Abstract
Mitochondrial dysfunction has emerged as a critical factor in the progression and prognosis of low-grade glioma (LGG). In this study, we explored the role of mitochondrial-related genes through consensus clustering analysis using multi-omics data from the TCGA, CGGA, and other independent datasets. Patients were categorized into three clusters (Cluster A, B, and C), with Cluster B consistently associated with poorer prognosis. Mutation landscape analysis revealed distinct genetic alterations and copy number variations among clusters, particularly in Cluster B, which exhibited unique genetic signatures. Immune infiltration analysis showed that Cluster B had higher expression levels of immune checkpoint genes, stronger immune evasion activity, and greater immune cell infiltration, suggesting an immunosuppressive tumor microenvironment. Furthermore, we identified mitochondrial-related prognostic markers and developed a MITscore based on gene expression patterns, which stratified patients into high- and low-risk groups. High MITscore groups displayed stronger stemness characteristics, poorer survival outcomes, and differential responses to chemotherapy and immunotherapy. Cross-validation with drug sensitivity and immunotherapy cohorts indicated that high MITscore patients were more sensitive to certain chemotherapeutic agents and responded better to immunotherapy. Finally, using the SRGA method, we identified novel biomarkers (KDR, LRRK2, SQSTM1) closely associated with mitochondrial function, which may serve as potential targets for therapeutic intervention. These findings highlight the critical role of mitochondrial dysfunction in LGG prognosis, tumor microenvironment regulation, and treatment response, providing new avenues for precision oncology.