Abstract
BACKGROUND: Hepatocellular carcinoma (HCC), a common and lethal form of liver cancer, includes mitochondrial dysfunction in its pathogenesis. OBJECTIVES: This study investigated the relationship between mitochondrial function-related genes and HCC progression. METHODS: We systematically retrieved mitochondrial genetic data from the MitoCarta database and identified differentially expressed genes through Gene Expression Omnibus analysis. Weighted gene co-expression network analysis was subsequently employed to construct co-expression networks and identify key modules associated with HCC progression. We evaluated 113 machine learning algorithms to develop mitochondrial gene-based prognostic models. Gene set enrichment analysis further delineated the pathways and biological processes enriched in the module hub genes, offering mechanistic insights into HCC. Immune infiltration analysis using CIBERSORT highlighted the pivotal roles of M1 and M2 macrophages in HCC. Finally, therapeutic candidates targeting critical genes were explored using computational drug prediction, molecular docking, and molecular dynamic simulations, providing novel strategies for HCC-targeted therapy. RESULTS: Stepwise Logistic Regression with Gradient Boosting Machine was chosen as the optimal model (area under the curve [AUC] = 0.977). Moreover, 15 potential HCC biomarkers were identified, including PSMD4 (AUC = 0.888), TBCE (AUC = 0.879), and CKS1B (AUC = 0.860). Additionally, fluoxetine and paroxetine were predicted as potential HCC drugs and validated through molecular docking and dynamic simulations. CONCLUSIONS: This study highlights the prognostic significance of mitochondrial function-related genes in HCC and establishes a framework for developing innovative diagnostic and therapeutic interventions. Future research should prioritize clinical validation of these findings and evaluate the translational potential of the identified drug candidates in HCC.