Yu Caiyuan, Xun Mingjuan, Yu Fei, Li Hengyu, Liu Ying, Zhang Wei, Yan Jun
School of Pharmacy, Faculty of Medicine & State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau SAR 999078, China.
Laboratory of Brain Disorders, Beijing Institute of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing 100069, China.
Int J Mol Sci. 2025 May 12;26(10):4609. doi: 10.3390/ijms26104609.
Glioma is the most common primary malignant intracranial tumor with limited treatment options and a dismal prognosis. This study aimed to develop a robust gene expression-based prognostic signature for GBM using the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets. Using WGCNA and LASSO algorithms, we identified four MHC-related genes (TNFSF14, MXRA5, FCGR2B, and TNFRSF9) as prognostic biomarkers for glioma. A risk model based on these genes effectively stratified patients into high- and low-risk groups with distinct survival outcomes across TCGA and CGGA cohorts. This signature correlated with immune pathways and glioma progression mechanisms, showing strong associations with immune function and tumor microenvironment infiltration patterns. The risk score reflected tumor microenvironment remodeling, suggesting its prognostic relevance. We further propose I-BET-762 and Enzastaurin as potential therapeutic candidates for glioma. In conclusion, the four-gene signature we identified and the corresponding risk score model constructed from it provide valuable tools for the prognosis prediction of glioblastoma multiforme (GBM) and may guide personalized treatment strategies. The least absolute shrinkage and selection operator (LASSO) risk score has demonstrated significant prognostic evaluation utility in clinical GBM patients, bringing potential implications for patient stratification and the optimization of treatment regimens.
胶质瘤是最常见的原发性恶性颅内肿瘤，治疗选择有限，预后不佳。本研究旨在利用癌症基因组图谱（TCGA）和中国胶质瘤基因组图谱（CGGA）数据集，开发一种基于基因表达的强大的胶质母细胞瘤预后特征。使用加权基因共表达网络分析（WGCNA）和套索（LASSO）算法，我们确定了四个与主要组织相容性复合体（MHC）相关的基因（肿瘤坏死因子配体超家族成员14（TNFSF14）、多配体聚糖5（MXRA5）、Fc段γ受体ⅡB（FCGR2B）和肿瘤坏死因子受体超家族成员9（TNFRSF9））作为胶质瘤的预后生物标志物。基于这些基因的风险模型有效地将患者分为高风险组和低风险组，在TCGA和CGGA队列中具有不同的生存结果。该特征与免疫途径和胶质瘤进展机制相关，显示出与免疫功能和肿瘤微环境浸润模式的强烈关联。风险评分反映了肿瘤微环境重塑，表明其预后相关性。我们进一步提出I-BET-762和恩扎妥林作为胶质瘤的潜在治疗候选药物。总之，我们鉴定的四基因特征及其构建的相应风险评分模型为多形性胶质母细胞瘤（GBM）的预后预测提供了有价值的工具，并可能指导个性化治疗策略。最小绝对收缩和选择算子（LASSO）风险评分在临床GBM患者中已显示出显著的预后评估效用，对患者分层和治疗方案优化具有潜在意义。
