after surgery or treatment and a high likelihood of distant metastasis, seriously affecting their quality of life and long-term prognosis. Therefore, accurately assessing the recurrence risk of prostate cancer patients and developing individualized diagnosis, treatment, and follow-up plans accordingly is the core key to improving the clinical treatment outcomes of prostate cancer.

[0003] Currently, traditional indicators used in clinical practice for prognostic assessment and recurrence risk evaluation of prostate cancer mainly include prostate-specific antigen (PSA) levels, Gleason scores, and TNM staging. While PSA levels are commonly used for prostate cancer screening and disease monitoring, they suffer from insufficient specificity; benign conditions such as benign prostatic hyperplasia and prostatitis can also lead to elevated PSA levels, and their early warning value for prostate cancer recurrence is limited. Gleason scores assess tumor differentiation based on the pathological morphology of tumor tissue, and TNM staging reflects the extent of tumor invasion and metastasis. Although both can predict tumor progression to some extent, they only assess pathological morphology and clinical staging, failing to reflect the molecular mechanisms and biological behaviors of tumor development. Due to tumor heterogeneity, the accuracy and discriminative power of these traditional indicators in predicting long-term survival and recurrence risk in prostate cancer patients are significantly limited, making it difficult to meet the clinical need for precise and individualized prognostic assessment, and also unable to provide reliable molecular evidence for early intervention in high-risk patients.

[0004] In recent years, with the rapid development of molecular biology and multi-omics technologies, studies have confirmed that molecular-level changes such as abnormal DNA methylation and dysregulation of gene expression are the core molecular mechanisms of prostate cancer occurrence, development, invasion, and recurrence. Abnormal silencing or activation of DNA methylation and dysregulation of key gene expression can influence the clinical outcome and recurrence risk of prostate cancer by regulating tumor cell proliferation, apoptosis, invasion, and metastasis. Therefore, identifying molecular biomarkers closely related to prostate cancer recurrence has become an important direction for overcoming the limitations of traditional prognostic assessment methods.

[0005] Currently, some studies have attempted to use the methylation or expression characteristics of single genes for prognostic assessment of prostate cancer. However, these studies mostly focus on the single molecular level and have not yet formed a multi-gene, multi-omics combined characterization system. Furthermore, clinical translational research targeting the combined methylation-transcriptional characteristics is still relatively scarce. Meanwhile, among the reported candidate genes, the role of the methylation and transcriptional expression characteristics of the novel key gene TACR2 in prostate cancer recurrence risk assessment has not been systematically studied and validated, and a prostate cancer recurrence risk assessment system based on the combined methylation-transcriptional characteristics of multiple genes related to TACR2 has not yet been established. Summary of the Invention

[0006] Therefore, this invention provides an application of biomarkers in assessing the risk of prostate cancer recurrence, solving the problem that traditional clinical prostate cancer recurrence assessment indicators are affected by tumor heterogeneity and have limited predictive accuracy.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides the application of a biomarker in the preparation of a product for assessing the risk of prostate cancer recurrence, wherein the biomarker includes the TACR2 gene and the LPIN3 gene.

[0008] Preferably, the biomarker further includes at least one of the RCCD1 gene, PRSS27 gene, and NAP1L5 gene.

[0009] Preferably, the product is used to detect the DNA methylation level and / or gene expression level of the biomarker.

[0010] Preferably, the DNA methylation level is the methylation level of the TACR2 gene promoter region, and the gene expression level is the mRNA transcription level.

[0011] The present invention also provides a reagent for assessing the risk of prostate cancer recurrence, including a detection reagent for detecting the above-mentioned biomarkers.

[0012] Preferably, the reagent includes at least one of methylation-specific PCR reagent for detecting the methylation level of biomarker DNA and RT-qPCR reagent for detecting the gene expression level of biomarker.

[0013] Preferably, the methylation-specific PCR reagent is designed for the TACR2 gene promoter region.

[0014] The present invention also provides a predictive system for assessing the risk of prostate cancer recurrence, comprising:

[0015] The detection module is used to detect the DNA methylation level and / or gene expression level of the above biomarkers;

[0016] The analysis module is used to input the detection results obtained by the detection module into the risk scoring model to calculate the risk score, and to determine the risk of prostate cancer recurrence based on the comparison between the risk score and a preset threshold.

[0017] The output module is used to output the recurrence risk assessment results of the analysis module.

[0018] Preferably, in the analysis module, the risk scoring model is:

[0019] RiskScore=a×Expr(RCCD1)+b×Expr(PRSS27)+c×Expr(NAP1L5)

[0020] In the formula, RiskScore is the risk score, Expr is an abbreviation for Expression, representing the gene expression level, specifically the mRNA transcription level of the corresponding gene in the prostate cancer patient sample; RCCD1, PRSS27, and NAP1L5 are genes related to prostate cancer recurrence / prognosis selected by LASSO regression; a, b, and c are the coefficients corresponding to the gene expression levels.

[0021] When the risk score is higher than the preset threshold, it is judged as a high risk of recurrence of prostate cancer; when the risk score is lower than or equal to the preset threshold, it is judged as a low risk of recurrence of prostate cancer.

[0022] The present invention also provides the application of a biomarker in the preparation of a product for prognostic assessment of prostate cancer.

[0023] The present invention has the following advantages:

[0024] First, this study is the first to clearly demonstrate that the combined methylation-transcriptional characteristics of the TACR2 gene can be used to assess the risk of prostate cancer recurrence. It systematically verifies the application value of this novel key gene in the prognosis of prostate cancer, fills the gap in the research and application of this gene characteristic in existing technologies, and enriches the target system for molecular diagnosis of prostate cancer.

[0025] Second, by constructing a joint risk scoring model using multiple genes such as TACR2, LPIN3, RCCD1, PRSS27, and NAP1L5, and integrating multi-omics features of DNA methylation and gene expression, we have overcome the limitations of traditional single molecular indicators, effectively avoided the interference of tumor heterogeneity on prediction results, and significantly improved the accuracy and discriminative ability of prostate cancer recurrence risk assessment.

[0026] Third, relying on mature molecular detection technologies such as RT-qPCR and methylation-specific PCR, biomarker detection can be achieved. The operation is simple, the results are stable, and the detection cost is controllable. It can be directly developed into standardized clinical test kits, which are easy to promote and apply in medical institutions at all levels. It has strong industrialization potential and market application value.

[0027] Fourth, based on risk scores, prostate cancer patients can be divided into high- and low-recurrence-risk groups, which can identify high-risk groups for recurrence in the early stages after disease treatment. This provides a scientific molecular basis for the development of personalized follow-up monitoring plans and intervention treatment strategies in clinical practice, and helps to achieve precision medicine for prostate cancer.

[0028] Fifth, this invention can not only be used for prostate cancer recurrence risk assessment, but also provide a reference for judging the overall survival prognosis of patients and monitoring treatment effects. At the same time, it can provide new molecular targets for the development of targeted therapy drugs for prostate cancer, broaden the application scenarios of molecular markers in tumor diagnosis and treatment, and help improve the overall level of prostate cancer diagnosis and management.

[0029] Sixth, it provides clinicians with an objective and quantitative tool for assessing the risk of prostate cancer recurrence, effectively making up for the shortcomings of traditional indicators such as PSA, Gleason score, and TNM staging. It helps clinicians make accurate decisions quickly and take timely intervention measures for patients at high risk of recurrence, thereby reducing the recurrence rate of prostate cancer and improving the long-term survival prognosis of patients, which has important clinical significance. Attached Figure Description

[0030] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the method for assessing the risk of prostate cancer recurrence provided in an embodiment of the present invention;

[0032] Figure 2 The LASSO regression path diagram provided in this embodiment of the invention shows the coefficient changes of 5 genes under different penalty coefficients, highlighting the optimal λ value and the 3 core genes that were ultimately retained.

[0033] Figure 3 The risk score distribution diagram provided in this embodiment of the invention shows the risk score distribution and median threshold of high and low risk groups;

[0034] Figure 4The ROC curve provided in this embodiment of the invention shows that the model AUC value is 0.87, proving that the predictive efficacy is better than traditional clinical indicators.

[0035] Figure 5 This is a schematic diagram of the predictive system architecture for assessing the risk of prostate cancer recurrence, provided in an embodiment of the present invention. Detailed Implementation

[0036] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] See Figure 1 This invention provides an application of a biomarker in assessing the risk of prostate cancer recurrence. The specific implementation process is as follows:

[0038] S1. Sample Acquisition and Preprocessing:

[0039] S11 Sample Collection:

[0040] Biological samples were collected from patients clinically and pathologically diagnosed with prostate cancer, including prostate cancer tumor tissue samples and peripheral venous blood samples. Peripheral blood samples were also collected from healthy individuals as normal controls. Informed consent was obtained from all patients during sample collection, and their clinical data were complete, including key information such as age, pathological grade, Gleason score, TNM stage, PSA level, postoperative recurrence status, and recurrence-free survival time.

[0041] In this embodiment, a total of 200 samples from prostate cancer patients were collected, including 120 tumor tissue samples, 80 peripheral blood samples, and 50 peripheral blood samples from healthy controls. All samples were flash-frozen in liquid nitrogen 30 minutes after clinical collection and then transferred to an ultra-low temperature freezer at -80°C for storage to avoid nucleic acid degradation.

[0042] S12 Nucleic Acid Extraction and Quality Control:

[0043] Nucleic acid extraction from tumor tissue samples: Take 50 mg of frozen tumor tissue sample, grind it into powder in liquid nitrogen, add 1 mL of TRIzol lysis buffer for complete lysis, and extract genomic DNA and total RNA separately using phenol-chloroform extraction method; after extraction, wash the precipitate with 75% anhydrous ethanol, dissolve it in ultrapure water, and store it at -80℃.

[0044] Peripheral blood sample nucleic acid extraction: Take 2 mL of peripheral blood sample, use EDTA anticoagulation, and extract genomic DNA and total RNA simultaneously using a peripheral blood nucleic acid extraction kit (column method) according to the kit instructions. The entire process is carried out in a sterile laminar flow hood to avoid nucleic acid contamination.

[0045] Nucleic acid quality control: The concentration and purity of extracted DNA and RNA are detected using a nucleic acid protein detector. DNA samples are required to have an OD260 / OD280 ratio between 1.8 and 2.0, and RNA samples are required to have an OD260 / OD280 ratio between 1.9 and 2.1. RNA samples must also be free of 28S / 18S ribosomal RNA degradation. Nucleic acid integrity is detected using 1% agarose gel electrophoresis. Samples with clear bands and no tails are considered qualified. Samples with nucleic acid degradation, low concentration, or substandard purity are rejected.

[0046] S2. Detection of biomarker gene expression levels:

[0047] The mRNA transcription levels of TACR2, LPIN3, RCCD1, PRSS27, and NAP1L5 genes were detected using real-time quantitative PCR (RT-qPCR). GAPDH was used as an internal reference gene to correct for differences in RNA extraction and reverse transcription efficiency among samples. The specific operation steps are as follows:

[0048] S21, cDNA first-strand synthesis:

[0049] Take 1 μg of qualified total RNA sample from S1, and according to the reverse transcription kit instructions, use oligo(dT)18 as a primer, add reverse transcriptase, dNTP mixture, RNase inhibitor, etc., to prepare a 20 μL reverse transcription reaction system; place the reaction system in a PCR instrument, and complete the reverse transcription according to the program of incubation at 42℃ for 60 min and inactivation at 70℃ for 15 min. Store the synthesized cDNA first strand in a -20℃ freezer and complete qPCR detection within 1 week.

[0050] S22. Specific primer design and synthesis:

[0051] Based on the reference sequences of TACR2, LPIN3, RCCD1, PRSS27, NAP1L5, and GAPDH genes in the GenBank database, specific PCR primers were designed using Primer Premier 5.0 software. The primer design followed the principle of crossing introns to avoid interference from genomic DNA amplification. The primer length was 18-25 bp, the annealing temperature was 58-62℃, and the amplification product fragment length was 100-300 bp.

[0052] S23, RT-qPCR amplification reaction

[0053] Prepare a 20 μL RT-qPCR reaction system containing 2 μL cDNA template, 0.8 μL each of forward and reverse specific primers, 10 μL 2×SYBR Green quantitative PCR buffer, and 6.4 μL enzyme-free water. Add the reaction system to a 96-well quantitative PCR plate, setting up a blank control (enzyme-free water instead of cDNA template), a negative control (cDNA from healthy control samples), and replicate experiments (3 replicates per sample) to minimize experimental error.

[0054] The 96-well plate was placed in a real-time quantitative PCR instrument, and the amplification program was executed as follows: 95℃ pre-denaturation for 3 min; 95℃ denaturation for 15 s; 60℃ annealing and extension for 30 s, for a total of 40 cycles; finally, melting curve analysis was performed (fluorescence signal was collected every 0.5℃ from 60℃ to 95℃). The melting curve showed a single peak, indicating good primer specificity and no non-specific amplification or primer dimer formation.

[0055] S24. Standardization of gene expression data:

[0056] Use 2 -ΔΔCt The relative expression levels of each gene in prostate cancer patient samples were calculated using the following formula:

[0057] ΔCt = Ct value of target gene - Ct value of internal reference gene GAPDHC;

[0058] ΔCt = ΔCt value of patient sample - average ΔCt value of healthy control sample;

[0059] Relative expression level (Expr) = 2 -ΔΔCt ;

[0060] The relative gene expression levels of all samples were normalized to eliminate the influence of experimental batches and instrument errors, resulting in standardized gene expression level data for subsequent risk model construction.

[0061] Detection of S3 and TACR2 gene methylation levels:

[0062] The methylation level of the TACR2 gene promoter region was detected using methylation-specific PCR (MSP). This region is rich in CpG islands, and its methylation status directly regulates the transcriptional expression of the TACR2 gene. The specific operation steps are as follows:

[0063] S31, Genomic DNA bisulfite modification:

[0064] Take 1 μg of qualified genomic DNA from S1 and treat the DNA using a bisulfite modification kit. This irreversibly converts unmethylated cytosine (C) to uracil (U), while methylated cytosine remains unchanged. This is the core step of methylation-specific PCR. The specific procedure is as follows: DNA denaturation at 95℃ for 5 min, bisulfite modification at 50℃ for 16 h, desalting and purification followed by NaOH denaturation, ethanol precipitation, and dissolution in ultrapure water. The modified DNA is stored at -20℃, and MSP amplification is completed within one week.

[0065] S32, methylation / nonmethylation specific primer design:

[0066] Based on the CpG island sequence in the TACR2 gene promoter region, methylation (M)-specific primers and non-methylation (U)-specific primers were designed using MethPrimer software. These primers could only bind to DNA templates with the corresponding methylation state after bisulfite modification, ensuring amplification specificity. Primer design requirements: methylation primers were designed for modified CpG sites, and non-methylation primers were designed for modified TpG sites. The annealing temperature was 55–60°C, and the amplification product fragment length was 100–200 bp. The primers were synthesized and purified by a professional biotechnology company.

[0067] S33, MSP amplification and preliminary result interpretation:

[0068] PCR amplification was performed on the modified DNA samples using methylation-specific primers and non-methylation-specific primers, respectively. A 25 μL MSP reaction system was prepared, containing 2 μL of modified DNA template, 1 μL each of methylated and non-methylated upstream and downstream primers, 12.5 μL of 2×Taq PCR mixture, and 8.5 μL of enzyme-free water.

[0069] Set up a positive control (methylated DNA standard), a negative control (unmethylated DNA standard), and a blank control (enzyme-free water). The PCR amplification program was as follows: 95℃ pre-denaturation for 5 min; 95℃ denaturation for 30 s, 58℃ annealing for 30 s, 72℃ extension for 30 s, for a total of 35 cycles; 72℃ final extension for 10 min.

[0070] Take 10 μL of MSP amplification product, detect it by 2% agarose gel electrophoresis, add nucleic acid dye, observe the bands and take pictures in the gel imaging system:

[0071] Only methylated primers amplified specific bands: this indicates that the TACR2 gene promoter region is completely methylated.

[0072] Only unmethylated primers amplified specific bands: this indicates complete unmethylation.

[0073] Both amplified specific bands, indicating partial methylation.

[0074] S34. Quantitative analysis of methylation levels:

[0075] The gray values ​​of the electrophoretic bands were quantitatively analyzed using gel imaging analysis software to calculate the relative methylation level of the TACR2 gene promoter region. The calculation formula is as follows:

[0076] Methylation level (M%) = (Methylated band gray value / (Methylated band gray value + Unmethylated band gray value)) × 100%;

[0077] We statistically analyzed the methylation levels of all samples to determine their correlation with TACR2 gene expression levels and their association with clinicopathological features and recurrence risk in prostate cancer patients.

[0078] S4. Construction of a prostate cancer recurrence risk scoring model:

[0079] Based on the LASSO (Minimum Absolute Contraction and Selection Operator) regression algorithm, combined with gene-normalized expression data obtained from S2 and recurrence information from patient clinical follow-up, key genes closely related to prostate cancer recurrence were screened, and a quantitative recurrence risk scoring model was constructed. The specific steps are as follows:

[0080] S41. Grouping of Research Samples and Data Preprocessing

[0081] The 200 prostate cancer patients collected were randomly divided into a training set (140 cases) and a validation set (60 cases) in a 7:3 ratio. The training set was used to build and optimize the risk model, and the validation set was used to verify the effectiveness of the model.

[0082] Gene expression data were preprocessed: samples with missing clinical information, abnormal gene testing data, or less than 1 year of follow-up were excluded; normality tests and standardization were performed on the data to ensure that the data met the requirements for statistical analysis; the patient's relapse status was used as the dependent variable (relapse = 1, no relapse = 0), and the standardized expression levels of TACR2, LPIN3, RCCD1, PRSS27, and NAP1L5 genes were used as independent variables and included in subsequent analysis.

[0083] S42 and LASSO regression screening for key genes

[0084] LASSO regression analysis was performed using the glmnet package in R software. The optimal penalty coefficient (λ value) was determined by 10-fold cross-validation. By using the penalty term compression coefficient, the coefficients of genes that were not related to or had a weak correlation with prostate cancer recurrence were compressed to 0, and only key genes that had a significant impact on the risk of recurrence were retained.

[0085] See Figure 2In this embodiment, RCCD1, PRSS27, and NAP1L5 were identified as the core genes for predicting the risk of prostate cancer recurrence through LASSO regression screening. The expression levels of these three genes were significantly correlated with prostate cancer recurrence, and they were key indicators for model construction.

[0086] S43. Risk scoring formula establishment:

[0087] Based on the regression coefficients of each core gene obtained from LASSO regression analysis, a formula for prostate cancer recurrence risk scoring was established:

[0088] RiskScore=a×Expr(RCCD1)+b×Expr(PRSS27)+c×Expr(NAP1L5)

[0089] Where: a, b, and c are the regression coefficients corresponding to each core gene, obtained by fitting the training set data, and are quantitative weight parameters. The positive or negative value of the coefficient reflects the correlation between gene expression level and the risk of recurrence of prostate cancer (positive coefficient indicates that the risk of recurrence increases with the increase of gene expression level; negative coefficient indicates that the risk of recurrence decreases with the increase of gene expression level). The absolute value of the coefficient reflects the degree of influence of the gene on the risk of recurrence. Expr(RCCD1), Expr(PRSS27), and Expr(NAP1L5) are the relative mRNA expression levels of each gene after S2 normalization.

[0090] S5. Prognostic stratification validation of prostate cancer recurrence risk:

[0091] Using a risk scoring model constructed with S4, recurrence risk scores were calculated for prostate cancer patients in the validation set and external independent sample sets. Multi-dimensional prognostic stratification validation was also conducted to verify the model's predictive accuracy and clinical applicability. The specific procedures are as follows:

[0092] S51. Risk Score Calculation and Grouping:

[0093] The standardized expression levels of RCCD1, PRSS27, and NAP1L5 genes from 60 patients in the validation set and 50 patients in the external independent sample set of prostate cancer were substituted into the risk scoring formula to calculate the personalized recurrence risk score for each patient.

[0094] Using the median risk score of patients in the training set as a preset threshold, patients in the validation set and external independent sample set were divided into a high-risk group (risk score > median value) and a low-risk group (risk score ≤ median value).

[0095] S52. Relapse-free survival analysis:

[0096] The Kaplan-Meier method was used to plot relapse-free survival curves for patients in the high-risk and low-risk groups. The differences in relapse-free survival rate and relapse-free survival time between the two groups were compared using the Log-rank test, with a significance level of α=0.05.

[0097] See Figure 3 The results showed that the recurrence-free survival time of patients in the high-risk group was significantly shorter than that in the low-risk group, and the 1-year, 3-year, and 5-year recurrence-free survival rates of the high-risk group were significantly lower than those of the low-risk group (P<0.001). This indicates that the risk scoring model constructed in this invention can effectively distinguish between high- and low-risk groups for prostate cancer recurrence.

[0098] S53. Evaluation of Model Predictive Performance:

[0099] The predictive efficacy of the risk scoring model was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC), and compared with the predictive efficacy of traditional clinical indicators (PSA level, Gleason score, TNM stage).

[0100] See Figure 4 The results showed that the risk scoring model of the present invention had an AUC value of 0.85-0.90 for predicting the risk of prostate cancer recurrence, which was significantly higher than that of traditional indicators (AUC value 0.60-0.75). This indicates that the model has higher predictive accuracy and specificity and can more accurately predict the risk of recurrence in prostate cancer patients.

[0101] S5. Correlation analysis of clinical pathological features:

[0102] The association between high-risk and low-risk patients and clinicopathological characteristics (age, Gleason score, TNM stage, PSA level) was analyzed. The results showed that the proportion of patients with Gleason score ≥8, TNM stage III-IV, and PSA level >10 ng / mL was significantly higher in the high-risk group than in the low-risk group (P<0.05), indicating that the risk score is positively correlated with the malignancy of prostate cancer, further validating the clinical significance of the model.

[0103] S6. Development of a prostate cancer recurrence risk detection kit:

[0104] Based on the detection methods and biomarkers described in the above embodiments, a prostate cancer recurrence risk detection kit suitable for routine clinical testing was developed. This kit can simultaneously detect the methylation level of the TACR2 gene and the expression levels of the TACR2, LPIN3, RCCD1, PRSS27, and NAP1L5 genes. It is simple, rapid, and provides stable results, making it suitable for clinical testing in medical institutions at all levels. The specific components are as follows:

[0105] S61, Core components of the dosage box:

[0106] Nucleic acid extraction reagent kit: including lysis buffer, proteinase K solution, phenol-chloroform mixture, 75% anhydrous ethanol, nucleic acid elution buffer, centrifuge column and collection tube, used to rapidly extract qualified genomic DNA and total RNA from tumor tissue or peripheral blood samples;

[0107] RT-qPCR detection kit: includes reverse transcription buffer, reverse transcriptase, RNase inhibitor, dNTP mixture, specific upstream and downstream primers for each gene (TACR2, LPIN3, RCCD1, PRSS27, NAP1L5) and internal reference gene GAPDH, 2×SYBR Green real-time quantitative PCR solution, and enzyme-free water;

[0108] MSP detection reagent kit: includes bisulfite modification solution, modification and purification reagent, TACR2 gene methylation / nonmethylation specific primers, 2×TaqPCR mixture, DNA loading buffer, and 2% agarose prepreg.

[0109] Quality control group: includes positive control (containing standards of methylated TACR2 gene and various highly expressed genes), negative control (containing standards of unmethylated TACR2 gene and various low-expressed genes), and blank control (enzyme-free water), used for quality control throughout the experimental process to avoid false positive and false negative results;

[0110] Supporting reagents and consumables include nucleic acid dyes, PCR tubes, 96-well real-time PCR plates, sealing films, etc.

[0111] S62. Reagent Kit Instructions:

[0112] The kit comes with a detailed instruction manual, which clearly describes the kit's applicable scope, sample collection and preservation methods, nucleic acid extraction steps, RT-qPCR detection procedure, MSP detection procedure, result interpretation criteria, quality control requirements, precautions, etc., to guide clinical trial personnel in standardized operation. The kit's detection process can be completed within 4 to 6 hours, and the test results can be directly used for subsequent risk score calculations.

[0113] S63. Reagent kit performance verification:

[0114] The sensitivity, specificity, repeatability, and stability of the reagent kit were validated.

[0115] Sensitivity: It can detect nucleic acid samples as low as 1 ng / μL and accurately distinguish samples with a methylation level difference of ≥5%;

[0116] Specificity: Amplification is performed only on the target gene and methylation sites, with no cross-reaction or non-specific amplification;

[0117] Repeatability: The coefficient of variation (CV) of multiple tests on the same sample is <5%, and the test results of different personnel and different laboratories are consistent.

[0118] Stability: The reagent kit showed no significant changes in performance indicators when stored at 2~8℃ for 6 months, and remained stable when stored at -20℃ for 12 months.

[0119] Based on the biomarker and risk scoring model of this invention, a prostate cancer recurrence risk prediction system is built to automate the analysis of gene testing data, calculate risk scores, and output recurrence risk results. This system is suitable for clinicians to quickly assess patients' recurrence risk. The system includes hardware and software components, and its specific architecture is as follows:

[0120] The hardware components include a sample detection module (real-time quantitative PCR instrument, gel imaging system, nucleic acid and protein detector), a data processing module (computer, server), and a result output module (printer, display screen). All hardware devices are interconnected through data interfaces to ensure real-time transmission and processing of test data.

[0121] See Figure 5 The system is partly developed using Python / Java, embedding the risk scoring formula and prognostic stratification criteria of this invention, and mainly includes three functional modules:

[0122] The detection module 100 is used to detect the DNA methylation level and / or gene expression level of the above biomarkers. It supports manual entry or automatic import of gene expression level data and TACR2 gene methylation level data, and allows data editing, saving, and exporting.

[0123] The analysis module 200 is used to input the detection results obtained by the detection module 100 into a risk scoring model to calculate a risk score, and to determine the risk of prostate cancer recurrence based on the comparison between the risk score and a preset threshold. The module automatically calculates the patient's recurrence risk score by substituting the entered detection data into a preset risk scoring formula, and compares it with a preset threshold to quickly determine whether the patient has a high or low risk of recurrence. Simultaneously, it can generate a patient risk analysis report, including gene testing results, risk score, risk level, and clinical recommendations.

[0124] The output module 300 is used to output the recurrence risk assessment results from the analysis module. It supports exporting risk analysis reports in formats such as PDF and Word, and can be printed directly. The system also has data storage capabilities, allowing for long-term preservation of patient test data and risk assessment results, facilitating clinical follow-up and data traceability.

[0125] In the analysis module 200, the risk scoring model is as follows:

[0126] RiskScore=a×Expr(RCCD1)+b×Expr(PRSS27)+c×Expr(NAP1L5)

[0127] In the formula, RiskScore is the risk score, Expr is an abbreviation for Expression, representing the gene expression level, specifically the mRNA transcription level of the corresponding gene in the prostate cancer patient sample; RCCD1, PRSS27, and NAP1L5 are genes related to prostate cancer recurrence / prognosis selected by LASSO regression; a, b, and c are the coefficients corresponding to the gene expression levels.

[0128] When the risk score is higher than the preset threshold, it is judged as a high risk of recurrence of prostate cancer; when the risk score is lower than or equal to the preset threshold, it is judged as a low risk of recurrence of prostate cancer.

[0129] After clinical trial personnel input the gene testing data into the system, the system completes the calculation and outputs the risk assessment results. It is easy to operate, requires no professional bioinformatics knowledge, and is suitable for clinicians to quickly conduct prostate cancer recurrence risk assessment, providing immediate decision support for developing individualized follow-up monitoring plans and intervention treatment strategies.

[0130] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. The use of a biomarker in the preparation of a product for assessing the risk of prostate cancer recurrence, characterized in that, The biomarkers include the TACR2 gene and the LPIN3 gene.

2. The application according to claim 1, characterized in that, The biomarkers also include at least one of the RCCD1 gene, PRSS27 gene, and NAP1L5 gene.

3. The application according to claim 1, characterized in that, The product is used to detect the DNA methylation level and / or gene expression level of the biomarker.

4. The application according to claim 3, characterized in that, The DNA methylation level refers to the methylation level of the TACR2 gene promoter region, and the gene expression level refers to the mRNA transcription level.

5. A reagent for assessing the risk of prostate cancer recurrence, characterized in that, Includes detection reagents for detecting the biomarkers of claim 1 or 2.

6. The reagent according to claim 5, characterized in that, The reagents include at least one of methylation-specific PCR reagents for detecting the methylation level of biomarker DNA and RT-qPCR reagents for detecting the expression level of biomarker genes.

7. The reagent according to claim 6, characterized in that, The methylation-specific PCR reagent is designed for the TACR2 gene promoter region.

8. A predictive system for assessing the risk of prostate cancer recurrence, characterized in that, include: The detection module is used to detect the DNA methylation level and / or gene expression level of the biomarker as described in claim 1 or 2; The analysis module is used to input the detection results obtained by the detection module into the risk scoring model to calculate the risk score, and to determine the risk of prostate cancer recurrence based on the comparison between the risk score and a preset threshold. The output module is used to output the recurrence risk assessment results of the analysis module.

9. The prediction system according to claim 8, characterized in that, In the analysis module, the risk scoring model is: RiskScore=a×Expr(RCCD1)+b×Expr(PRSS27)+c×Expr(NAP1L5) In the formula, RiskScore is the risk score, Expr is an abbreviation for Expression, which represents the gene expression level, specifically the mRNA transcription level of the corresponding gene in the prostate cancer patient sample; RCCD1, PRSS27, and NAP1L5 are genes screened by LASSO regression that are associated with prostate cancer recurrence / prognosis. a, b, and c are coefficients corresponding to gene expression levels; When the risk score is higher than the preset threshold, it is judged as a high risk of recurrence of prostate cancer; when the risk score is lower than or equal to the preset threshold, it is judged as a low risk of recurrence of prostate cancer.

10. The use of the biomarker of claim 1 or 2 in the preparation of a product for prognostic assessment of prostate cancer.

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