Construction and validation of a survival prognostic model for clear cell renal cell carcinoma

Objective: Utilizing expression data of clear cell renal cell carcinoma (ccRCC) genes from the Cancer Genome Atlas (TCGA) database, this study employs weighted gene co-expression network analysis (WGCNA) and Cox regression analysis to identify genes associated with the occurrence and development of ccRCC, thereby providing a scientific basis for its treatment. Materials and methods: Differentially expressed genes between tumor and control groups were identified by preprocessing and batch correction of ccRCC transcriptome data in the TCGA database using the Wilcoxon test. Prognostic prediction models were established through a combination of WGCNA analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. The reliability of these prognostic models was evaluated by plotting Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves and by further analyzing the relationship between model gene expression levels, tumor staging, and tumor grading. Results: Post-batch correction, M2-type macrophage infiltration was pronounced in tumor tissue, and 13 out of 290 screened relevant differential genes were included in the prognostic model. The Kaplan-Meier survival curves indicated that the 3- and 5-year overall survival rates were significantly higher in the low-risk group compared with the high-risk group (83.7 vs. 69.1%; 75.7 vs. 52.6%, p = 1.169e-08). The area under the ROC curve was 0.732, signifying strong predictive power for the survival curve. In this model, the expression levels of 11 genes were positively correlated with tu mor stage and pathological grade, whereas the remaining 2 genes were negatively correlated. Conclusion: This model can predict the overall survival of patients with ccRCC and has the potential to become an important therapeutic target.