Subtyping of Renal Clear Cell Carcinoma based on gene energy metabolism data and analysis of prognosis and immune characteristic

In this study, we classified immune subtypes of renal clear cell carcinoma (KIRC) based on immune metabolism-related genes using gene expression data from TCGA, GO, and KEGG databases. The NMF algorithm was employed for immune subtype clustering, resulting in three distinct categories. Immune infiltrate analysis, conducted via the TIMER database, revealed significant differences in immune scores among the subtypes. Differentially expressed genes were identified using the DESeq2 method in R. Prognostic analysis, performed through multivariate Cox regression, identified high-risk genes in each subtype: IRF9, TMSB4XP8, and TNFRSF25 in subtype 1; NFKBIB, HRAS, and TKFC in subtype 2; and CXCL2, FCGR3B and TGFBR3 in subtype 3. Gene function enrichment analysis using KEGG further elucidated the immune characteristics of the subtypes. Notably, CCR10 and IL17RE were protective in subtype 2, while CXCL2 and FCGR3B were risk factors. In subtype 3, NR4A3 was protective, while NFKBIB and TKFC were risk factors. Based on immune infiltration analysis and prognostic risk models, targeted immunotherapy strategies were proposed: DC or macrophage immunotherapy for subtype 1, T cell or B cell-targeted treatments for subtype 3.