Kidney cancer patient prognosis evaluation method based on S100A11 protein expression quantity

Abstract

The invention relates to the technical field of diagnosis, in particular to application of S100A11 as a biomarker in preparation of a renal cell carcinoma prognosis evaluation kit, the renal cell carcinoma patient prognosis evaluation kit and an evaluation method. The invention provides a method for evaluating the prognosis and immune microenvironment state of a patient with renal cell carcinoma (RCC) based on the expression level of S100A11, which has good stability, accuracy and operability, can realize hierarchical management and individualized treatment guidance of patient risks, and has remarkable clinical and social values.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tumor molecular diagnosis, in particular to a renal cell carcinoma patient prognosis evaluation kit and evaluation method based on S100A11 protein expression. BACKGROUND

[0002] Renal cell carcinoma (RCC) is the most common type of malignant tumor in the kidney, accounting for more than 85% of adult kidney cancer, and its incidence has been increasing in recent years. Because kidney cancer is usually less sensitive to conventional radiotherapy and chemotherapy, immunotherapy and targeted drugs have gradually become the first-line regimen. Although the treatment regimen has been relatively standardized, the clinical outcomes of patients still differ significantly, which indicates that the existing risk grading method is not sufficient to reflect the biological differences between tumors.

[0003] Currently, the prognosis of RCC patients in clinical practice is mainly determined by traditional indicators such as TNM staging, histological subtype, and Fuhrman nuclear grading. These pathological parameters have certain value in guiding clinical decision-making, but they cannot effectively distinguish patients with different prognoses in the same stage, nor can they predict the treatment response of individual patients to immunotherapy drugs (such as PD-1 or PD-L1 blockers), which limits the implementation of precision treatment strategies.

[0004] In recent years, with the deepening of research on tumor immune microenvironment (TIME), researchers are trying to mine molecular features with predictive immune response or prognosis ability through transcriptome data. However, some potential molecular markers reported so far, such as PD-L1 and CTLA-4, still have problems such as unstable detection results, weak specificity, and highly heterogeneous expression levels, making it difficult to be independently applied to prognosis evaluation.

[0005] In summary, the existing technology in the prognosis evaluation of kidney cancer has the following shortcomings: 1. Lack of independent molecular markers that can be stably detected in clinical tissue samples; 2. Traditional pathological staging methods cannot meet the needs of precise stratification; 3. The expression of known molecular markers is unstable, and the cancer applicability is poor; 4. There is no method to integrate tumor immune characteristics, patient survival information, and marker expression systems to form an easy-to-promote evaluation method.

[0006] The S100 protein family is a class of calcium-binding proteins. The S100 protein structure contains two EF-hand domains. After binding with calcium ions, this binding will cause changes in protein conformation, thereby affecting the interaction of the protein with its target. S100A11 has biological functions such as regulating cell growth, cell dynamics, cell migration and signal transduction. The expression characteristics of S100A11 in renal cell carcinoma, the clinical prognostic value and the relationship with immunotherapy response have not been systematically revealed, and no evaluation method that can be popularized and applied has been formed.

[0007] There is no report on a new method for evaluating the prognosis of renal cell carcinoma based on the expression level of S100A11 in tumor tissue. SUMMARY

[0008] The purpose of the present application is to provide a method for applying S100A11 expression level in molecular risk stratification and immune microenvironment evaluation of renal cell carcinoma (RCC), which has the characteristics of stable and repeatable, strong independent prediction, easy to operate clinically, etc., and makes up for the precision defects of the existing evaluation system in the era of immunotherapy. By integrating tissue RNA sequencing data (The Cancer Genome Atlas, TCGA), immunohistochemical staining (Immunohistochemistry, IHC) results and immune-related feature analysis, the molecular diagnostic value of S100A11 as an independent poor prognostic indicator is determined.

[0009] In order to achieve the above purpose, the present application adopts the following technical solutions to construct the risk evaluation and immune prediction system of S100A11 in renal cell carcinoma:

[0010] 1. Molecular detection method: the expression level of S100A11 protein in RCC tissue sample is detected by IHC method, and the patients are divided into high or low expression group of S100A11 protein according to the expression amount. This grouping method can stably distinguish different prognosis populations, and K-M survival analysis verifies that the overall survival (OS) and progression-free survival (PFS) of high and low expression groups of S100A11 are significantly different, and the OS and PFS of patients with high expression of S100A11 are significantly reduced.

[0011] 2. Constructing independent prognostic model: In combination with the multivariate Cox regression model analysis of 370 RCC patients, the expression of S100A11 still maintained statistically significant risk prediction ability after controlling clinical variables such as age, gender, clinical stage, Fuhrman grade, etc. The OS of the high expression S100A11 group was significantly shorter than that of the low expression group (OS: HR = 3.55, P = 0.019), further proving that it is an independent poor prognostic factor for renal cell carcinoma.

[0012] 3. Construction of clinical scoring system: Based on the expression of S100A11 and clinical variables such as clinical stage information, the present application jointly constructs a prognostic scoring system for RCC patients, and establishes a visual nomogram through a multivariate Cox proportional hazards model. The nomogram can provide 1-year, 2-year, 3-year, 4-year and 5-year overall survival (OS) probability prediction for individual patients, helping clinicians achieve intuitive, quantitative risk assessment and individualized decision support. In the nomogram scoring system constructed by the present application, each variable is assigned a corresponding weight score according to its impact on patient prognosis, thereby achieving quantitative prediction of overall survival probability. Specifically, those aged 21-60 years old are scored 10 points, those aged 61-88 years old are scored 0 points; in terms of gender, females are scored 0 points and males are scored 2 points; in terms of pathological subtype, KIRC patients are scored 0 points, KICH patients are scored 72 points, and KIRP patients are scored 37 points; in terms of clinical stage, Stage 1 patients are scored 0 points, Stage 2 patients are scored 84 points, and Stage 3 and Stage 4 patients are scored 100 points; finally, in terms of S100A11 expression grouping, high expression is scored 62 points and low expression is scored 0 points. After adding the scores of the above items to obtain the total score, the overall survival (Overall Survival, OS) probability of the patient at 1 year, 2 years, 3 years, 4 years and 5 years can be calculated through the nomogram model, providing accurate individualized risk assessment basis for renal cell carcinoma patients. In order to evaluate the prediction ability of the combined model, the present application uses the receiver operating characteristic curve (Receiver Operating Characteristic curve, ROC) and the corresponding area under the curve (Area Under the Curve, AUC) for quantitative verification. The results show that the combined scoring system shows excellent prediction performance at multiple time points: 1-year AUC is 0.98, 2-year AUC is 0.817, 3-year AUC is 0.851, 4-year AUC is 0.882, and 5-year AUC is 0.894, indicating that the model has very high sensitivity and specificity in short-term and long-term survival prediction.

[0013] 4. Immunological feature evaluation: Through systematic correlation analysis of CIBERSORT immune infiltration data of multiple cancer types, it is found that S100A11 is significantly correlated with various immune cell components in different tumor types. In RCC, S100A11 is positively correlated with pro-inflammatory cells such as plasma cells (R = 0.24), activated NK cells (R = 0.17), and activated CD4 memory T cells (R = 0.19), and is negatively correlated with anti-inflammatory cells such as resting NK cells (R = -0.34) and resting CD4 memory T cells (R = -0.41). These results indicate that high expression of S100A11 may indicate the activation of inflammatory response in the tumor microenvironment, reflecting its role in regulating the immune status of tumors.

[0014] 5. Visualization operation platform and staining standardization: The present application combines immunohistochemical staining (Immunohistochemistry, IHC) technology to establish a standardized process for S100A11 detection based on tissue microarray (Tissue Microarray, TMA), ensuring visualization, semi-quantification, and high reproducibility of the detection.

[0015] The present application fills the gap of the lack of stable and independent prognostic factors in the diagnosis and evaluation system of renal cell carcinoma by introducing S100A11 as a molecular marker, providing a new tool for individualized management of renal cell carcinoma patients, and has good market application prospects.

[0016] Based on the above technical solutions, the first aspect of the present application provides the use of S100A11 as a biomarker in the preparation of a renal cell carcinoma prognosis evaluation kit.

[0017] The second aspect of the present application provides the use of a reagent for detecting the expression level of S100A11 in the preparation of a renal cell carcinoma prognosis evaluation kit.

[0018] Further, the reagent for detecting the expression level of S100A11 is a reagent for detecting the expression level of S100A11 in renal cell carcinoma tissue.

[0019] Further, the source of the renal cell carcinoma tissue sample can be postoperative tumor tissue or a biopsy specimen.

[0020] Further, the reagent for detecting the expression level of S100A11 in a biological sample is selected from a reagent for detecting by immunohistochemical staining (Immunohistochemistry, IHC), qRT-PCR, RNA in situ hybridization (RNAscope), or digital PCR.

[0021] Further, the reagent for detecting the expression level of S100A11 in the biological sample comprises an antibody specifically recognizing human S100A11 and an immunohistochemical reagent.

[0022] The present application combines the immunohistochemical staining (IHC) technology to establish a set of S100A11 detection standardization process based on tissue microarray (TMA), which ensures the visualization, semi-quantification and high repeatability of the detection. The specific operation steps are as follows: the tumor and normal tissue samples removed by surgery are sequentially subjected to formaldehyde fixation, paraffin embedding, and continuous sectioning, and then a tissue microarray (TMA) is constructed. After the chip is dewaxed and hydrated, the target antigen S100A11 is exposed by using a citric acid antigen retrieval solution with pH = 6.0 for antigen retrieval (primary antibody: Proteintech, #10237-1-AP, dilution concentration 1:50), and then the staining and color development processes are completed. The IHC expression scoring system combines two dimensions: staining intensity and staining area. The specific scoring criteria are as follows: staining intensity: negative (0 points), weakly positive (1 point), positive (2 points), and strongly positive (3 points); staining area: <10% (1 point), 10-39% (2 points), 40-69% (3 points), and 70-100% (4 points); the total score calculation method: final score = staining intensity x staining area, with a full score of 12 points. According to the final score, the expression level of the tumor tissue sample is classified as high (10-12 points) or low (0-9 points).

[0023] Further, the high expression of S100A11 is significantly shorter than the low expression in terms of overall survival (OS) and progression-free survival (PFS).

[0024] In a third aspect of the present application, the use of the reagent for detecting the expression level of S100A11 combined with the clinical staging information in the preparation of a renal cell carcinoma prognosis evaluation kit is provided.

[0025] In a fourth aspect of the present application, a renal cell carcinoma prognosis evaluation kit is provided, which comprises a reagent for detecting the expression level of S100A11.

[0026] In a fifth aspect of the present application, a renal cell carcinoma prognosis prediction model is provided, which takes the expression level of S100A11 in the renal cell carcinoma tissue, age, gender, pathological subtype, and clinical stage as input variables, and predicts the prognosis of the renal cell carcinoma patient by calculating the risk score.

[0027] Further, the present application predicts the overall survival (OS) probability of the renal cell carcinoma patient for 1 year, 2 years, 3 years, 4 years, and 5 years by using the nomogram scoring system as shown in Figure 3

[0028] ​The present application is based on S100A11 expression and clinical stage information, and a prognosis scoring system for RCC patients is constructed, and a visual nomogram is established by a multi-factor Cox proportional hazards model. Figure 3 As shown in the formula (I), the nomogram can provide 1-year, 2-year, 3-year, 4-year and 5-year overall survival (OS) probability prediction for individual patients, helping clinicians achieve intuitive and quantitative risk assessment and individualized decision support. In the nomogram scoring system constructed in the present application, each variable is assigned a corresponding weight score according to its effect on patient prognosis, thereby achieving quantitative prediction of overall survival probability.

[0029] Specifically, those aged 21-60 years old are scored 10 points, and those aged 61-88 years old are scored 0 points; in terms of gender, females are scored 0 points and males are scored 2 points; in terms of pathological subtype, KIRC patients are scored 0 points, KICH patients are scored 72 points, and KIRP patients are scored 37 points; in terms of clinical stage, Stage 1 patients are scored 0 points, Stage 2 patients are scored 84 points, and Stage 3 and Stage 4 patients are scored 100 points; finally, in terms of S100A11 expression grouping, high expression is scored 62 points and low expression is scored 0 points. After adding the scores of the above items to obtain the total score, the nomogram model can be used to calculate the overall survival (OS) probability of patients at 1 year, 2 years, 3 years, 4 years and 5 years, providing accurate individualized risk assessment basis for renal cell carcinoma patients.

[0030] In the nomogram scoring system constructed in the present application based on S100A11 expression and clinical stage information, the total score of the patient can be used to intuitively predict the overall survival (OS) probability at different time points. Specifically: for 1-year survival prediction, a total score of 171 points corresponds to a survival probability of about 0.95, a total score of 206 points corresponds to a survival probability of 0.90, and a total score of 243 points corresponds to a survival probability of 0.80; for 2-year survival prediction, a total score of 142 points corresponds to a survival probability of about 0.95, a total score of 177 points corresponds to a survival probability of 0.90, a total score of 214 points corresponds to a survival probability of 0.80, and a total score of 255 points corresponds to a survival probability of 0.60; for 3-year survival prediction, a total score of 108 points corresponds to a survival probability of about 0.95, a total score of 144 points corresponds to a survival probability of 0.90, a total score of 180 points corresponds to a survival probability of 0.80, a total score of 221 points corresponds to a survival probability of 0.60, and a total score of 250 points corresponds to a survival probability of 0.40; for 4-year and 5-year survival prediction, a total score of 73 points corresponds to a survival probability of about 0.95, a total score of 108 points corresponds to a survival probability of 0.90, a total score of 145 points corresponds to a survival probability of 0.80, a total score of 185 points corresponds to a survival probability of 0.60, a total score of 214 points corresponds to a survival probability of 0.40, and a total score of 258 points corresponds to a survival probability of 0.10.

[0031] In a fifth aspect, the present application provides a system for predicting the prognosis of renal cell carcinoma, comprising:

[0032] a data acquisition module for acquiring S100A11 expression level, age, gender, pathological subtype, clinical stage of renal cell carcinoma tissue;

[0033] a prognosis evaluation module for predicting a risk score of prognosis of the renal cell carcinoma patient according to the data obtained by the data acquisition module, and outputting the risk score; the prognosis evaluation module comprises the renal cell carcinoma prognosis prediction model as described above;

[0034] an output module for outputting a prediction result according to the risk score.

[0035] Further, the prediction result is that: according to the risk score, the one-year, two-year, three-year, four-year and five-year overall survival (OS) probability of the renal cell carcinoma patient is predicted; in terms of one-year survival prediction, when the total score is 171, the survival probability is about 0.95, when the total score is 206, the survival probability is 0.90, and when the total score is 243, the survival probability is 0.80; in terms of two-year survival prediction, when the total score is 142, the survival probability is about 0.95, when the total score is 177, the survival probability is 0.90, when the total score is 214, the survival probability is 0.80, and when the total score is 255, the survival probability is 0.60; in terms of three-year survival prediction, when the total score is 108, the survival probability is about 0.95, when the total score is 144, the survival probability is 0.90, when the total score is 180, the survival probability is 0.80, when the total score is 221, the survival probability is 0.60, and when the total score is 250, the survival probability is 0.40; in terms of four-year and five-year survival prediction, when the total score is 73, the survival probability is about 0.95, when the total score is 108, the survival probability is 0.90, when the total score is 145, the survival probability is 0.80, when the total score is 185, the survival probability is 0.60, when the total score is 214, the survival probability is 0.40, and when the total score is 258, the survival probability is 0.10.

[0036] In a sixth aspect, the present application provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to realize the functions of the system as described above.

[0037] The present application has the following advantages:

[0038] 1. The application provides a method for evaluating the prognosis and immune microenvironment state of renal cell carcinoma (RCC) patients based on the expression level of S100A11, which has good stability, accuracy and operability, can realize the hierarchical management and individualized treatment guidance of patient risk, and has significant clinical and social value. In the retrospective clinical cohort constructed by the inventors, by performing immunohistochemical staining of S100A11 protein on tumor tissue samples of RCC patients and digital scoring analysis, combined with RNA expression data and survival information in the TCGA database, the molecular diagnostic value of S100A11 as an independent adverse prognostic factor was systematically verified. K-M survival analysis showed that high expression of S100A11 was significantly associated with the significant shortening of overall survival (OS) and progression-free survival (PFS) of RCC patients; the multivariate Cox regression model further confirmed that after controlling other clinical factors (such as age, gender, clinical stage, Fuhrman grade), the high expression group of S100A11 was still a statistically significant independent adverse prognostic indicator (OS: HR = 3.55, P = 0.019).

[0039] 2. In addition, the combined scoring system (S100A11 + clinical stage) proposed by the application has excellent prediction effect, and the AUC value under the ROC curve shows that it has very high sensitivity and specificity in 1 to 5 year survival prediction, which is significantly better than single clinical indicators. In terms of tumor immune characteristics, high expression of S100A11 is positively correlated with pro-inflammatory immune cells (such as plasma cells, activated NK cells, activated CD4 memory T cells) and negatively correlated with anti-inflammatory immune cells (such as resting NK cells, resting CD4 memory T cells) in RCC, indicating that it may reflect the tumor immune activation state.

[0040] Based on the above results, the application establishes a stable, reproducible and clinically applicable S100A11 renal cell carcinoma risk assessment and immune phenotype identification system through the integration strategy of IHC detection, prognosis modeling and immune characteristic analysis, which can not only realize the early identification and precise treatment guidance of high-risk patients, but also has potential application value in broad-spectrum tumor immune typing. Its promotion will help to optimize the prognosis management mode of renal cell carcinoma patients, reduce the burden of over-treatment, improve the level of individualized medical treatment, and has good social benefits and economic conversion prospects. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 The immunohistochemical staining results of S100A11 in renal cell carcinoma tissue show the expression difference in different patient tissues, and directly reflect the localization and expression intensity of protein level in tumor tissue.

[0042] Figure 2 Part A is a comparison chart of S100A11 immunohistochemical staining results of normal kidney tissue and cancer tissue, and parts B-C are K-M (Kaplan-Meier) survival analysis charts obtained according to the S100A11 expression level, including overall survival (OS) and progression-free survival (PFS), which show that the S100A11 high expression group has a significantly poor prognosis. Part D combines the clinical cohort samples of renal cell carcinoma to show the distribution trend and statistical significance of S100A11 expression in tumor and non-tumor tissues. Part E is the result chart of the multi-factor Cox regression analysis in which S100A11 participates, including a forest plot showing its independent prognostic ability under the control of clinical factors, and parts F-I are ROC curve analysis of the prediction performance of the joint scoring system, which reflects its risk prediction value in the clinic.

[0043] Figure 3 It is a nomogram based on the multi-factor Cox model, which combines S100A11 expression level and clinical stage parameters to predict the survival probability of patients within 1 to 5 years, supporting individualized clinical decision-making.

[0044] Figure 4 It is a correlation heat map of the main immune components in the immune microenvironment of renal cell carcinoma and the expression level of S100A11, which is based on the CIBERSORT algorithm, revealing its potential function in tumor immune regulation and application value in immune evaluation. DETAILED DESCRIPTION

[0045] The specific embodiments provided by the present application will be described in detail below with reference to the examples.

[0046] Example 1: Integrating multi-omics data to identify the expression characteristics and prognostic value of S100A11 in renal cell carcinoma

[0047] Based on the tissue RNA sequencing data of renal cell carcinoma (RCC) samples in the TCGA database, combined with their clinical survival information, the relationship between the expression level of S100A11 and the survival outcome of patients was evaluated. The survival analysis results showed that the overall survival (OS) and progression-free survival (PFS) of patients with high expression of S100A11 were significantly shortened, suggesting that it may be a poor prognostic factor. Multi-factor Cox regression analysis further verified its independence, and after controlling variables such as age, gender, clinical stage, and Fuhrman grade, high expression of S100A11 was still significantly associated with increased risk of death (HR = 3.55, P = 0.019).

[0048] Example 2: Protein expression level detection of S100A11 and establishment of immunohistochemical scoring standard

[0049] To verify the consistency of protein level with RNA level expression and establish a standardized detection process, we performed immunohistochemical (IHC) staining on 370 paraffin-embedded tissue samples of renal cell carcinoma (RCC). After deparaffinization and antigen retrieval, all samples were stained with S100A11 antibody (Proteintech, #10237-1-AP, dilution concentration 1:50), and scored independently by two pathologists. The scoring standard combined staining intensity (0-3 points) and staining area (1-4 points), and the final product was used as the total score. Finally, the patients with renal cell carcinoma were divided into low expression (0-9 points) and high expression (10-12 points) of S100A11 group.

[0050] Example 3: Construction of nomogram model based on S100A11 and clinical stage combination and prognosis scoring system

[0051] Combining the expression level of S100A11 and the clinical stage information of patients, a multi-factor Cox regression model was constructed, and a nomogram was drawn to predict the OS probability of patients at 1 year, 2 years, 3 years, 4 years and 5 years. In the nomogram scoring system, each variable is assigned a corresponding weight score according to its impact on patient prognosis, so as to realize the quantitative prediction of overall survival probability. Specifically, those aged 21-60 years old were scored 10 points, and those aged 61-88 years old were scored 0 points; in terms of gender, females were scored 0 points and males were scored 2 points; in terms of pathological subtype, KIRC patients were scored 0 points, KICH patients were scored 72 points, and KIRP patients were scored 37 points; in terms of clinical stage, Stage 1 patients were scored 0 points, Stage 2 patients were scored 84 points, Stage 3 and Stage 4 patients were scored 100 points; finally, in terms of S100A11 expression grouping, high expression was scored 62 points and low expression was scored 0 points. After adding the scores of the above items to obtain the total score, the nomogram model can be used to calculate the overall survival (OS) probability of patients at 1 year, 2 years, 3 years, 4 years and 5 years, providing accurate individualized risk assessment basis for patients with renal cell carcinoma. In the nomogram scoring system based on S100A11 expression and clinical characteristics constructed in the present application, the total score of patients can be used to intuitively predict the overall survival (OS) probability at different time points (Fig. 3) Figure 3). Specifically: for one-year survival prediction, a total score of 171 corresponds to a survival probability of about 0.95, 206 corresponds to 0.90, and 243 corresponds to 0.80; for two-year survival prediction, 142 corresponds to a survival probability of about 0.95, 177 corresponds to 0.90, 214 corresponds to 0.80, and 255 corresponds to 0.60; for three-year survival prediction, 108 corresponds to a survival probability of about 0.95, 144 corresponds to 0.90, 180 corresponds to 0.80, 221 corresponds to 0.60, and 250 corresponds to 0.40; for four- and five-year survival prediction, 73 corresponds to a survival probability of about 0.95, 108 corresponds to 0.90, 145 corresponds to 0.80, 185 corresponds to 0.60, 214 corresponds to 0.40, and 258 corresponds to 0.10. The nomogram performs well in the independent clinical cohort. ROC curve analysis shows that the AUC values of the combined scoring system at multiple time points are: 1-year AUC is 0.98, 2-year AUC is 0.817, 3-year AUC is 0.851, 4-year AUC is 0.882, and 5-year AUC is 0.894, with very high sensitivity and specificity. The model can provide intuitive basis for individualized prognosis evaluation and treatment decision of renal cell carcinoma patients.

[0052] Example 4: Immunological correlation analysis of S100A11 in the immune microenvironment of renal cell carcinoma

[0053] In this embodiment, the correlation between S100A11 expression level and different immune cell subgroups in the tumor immune microenvironment was evaluated by CIBERSORT algorithm. In RCC, S100A11 was positively correlated with pro-inflammatory cells (such as plasma cells R = 0.24, activated NK cells R = 0.17, activated CD4 memory T cells R = 0.19), and negatively correlated with anti-inflammatory cells (such as resting NK cells R = -0.34, resting CD4 memory T cells R = -0.41) Figure 4 ). It is indicated that high expression of S100A11 may reflect the immune activation status in the tumor microenvironment, and can be used to evaluate the response tendency of immunotherapy.

[0054] The above has specifically described the preferred embodiments of the present application, but the present application is not limited to the described embodiments, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are all included in the scope defined by the claims of the present application.

Claims

1. Application of reagents for detecting S100A11 expression levels in the preparation of a prognostic assessment kit for renal cell carcinoma.

2. The application according to claim 1, characterized in that, The reagent used to detect the expression level of S100A11 is the same reagent used to detect the expression level of S100A11 in renal cell carcinoma tissue.

3. The application according to claim 2, characterized in that, The reagents used to detect the expression level of S100A11 in biological samples are selected from those used by immunohistochemical staining, qRT-PCR, RNA in situ hybridization, or digital PCR.

4. A renal cell carcinoma prognostic assessment kit, characterized in that, The kit contains reagents for detecting the expression level of S100A11.

5. Application of reagents for detecting S100A11 expression levels in combination with clinical staging information in the preparation of a prognostic assessment kit for renal cell carcinoma.

6. A prognostic prediction model for renal cell carcinoma, characterized in that, The prediction model uses the expression level of S100A11 in renal cell carcinoma tissue, age, gender, pathological subtype, and clinical stage as input variables, and predicts the prognosis of renal cell carcinoma patients by calculating a risk score.

7. The prediction model according to claim 6, characterized in that, The overall survival probability of renal cell carcinoma patients at 1, 2, 3, 4 and 5 years was predicted using the nomogram scoring system shown in Figure 3.

8. A system for predicting the prognosis of renal cell carcinoma, characterized in that, include: The data acquisition module is used to acquire information such as S100A11 expression level, age, sex, pathological subtype, and clinical stage of renal cell carcinoma tissue. The prognostic assessment module predicts the risk score of renal cell carcinoma patients based on the data obtained by the data acquisition module and outputs the risk score; the prognostic assessment module includes the renal cell carcinoma prognostic prediction model as described in claim 6. The output module outputs the prediction result based on the risk score.

9. The system according to claim 8, characterized in that, Individuals aged 21–60 years receive 10 points, while those aged 61–88 years receive 0 points. Regarding gender, females receive 0 points, and males receive 2 points. In terms of pathological subtype, KIRC patients receive 0 points, KICH patients receive 72 points, and KIRP patients receive 37 points. For clinical staging, Stage 1 patients receive 0 points, Stage 2 patients receive 84 points, and Stage 3 and Stage 4 patients both receive 100 points. Finally, regarding S100A11 expression grouping, high expression receives 62 points, and low expression receives 0 points. The total score is obtained by adding up all the scores. For one-year survival prediction, a total score of 171 corresponds to a survival probability of approximately 0.95, 206 corresponds to 0.90, and 243 corresponds to 0.

80. For two-year survival prediction, a score of 142 corresponds to a survival probability of approximately 0.95, 177 corresponds to 0.90, 214 corresponds to 0.80, and 255 corresponds to 0.

60. For three-year survival prediction, a score of 108 corresponds to a survival probability of approximately 0.95, 144 corresponds to 0.90, 180 corresponds to 0.80, 221 corresponds to 0.60, and 250 corresponds to 0.

40. For four- and five-year survival prediction, a score of 73 corresponds to a survival probability of approximately 0.95, 108 corresponds to 0.90, 145 corresponds to 0.80, 185 corresponds to 0.60, 214 corresponds to 0.40, and 258 corresponds to 0.

10.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the functions of the system as described in claim 8.

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