Identification of mitophagy-related genes impacting patient survival in glioma

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.

Keywords: Bioinformatics; Glioma; Mitophagy; Prognostic model; Risk score.

Fig. 1

Construction of MRGs-based predictive model. A 1000-fold cross-validation to select variables by the LASSO regression with the optimal λ value. B LASSO coefficients of MRGs. Each curve represents a MRG. C The non-zero regression coefficient of 14 prognostic MRGs

Fig. 2

Assessment of risk model between the low-risk and high-risk groups. A The evaluation of survival differences between low-risk and high-risk groups in the training cohort. B ROC analysis conducted for the risk model in the training cohort, resulting in AUC of 0.865, 0.881, and 0.805 at 1, 3, and 5 years, respectively. C The assessment of survival differences between the low-risk and high-risk groups in the validation cohort. D ROC analysis performed for the risk model in the validation cohort, yielding AUC of 0.761, 0.781, and 0.745 at the corresponding 1-year, 3-year, and 5-year marks

Fig. 3

Construction of the nomogram system. A The nomogram predicting the OS of glioma patients at 1, 5, and 10 years, using risk score and other clinicopathological parameters from the training cohort. B The calibration curves comparing the predicted and observed OS at 1, 5, and 10 years in the training cohort. The dashed line of 45° represents the perfect prediction of the nomogram

Fig. 4

Bioinformatic analysis of the DEGs and signaling pathways between the low-risk and high-risk groups. A PPI network comprised of the 14 MRGs. B Volcano plot that highlights the DEGs between the low-risk and high-risk cohorts sourced from TCGA. Genes indicated in red or blue exhibit significant upregulation or downregulation, respectively. C GO analysis of the DEGs. D KEGG analysis of the DEGs

Fig. 5

GSEA for biological pathways and processes correlated with risk score in the cohort from TCGA. NES, normalized enrichment score; p: Nominal p-value; FDR: false discovery rate

Fig. 6

The key miRNAs associated with 14 MRGs

Fig. 7

The analysis of immune functions associated with 14 MRGs. The assessment of immune cell infiltration related to 14 MRGs, using ssGSEA A and Cibersort B. C The influence of 14MRGs on the expression of immune checkpoint-associated genes