Abstract The rising incidence of clear cell renal cell carcinoma (ccRCC) with current treatments offering limited survival benefits and a poor prognosis. Mitochondrial abnormalities impact tumor immunity, progression, and metastasis, and the role of mitochondrial energy metabolism-related genes (MMRGs) in ccRCC remains largely unexplored. This study analyzed TCGA-KIRC, GSE159115, and GSE29609 datasets to identify differentially expressed (DE) MMRGs and their functions. It used LASSO and Cox models to select prognostic MMRGs for model building, created a nomogram (based on independent factors) in TCGA-KIRC (evaluated via calibration and ROC curves), and conducted GSEA, immune cell correlation analyses, TF-miRNA-mRNA network studies, qRT-PCR (ccRCC vs. controls), and WB (RIPA) for biomarker validation. A study of 103 DE-MMRGs highlighted their link to fatty acid metabolism and peroxisome proliferator-activated receptor (PPAR) signaling. Machine learning assessed the prognostic potential of these DE-MMRGs, which yielded a risk model based on six key biomarkers. The constructed prognostic model exhibited outstanding performance in both training and validation sets. This study also explored immune cell relevance and regulatory networks and elucidated complex mitochondrial-tumor interactions. The validation of predictive biomarker expression in clinical samples underscored their role in refining prognostic assessment and therapeutic strategies for ccRCC. In this study, six mitochondrial energy metabolism-related prognosis biomarkers ( COX7B, PPARGC1B, NDUFA11, PFKFB4, NDUFV2, and NDUFA7 ) were screened. A risk model was developed to provide a new reference for the prognosis of ccRCC patients.
Peng et al. (2025) studied this question.