Constructing a Neutrophil Extracellular Trap Model Based on Machine Learning to Predict Clinical Outcomes and Immunotherapy Response in Renal Cell Carcinoma

Abstract Neutrophil extracellular traps (NETs) represent a novel form of inflammatory cell death within neutrophils. Emerging research indicates that NETs promote cancer progression and metastasis in various ways. This study aims to provide prognostic NETs characteristics and therapeutic targets for patients with renal cell carcinoma (RCC). NMF analysis was conducted on 89 NET-related genes in the training cohort. Subsequently, WGCNA networks were utilized to study the subtype feature genes. Six machine learning algorithms were assessed for model training, and the optimal model was selected based on 1-year, 3-year, and 5-year AUC values. A NETs signature was then constructed to predict overall survival in RCC patients. Furthermore, multi-omics validation was performed based on NETs signature. Finally, stable knockout key gene RCC cell lines were established to verify the biological function of KCNN4 both in vitro and in vivo. This study highlights the emerging hot topic of NETs in RCC. We provide a prognostic NETs signature and identify multiple roles of KCNN4 in RCC. This work contributes to risk stratification and the identification of new therapeutic targets for RCC patients.