Establishment and validation of a polygene prognostic model for clear cell renal cell carcinoma

Purpose: To establish an effective prognostic model for patients with clear cell renal cell carcinoma (ccRCC). Methods: We identified four hub differentially expressed genes (DEGs) in Gene Expression Omnibus (GEO) database and verified them in the Cancer Gene Atlas (TCGA), STRING, UALCAN, TIMER, and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We then used TCGA and International Cancer Genome Consortium (ICGC) to identify tumor pathway molecules highly correlated with hub DEGs. And by further LASSO and Cox regression analysis, we successfully identified five genes as prognostic factors. Results: We successfully identified a risk prediction model consisting of five genes: IGF2BP3, CDKN1A, GSDMB, FABP5, RBMX. We next distributed patients into low-risk and high-risk groups using the median as a cutoff. The low-risk group obviously had better survival than those in the predicted high-risk group. The results showed discrepancies in tumor-associated immune cell infiltration between risk groups. We also combined the risk model with clinical variables to create a nomogram. Conclusion: Our model has a satisfactory predictive effect on the prognosis of ccRCC patients and may provide new ideas for future immune therapy.