Mining TCGA database for genes of prognostic value in glioblastoma microenvironment 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. 从流程图上来看就几步: 如果需要入门TCGA数据库肿瘤微环境分析,可以学习生信自学网给大家录制的: 《TCGA数据库肿瘤微环境挖掘》 需要下载文献原文,可以关注微信公众号:biowolf_cn,回复“肿瘤微环境” 肿瘤微环境,肿瘤中免疫细胞和基质细胞的比例对预后有显着贡献,在肿瘤微环境中,免疫细胞和基质细胞是两种主要类型的非肿瘤组分,并且已被提出对于肿瘤的诊断和预后评估是有价值的;基于ESTIMATE算法计算的免疫评分和基质评分可以促进肿瘤中免疫和基质成分的定量;在该算法中,通过分析免疫和基质细胞的特定基因表达特征来计算免疫和基质评分以预测非肿瘤细胞的浸润;为了更好地了解免疫和基质细胞相关基因对预后的影响,可以系统的分析了肿瘤表达谱挖掘预后不良的肿瘤微环境相关基因,来挖掘其中潜在的调控机制。 1、下载TCGA数据 2、计算样本免疫微环境评分 3、免疫微环境评分与各个临床特征的关系 4、免疫微环境评分高低与预后的关系 5、免疫微环境评分高低样本的基因表达差异 6、这些差异基因与预后的关系,筛选预后显著相关的差异基因 7、外部数据集验证这些基因 看看这篇5分文献的图表: 免疫评分和基质评分与GBM亚型及其总生存率相关。 GBM中基因表达谱与免疫评分和基质评分的比较。 个体DEGS在TCGA总生存率中表达的相关性。 IL6、TIMP1和TLR2模块的前3个PPI网络。 DEGS的Go-Term和Kegg通路分析与总生存率显著相关。 在TCGA和CGGA中鉴定的对GBM总生存率有显著影响的基因。 从TCGA数据库中提取的DEGS与CGGA队列总生存率的相关性验证 如果需要入门TCGA数据库肿瘤微环境分析,可以学习生信自学网给大家录制的: 《TCGA数据库肿瘤微环境挖掘》 需要下载文献原文,可以关注微信公众号:biowolf_cn,回复“肿瘤微环境” 责任编辑:伏泽 作者申明:本文版权属于生信自学网(微信号:18520221056)未经授权,一律禁止转载! |