Abstract
BACKGROUND: This study presents a new prognostic model using mitophagy-related genes (MRGs) in glioma, a type of brain tumor, developed through bioinformatics. The model seeks to improve the understanding of glioma prognosis by focusing on mitophagy, a cellular process that eliminates damaged mitochondria and influences tumor behavior and patient outcomes. METHODS: The expression profile and clinical information of patients were downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus). By analyzing the correlation between the 14 MRGs and glioma prognosis, we established a novel prognostic model in the TCGA training cohort and validated it in the GSE16011 dataset. RESULTS: Using univariate Cox regression, we identified 26 MRGs that were significantly enriched in various mitophagy-related pathways. After filtering variables using least absolute shrinkage and selection operator (Lasso) regression analysis, 14 MRGs were introduced to construct the predictive model. The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01). The risk score was found to be an independent prognostic factor for glioma in both univariate and multivariate Cox regression analyses. Interestingly, Geneset enrichment analysis (GSEA) analysis revealed that multiple signaling pathways related to neurotransmission were significantly enriched in the high-risk group. Additionally, a hub miRNA-mRNA network was established, which disclosed the quantity and classification of miRNAs capable of interacting with 14 MRGs. Finally, our analysis revealed a notable association between 14 MRGs and immune functionality in gliomas. CONCLUSION: We developed a robust and accurate prognostic model with 14 MRGs. Our findings might provide a reference for the clinical prognosis and management of glioma.