Department of Neurosurgery, Huashan Hospital, Fudan University, No.12 Wulumuqi Zhong Road, Shanghai, 200040, China.
J Mol Neurosci. 2020 May;70(5):738-750. doi: 10.1007/s12031-020-01484-0. Epub 2020 Jan 31.
Tumor microenvironment (TME) cells are important elements in tumor tissue. There is increasing evidence that they have important clinical pathological significance in predicting tumor clinical outcomes and therapeutic effects. However, no systematic analysis of TME cell interactions in glioblastoma (GBM) has been reported. We systematically analyzed the transcriptional sequencing data of GBM to find an immune gene marker to predict the clinical results of GBM. First, we downloaded the expression profiles and clinical follow-up information of GBM from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). CIBERSORT was used to evaluate the infiltration mode of TME in 757 patients, systematically correlated TME phenotype with genomic characteristics and clinicopathological characteristics of GBM, defined four TME phenotypes, and TMEScore was constructed using algorithms such as random forest and principal component analysis. There is a significant correlation between TMEScore and age of onset. High TMEScore samples are characterized by immune activation, TGF pathway activation, and high expression of immune checkpoint genes, while low TMEScore samples are characterized by high-frequency IDH1 and MET mutations. Therefore, a comprehensive landscape depicting the TME characteristics of GBM may help explain GBM's response to immunotherapy and provide new strategies for cancer treatment. In this study, TMEScore can be used as a new prognostic marker to predict the survival of GBM patients, and as a potential predictor of immune checkpoint inhibitor response.
肿瘤微环境 (TME) 细胞是肿瘤组织中的重要组成部分。越来越多的证据表明，它们在预测肿瘤临床结局和治疗效果方面具有重要的临床病理意义。然而，目前尚未有系统分析胶质母细胞瘤 (GBM) 中 TME 细胞相互作用的报道。我们系统地分析了 GBM 的转录测序数据，以寻找一种免疫基因标志物来预测 GBM 的临床结果。首先，我们从癌症基因组图谱 (TCGA) 和基因表达综合数据库 (GEO) 下载了 GBM 的表达谱和临床随访信息。我们使用 CIBERSORT 评估了 757 例患者的 TME 浸润模式，系统地将 TME 表型与 GBM 的基因组特征和临床病理特征相关联，定义了四种 TME 表型，并使用随机森林和主成分分析等算法构建了 TMEScore。TMEScore 与发病年龄之间存在显著相关性。高 TMEScore 样本的特征是免疫激活、TGF 通路激活和免疫检查点基因高表达，而低 TMEScore 样本的特征是 IDH1 和 MET 突变频率高。因此，全面描绘 GBM 的 TME 特征可能有助于解释 GBM 对免疫治疗的反应，并为癌症治疗提供新的策略。在这项研究中，TMEScore 可用作新的预后标志物来预测 GBM 患者的生存情况，并且可能是免疫检查点抑制剂反应的潜在预测指标。
