PANoptosis-related gene clusters and prognostic risk model in clear cell renal cell carcinoma - Zhao, Qiyue; Xie, Huadong; Li, Chaofu; Xiong, Yanxiang; Fan, Yongyi; Huang, Yuanbi; Zhan, Yi; Zeng, Siping (Frontiers in Genetics, 2025)
BackgroundDespite advancements in targeted therapies, the prognosis for clear cell renal cell carcinoma (ccRCC) remains poor, particularly for metastatic cases. PANoptosis, a newly discovered programmed cell death pathway involving crosstalk among pyroptosis, apoptosis, and necroptosis, has an undefined role in ccRCC pathogenesis and prognosis, representing a critical knowledge gap.MethodsWe conducted a bioinformatics analysis of the expression PANoptosis-related genes (PRGs) in 524 ccRCC patients from the TCGA and GEO databases. Three ccRCC clusters were identified based on PRG expression. Innovatively, we developed a prognostic risk model using LASSO and Cox regression on three hub genes (WDR72, ANLN, SLC16A12), integrating multi-omics data for immune microenvironment, tumor mutation burden (TMB), cancer stem cell (CSC) index, and drug sensitivity assessment. Expression of these hub genes was further validated by RT-qPCR.ResultsWe found that most of the PRGs were upregulated in ccRCC tumors with low mutation rates, and 18 PRGs exhibited a significant correlation with ccRCC patient survival. Patients were stratified into three PRG clusters and two gene clusters, which were significantly associated with ccRCC prognosis. We constructed a prognostic risk model based on three genes, dividing ccRCC patients into high- and low-risk groups. The predictive value of this risk model was confirmed by ROC curves. High-risk scores were associated with an increased stromal score, immune score, and tumor mutation burden (TMB), but they were associated with a decrease in the cancer stem cell (CSC) index. RT-qPCR confirmed the expression of WDR72, ANLN, and SLC16A12 in ccRCC tissues and cell lines. Additionally, the PRG risk score model exhibited significant associations with sensitivity to multiple drugs.ConclusionThis novel PANoptosis-based model addresses the knowledge gap by providing enhanced prognostic accuracy and clinical utility for personalized ccRCC management, potentially guiding targeted and immunotherapeutic strategies.