Tao Yaling, Zhu Junqi, Yu Xiaoling, Cong Huaiwei, Li Jinpeng, Cai Ting, Chen Qian
Ningbo No 2 Hospital, Ningbo, China.
Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.
Front Genet. 2023 Oct 6;14:1208651. doi: 10.3389/fgene.2023.1208651. eCollection 2023.
Understanding the key factors in the tumor microenvironment (TME) that affect the prognosis of gliomas is crucial. In this study, we sought to uncover the prognostic significance of immune cells and immune-related genes in the TME of gliomas. We incorporated data of 970 glioma patient samples from the Chinese Glioma Genome Atlas (CGGA) database as the training set, and an additional set of 666 samples from The Cancer Genome Atlas (TCGA) database served as the validation set. From our analysis, we identified 21 immune-related differentially expressed genes (DEGs) in the TME, which holds implications for glioma prognosis. Based on these genes, we constructed a prognostic risk model on the 21 genes. The prognostic risk model demonstrated robust performance with an area under the curve (AUC) value of 0.848. Notably, the risk score derived from the model emerged as an independent prognostic factor of gliomas, with high risk scores indicative of an unfavorable prognosis. Furthermore, we observed that high infiltration levels of certain immune cells, namely, activated dendritic cells, M0 macrophages, M2 macrophages, and regulatory T cells (Tregs), correlated with an unfavorable glioma prognosis. In conclusion, our findings suggested that the TME of gliomas harbored a distinct immune-associated signature, comprising 21 immune-related genes and specific immune cells. These elements significantly influence the prognosis and present potential as novel indicators in the clinical assessment of glioma patient outcomes.
了解肿瘤微环境(TME)中影响胶质瘤预后的关键因素至关重要。在本研究中,我们试图揭示胶质瘤TME中免疫细胞和免疫相关基因的预后意义。我们纳入了来自中国胶质瘤基因组图谱(CGGA)数据库的970例胶质瘤患者样本数据作为训练集,另外来自癌症基因组图谱(TCGA)数据库的666例样本作为验证集。通过我们的分析,我们在TME中鉴定出21个免疫相关差异表达基因(DEG),这对胶质瘤预后具有重要意义。基于这些基因,我们构建了一个基于这21个基因的预后风险模型。该预后风险模型表现出强大的性能,曲线下面积(AUC)值为0.848。值得注意的是,该模型得出的风险评分成为胶质瘤的独立预后因素,高风险评分表明预后不良。此外,我们观察到某些免疫细胞,即活化树突状细胞、M0巨噬细胞、M2巨噬细胞和调节性T细胞(Tregs)的高浸润水平与不良的胶质瘤预后相关。总之,我们的研究结果表明,胶质瘤的TME具有独特的免疫相关特征,包括21个免疫相关基因和特定的免疫细胞。这些因素显著影响预后,并在胶质瘤患者临床结局评估中作为新指标具有潜力。