Abstract

R-loops are dynamic nucleic acid structures implicated in genome regulation and instability, yet their contributions to the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC) remain unclear. Here, we integrated publicly available ccRCC transcriptomic and clinical datasets to quantify R-loop-associated activity using single-sample gene set enrichment analysis (ssGSEA) and develop a prognostic model using linear machine-learning algorithms. R-loop activity was elevated in advanced disease and associated with unfavorable outcomes. Among R-loop-related genes, ACACA was prioritized for its dominant contribution across multiple linear prognostic models, alongside its strong association with metabolic reprogramming. Single-cell transcriptomics showed relatively high ACACA expression in malignant cells that functioned as communication hubs with lipid metabolism-related signaling, and spatial transcriptomics confirmed preferential enrichment of ACACA within malignant regions. In vitro and xenograft experiments further demonstrated that ACACA promoted proliferation and migration and suppressed apoptosis, accompanied by reduced R-loop accumulation, enhanced fatty acid metabolism and improved mitochondrial function. Together, these findings identify ACACA as an R-loop-associated metabolic driver connecting genomic stress with lipid reprogramming and TME remodeling in ccRCC, supporting its potential as a prognostic biomarker and therapeutic target.