Lin Yu, Li Huining, Ge Qiang, Hua Dan
Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.
Transl Cancer Res. 2025 Jan 31;14(1):240-253. doi: 10.21037/tcr-24-1035. Epub 2025 Jan 20.
Glioma is a common brain tumour that is associated with poor prognosis. Immunotherapy has shown significant potential in the treatment of gliomas. Herein, we proposed a new prognostic risk model based on immune- and mitochondrial energy metabolism-related differentially expressed genes (IR&MEMRDEGs) to enhance the accuracy of prognostic assessment in patients with glioma.
Data from samples from 671 glioma patients and 5 normal controls with available follow-up data and prognostic outcomes were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. All data were downloaded on 13 November 2023. IR&MEMRDEGs were screened from the GeneCards website and published literature. Prognostic prediction models were constructed and analysed using Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression, Kaplan-Meier (KM) curve, and receiver operating characteristic (ROC) curve analyses. Single-sample gene set enrichment analysis (ssGSEA) was further performed to ascertain the percentage of immune cell infiltration in the glioma specimens.
Bioinformatics analysis of the GEO and TCGA databases identified eleven MEMRDEGs with dysregulated expression in gliomas: and . Further analysis identified , and as separate predictive factors for glioma, among which and exhibited superior accuracy [area under the ROC curve (AUC) >0.9], while , and demonstrated slightly lower accuracy (0.7< AUC <0.9), and displayed poor accuracy (0.5< AUC <0.7). Among these genes, the levels of , and were significantly higher in the high-risk group (HRG) compared with the low-risk group (LRG) (P<0.001), indicating a negative association with patient prognosis. In contrast, and were significantly downregulated in the HRG compared to the LRG (P<0.05), indicating a potential correlation with patient outcomes. Subsequently, prognostic models were constructed based on IR&MEMRDEG and MEMRDEGs to anticipate the outcomes of glioma patients, while the predictive efficacy of the model was validated via KM and ROC curve analysis. The results revealed that , and had superior accuracy in predicting glioma prognosis. The ssGSEA results showed that only was negatively linked to the amount of immune cell infiltration in the LRG, while displaying a positive connection in the HRG (r value>0), indicating that the expression levels of may have a distinct influence on the tumour immune microenvironment.
The present study confirmed the significant predictive value of for glioma prognosis, which may guide immunotherapy for glioma treatment.
胶质瘤是一种常见的脑肿瘤，预后较差。免疫疗法在胶质瘤治疗中已显示出巨大潜力。在此，我们提出了一种基于免疫和线粒体能量代谢相关差异表达基因（IR&MEMRDEGs）的新预后风险模型，以提高胶质瘤患者预后评估的准确性。
从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库下载了671例胶质瘤患者和5例有可用随访数据及预后结果的正常对照样本的数据。所有数据于2023年11月13日下载。从GeneCards网站和已发表文献中筛选IR&MEMRDEGs。使用Cox和最小绝对收缩与选择算子（LASSO）回归、Kaplan-Meier（KM）曲线和受试者工作特征（ROC）曲线分析构建并分析预后预测模型。进一步进行单样本基因集富集分析（ssGSEA）以确定胶质瘤标本中免疫细胞浸润的百分比。
对GEO和TCGA数据库的生物信息学分析确定了11个在胶质瘤中表达失调的MEMRDEGs： 和 。进一步分析确定 、 和 为胶质瘤的独立预测因素，其中 和 表现出更高的准确性[ROC曲线下面积（AUC）>0.9]，而 、 和 准确性略低（0.7 < AUC < 0.9）， 准确性较差（0.5 < AUC < 0.7）。在这些基因中，与低风险组（LRG）相比，高风险组（HRG）中 、 和 的水平显著更高（P < 0.001），表明与患者预后呈负相关。相比之下，与LRG相比，HRG中 和 显著下调（P < 0.05），表明与患者预后可能存在相关性。随后，基于IR&MEMRDEG和MEMRDEGs构建预后模型以预测胶质瘤患者的预后，同时通过KM和ROC曲线分析验证模型的预测效能。结果显示 、 和 在预测胶质瘤预后方面具有更高的准确性。ssGSEA结果表明，只有 与LRG中免疫细胞浸润量呈负相关，而在HRG中呈正相关（r值>0），表明 的表达水平可能对肿瘤免疫微环境有不同影响。
本研究证实了 对胶质瘤预后具有显著的预测价值，这可能为胶质瘤治疗的免疫疗法提供指导。
