Abstract

Background: Recent research has identified pyroptosis as a distinct form of programmed cell death that is strongly correlated with tumor outcomes. However, its value as a prognostic biomarker in clear cell renal cell carcinoma (ccRCC) is not yet fully established. Methods: Differential expression of pyroptosis-related genes between ccRCC and normal tissues was identified using the DESeq2 R package. Next, univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses using data from The Cancer Genome Atlas cohort were applied to develop a prognostic model. Univariate and multivariate prognostic analyses were conducted to determine whether the risk score could independently predicted patient prognosis after adjustment for clinicopathological characteristics. A nomogram was then constructed to estimate patient survival probability, supporting clinical decision-making. Expression of pyroptosis-related proteins was assessed using tissue proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Additionally, the Tumor-Immune System Interaction Database (TISIDB) database was used to analyze immune cell infiltration, whereas gasdermin B (GSDMB) expression in ccRCC cell lines was assessed by Western blot (WB) and quantitative PCR (qPCR) experiments, with immunohistochemistry applied to measure the levels of GSDMB and programmed cell death protein 1 (PD-1). Results: A prognostic risk model was developed using nine genes related to pyroptosis (AIM2, CASP5, GSDMB, GSDMD, NLRP7, NOD2, PYCARD, SCAF11, and NLRP6), employing both univariate Cox and LASSO regression analyses. Based on the median risk score, ccRCC samples were divided into high- and low-risk categories, with Kaplan-Meier analysis revealing poorer survival rates for the high-risk group. Time-dependent receiver operating characteristic curve analysis confirmed the predictive performance of the model for ccRCC prognosis. Fu