An immunometabolism-related signature for renal clear cell carcinoma diagnosis and therapeutic target

Abstract

Kidney renal clear cell carcinoma (KIRC) lacks sensitive early diagnostic markers and effective therapeutic guidance. Given the tight crosstalk between tumor metabolism and immunity, we investigated immunometabolism for biomarker discovery. Transcriptomes from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus were integrated. Immunometabolism-related genes were screened by weighted gene co-expression network analysis and differential expression, followed by three machine learning algorithms (least absolute shrinkage and selection operator, Support Vector Machine–Recursive Feature Elimination (SVM-RFE), and random forest) to select features and build a diagnostic model. Performance was validated in external cohorts. Multi-omics correlation, immune infiltration, drug-sensitivity, and survival analyses were conducted. Functional assays were performed in vitro and in vivo. Six biomarkers—ABCB1, Acyl-CoA Dehydrogenase Short/Branched Chain (ACADSB), PLA2G6, AKR1C3, PANK1, and Lactate Dehydrogenase B (LDHB)—were identified. The model showed strong discrimination (AUC 0.976 in TCGA; 0.902 in GSE126964; and 0.916 in GSE36895). The genes correlated with immune checkpoints, cytokine signaling, T-cell infiltration, and clinical parameters. Drug analyses suggested cisplatin and sunitinib downregulated oncogenic targets. Silencing ABCB1 or AKR1C3, or overexpressing LDHB, suppressed KIRC cell proliferation and migration in vitro; LDHB overexpression combined with sorafenib significantly reduced tumor growth in vivo. We propose a robust immunometabolism-based diagnostic model and six experimentally supported biomarkers for KIRC, providing mechanistic insight into tumor–immune interactions and potential avenues for personalized therapy.