Zhao Qiang, Li Guangxin
Center for Precision Medicine and Translational Research, Tianjin Cancer Hospital Airport Hospital, Tianjin, 300000, China.
Department of Pathology, Chongqing University Cancer Hospital, Chongqing, 400042, China.
Discov Oncol. 2025 Feb 9;16(1):140. doi: 10.1007/s12672-025-01916-6.
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.
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.
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.
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.
本研究通过生物信息学方法，提出了一种利用线粒体自噬相关基因（MRGs）建立的脑肿瘤——胶质瘤新预后模型。该模型旨在通过关注线粒体自噬来增进对胶质瘤预后的理解，线粒体自噬是一种消除受损线粒体并影响肿瘤行为和患者预后的细胞过程。
从TCGA（癌症基因组图谱）和GEO（基因表达综合数据库）下载患者的表达谱和临床信息。通过分析14个MRGs与胶质瘤预后之间的相关性，我们在TCGA训练队列中建立了一个新的预后模型，并在GSE16011数据集中进行了验证。
使用单变量Cox回归，我们确定了26个在各种线粒体自噬相关途径中显著富集的MRGs。在使用最小绝对收缩和选择算子（Lasso）回归分析对变量进行筛选后，引入了14个MRGs来构建预测模型。生存分析显示，在训练队列和验证队列中，高风险评分患者的总生存期明显低于低风险评分患者（p < 0.01）。在单变量和多变量Cox回归分析中，风险评分均被发现是胶质瘤的独立预后因素。有趣的是，基因集富集分析（GSEA）显示，与神经传递相关的多个信号通路在高风险组中显著富集。此外，还建立了一个枢纽miRNA-mRNA网络，该网络揭示了能够与14个MRGs相互作用的miRNA的数量和分类。最后，我们的分析揭示了14个MRGs与胶质瘤免疫功能之间的显著关联。
我们开发了一个由14个MRGs组成的强大且准确的预后模型。我们的研究结果可能为胶质瘤的临床预后和管理提供参考。
