Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis
Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis
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生物信息学分析鉴定胶质母细胞瘤与低级别胶质瘤差异表达关键基因
DOI：
10.7717/peerj.6560
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发表时间：
2019-03-07
期刊：
影响因子：
2.7
通讯作者：
Chen, Qianxue
中科院分区：
文献类型：
--
作者：
Xu, Yang;Geng, Rongxin;Chen, Qianxue
胶质瘤是一种非常多样化的脑肿瘤，是最常见的原发性肿瘤，是中枢神经系统的一种难以治愈的肿瘤。通过了解不同级别胶质瘤的分子基础，区分低级别和高级别肿瘤是必要的，这是确定新的生物标志物和治疗策略的重要一步。我们从基因表达综合数据库（gene expression omnibus， GEO）中选择基因表达谱GSE52009来检测重要的差异基因。GSE52009包含120个样本，包括我们分析中选择的60个WHO II样本和24个WHO IV样本。我们利用GEO2R工具对低级别胶质瘤和高级别胶质瘤之间的差异表达基因（DEGs）进行筛选，然后利用数据库进行标注、可视化和整合发现，进行基因本体分析和京都百科全书的基因和基因组通路分析。此外，我们使用Cytoscape搜索工具检索相互作用基因，并应用分子复合物检测插件实现蛋白质-蛋白质相互作用（PPI）的可视化。我们选择了15个具有较高连通性的枢纽基因，包括组织抑制剂金属蛋白酶-1和血清淀粉样蛋白A1；此外，我们使用含有70个胶质母细胞瘤样本的GSE53733进行基因集富集分析。总之，我们的生物信息学分析表明，DEGs和hub基因可能被定义为胶质母细胞瘤的诊断和指导治疗策略的新生物标志物。
Gliomas are a very diverse group of brain tumors that are most commonly primary tumor and difficult to cure in central nervous system. It's necessary to distinguish low-grade tumors from high-grade tumors by understanding the molecular basis of different grades of glioma, which is an important step in defining new biomarkers and therapeutic strategies. We have chosen the gene expression profile GSE52009 from gene expression omnibus (GEO) database to detect important differential genes. GSE52009 contains 120 samples, including 60 WHO II samples and 24 WHO IV samples that were selected in our analysis. We used the GEO2R tool to pick out differently expressed genes (DEGs) between low-grade glioma and high-grade glioma, and then we used the database for annotation, visualization and integrated discovery to perform gene ontology analysis and Kyoto encyclopedia of gene and genome pathway analysis. Furthermore, we used the Cytoscape search tool for the retrieval of interacting genes with molecular complex detection plug-in applied to achieve the visualization of protein-protein interaction (PPI). We selected 15 hub genes with higher degrees of connectivity, including tissue inhibitors metalloproteinases-1 and serum amyloid A1; additionally, we used GSE53733 containing 70 glioblastoma samples to conduct Gene Set Enrichment Analysis. In conclusion, our bioinformatics analysis showed that DEGs and hub genes may be defined as new biomarkers for diagnosis and for guiding the therapeutic strategies of glioblastoma.