Integrated multi-omics analysis of neutrophil extracellular trap-related genes in renal cell carcinoma using bioinformatics and machine learning

Renal cell carcinoma (RCC) poses high recurrence/metastasis risk with limited advanced therapy. The role of neutrophil extracellular traps (NETs) in RCC remains unclear. This study aims to identify core NET-related genes and validate their molecular subtyping and prognostic value. Five GEO datasets were integrated to identify DEGs, subsequent functional enrichment and WGCNA extracted key modules that were cross-referenced with potential genes systematically compiled from GeneCards and literature review. Three machine learning algorithms (LASSO, SVM-RFE, RF) were refined core genes. ROC analysis validated diagnostic performance, and a nomogram was constructed. Consensus clustering defined molecular subtypes, which were subsequently characterized by immune infiltration, pathway activity, and validated in the independent TCGA-KIRC cohort for prognosis and clinicopathological correlations. We identified eight core NET-related genes with excellent diagnostic accuracy (AUCs 0.989/0.987). Based on these genes, patients were classified into two molecular subtypes: the C1 subtype exhibited high immune cell infiltration, particularly of activated CD8⁺ T cells and MDSCs, but was associated with poor prognosis and advanced tumor stage, while the C2 subtype showed low immune infiltration, was enriched in metabolic pathways, and correlated with favorable survival outcomes. Drug sensitivity analysis identified Capsaicin as a potential therapeutic agent. The eight-gene NET signature demonstrates strong diagnostic accuracy and enables molecular subtyping of RCC with validated prognostic significance, highlighting its potential as a prognostic biomarker and therapeutic guide.