Jia Di, Li Shenglan, Li Dali, Xue Haipeng, Yang Dan, Liu Ying
Department of Biochemistry and Molecular Biology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
The Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Aging (Albany NY). 2018 Apr 16;10(4):592-605. doi: 10.18632/aging.101415.
Glioblastoma (GBM) is one of the most deadly brain tumors. The convenient access to The Cancer Genome Atlas (TCGA) database allows for large-scale global gene expression profiling and database mining for potential correlation between genes and overall survival of a variety of malignancies including GBM. Previous reports have shown that tumor microenvironment cells and the extent of infiltrating immune and stromal cells in tumors contribute significantly to prognosis. Immune scores and stromal scores calculated based on the ESTIMATE algorithm could facilitate the quantification of the immune and stromal components in a tumor. To better understand the effects of genes involved in immune and stromal cells on prognosis, we categorized GBM cases in the TCGA database according to their immune/stromal scores into high and low score groups, and identified differentially expressed genes whose expression was significantly associated with prognosis in GBM patients. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. Finally, we validated these genes in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Thus, we obtained a list of tumor microenvironment-related genes that predict poor outcomes in GBM patients.
胶质母细胞瘤（GBM）是最致命的脑肿瘤之一。通过便捷访问癌症基因组图谱（TCGA）数据库，可以进行大规模的全球基因表达谱分析，并挖掘数据库以寻找包括GBM在内的各种恶性肿瘤中基因与总生存期之间的潜在相关性。先前的报告表明，肿瘤微环境细胞以及肿瘤中浸润的免疫细胞和基质细胞的程度对预后有显著影响。基于ESTIMATE算法计算的免疫评分和基质评分有助于量化肿瘤中的免疫和基质成分。为了更好地了解免疫细胞和基质细胞中相关基因对预后的影响，我们根据TCGA数据库中GBM病例的免疫/基质评分将其分为高分和低分两组，并鉴定出其表达与GBM患者预后显著相关的差异表达基因。功能富集分析和蛋白质-蛋白质相互作用网络进一步表明，这些基因主要参与免疫反应、细胞外基质和细胞粘附。最后，我们在中国胶质瘤基因组图谱（CGGA）的一个独立GBM队列中验证了这些基因。因此，我们获得了一份预测GBM患者预后不良的肿瘤微环境相关基因清单。
