A Predictive Model Based on Pyroptosis-Related Gene Features Can Effectively Predict Clear Cell Renal Cell Carcinoma Prognosis and May Be an Underlying Target for Immunotherapy

METHODS: The clinical information and RNA-seq data of ccRCC patients were collected from the TCGA dataset to first explore differential pyroptosis-related genes (PRGs). Univariate Cox regression and consensus clustering were applied to identify ccRCC subtypes. The prognostic PRGs were subjected to LASSO regression analysis to establish a prognostic model and to investigate its value and function. Finally, the relationship of the model immunity checkpoints and immunity infiltration was assessed. RESULTS: The receiver operating characteristic (ROC) showed that the 1-year, 3-year, and 5-year prediction rates of the prognostic model were 0.715, 0.693, and 0.732, respectively. The high-risk group had lower overall survival and higher stage than the low-risk group. Functional enrichment analysis showed that PRGs were significantly enriched mainly in the PPAR pathway, inflammatory pathway, and immune activity. ccRCC patient prognosis correlates with immune components in the microenvironment, and immune checkpoint molecules are significantly expressed in the high-risk group. Immunotherapy may be effective in the high-risk group. CONCLUSION: Pyroptosis-related gene has an important impact on the progression of ccRCC and can be used as an independent predictor of patient prognosis. In addition, immune checkpoint molecules are significantly upregulated in high-risk populations, which may be a potential target for immunotherapy.