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Tumor Microenvironment Characterization in Glioblastoma Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures.
Journal of Molecular Neuroscience ( IF 2.8 ) Pub Date : 2020-01-31 , DOI: 10.1007/s12031-020-01484-0
Jinsen Zhang 1 , Xing Xiao 1 , Xin Zhang 1 , Wei Hua 1
Affiliation  

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.

中文翻译:

胶质母细胞瘤中的肿瘤微环境特征鉴定了预后和免疫治疗相关的基因签名。

肿瘤微环境(TME)细胞是肿瘤组织中的重要元素。越来越多的证据表明它们在预测肿瘤的临床结果和治疗效果方面具有重要的临床病理意义。但是,尚未报道胶质母细胞瘤(GBM)中TME细胞相互作用的系统分析。我们系统地分析了GBM的转录测序数据,以找到免疫基因标记来预测GBM的临床结果。首先,我们从The Cancer Genome Atlas(TCGA)和Gene Expression Omnibus(GEO)下载了GBM的表达谱和临床随访信息。CIBERSORT用于评估757例患者的TME浸润模式,将TME表型与GBM的基因组特征和临床病理特征进行系统关联,定义了四种TME表型,TMEScore是使用随机森林和主成分分析等算法构建的。TMEScore与发病年龄之间存在显着相关性。高TMEScore样品的特征在于免疫激活,TGF途径激活和免疫检查点基因的高表达,而低TMEScore样品的特征在于高频IDH1和MET突变。因此,描绘GBM的TME特征的全面情况可能有助于解释GBM对免疫疗法的反应,并为癌症治疗提供新的策略。在这项研究中,TMEScore可以用作预测GBM患者生存的新的预后指标,并可以作为免疫检查点抑制剂反应的潜在预测指标。TMEScore与发病年龄之间存在显着相关性。高TMEScore样品的特征在于免疫激活,TGF途径激活和免疫检查点基因的高表达,而低TMEScore样品的特征在于高频IDH1和MET突变。因此,描绘GBM的TME特征的全面情况可能有助于解释GBM对免疫疗法的反应,并为癌症治疗提供新的策略。在这项研究中,TMEScore可以用作预测GBM患者生存的新预后标志物,并且可以作为免疫检查点抑制剂反应的潜在预测指标。TMEScore与发病年龄之间存在显着相关性。高TMEScore样品的特征在于免疫激活,TGF途径激活和免疫检查点基因的高表达,而低TMEScore样品的特征在于高频IDH1和MET突变。因此,描绘GBM的TME特征的全面情况可能有助于解释GBM对免疫疗法的反应,并为癌症治疗提供新的策略。在这项研究中,TMEScore可以用作预测GBM患者生存的新预后标志物,并且可以作为免疫检查点抑制剂反应的潜在预测指标。低TMEScore样本的特征是高频IDH1和MET突变。因此,描绘GBM的TME特征的全面情况可能有助于解释GBM对免疫疗法的反应,并为癌症治疗提供新的策略。在这项研究中,TMEScore可以用作预测GBM患者生存的新预后标志物,并且可以作为免疫检查点抑制剂反应的潜在预测指标。低TMEScore样本的特征是高频IDH1和MET突变。因此,描绘GBM的TME特征的全面情况可能有助于解释GBM对免疫疗法的反应,并为癌症治疗提供新的策略。在这项研究中,TMEScore可以用作预测GBM患者生存的新预后标志物,并且可以作为免疫检查点抑制剂反应的潜在预测指标。
更新日期:2020-01-31
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