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通过对微阵列数据的生物信息学分析揭示胶质瘤和神经胶质瘤发病机制的分子机制。

Molecular mechanisms underlying gliomas and glioblastoma pathogenesis revealed by bioinformatics analysis of microarray data.

机构信息

Department of Pharmaceutics, SET'S College of Pharmacy, Dharwad, Karnataka, 580002, India.

Department of Computer Science, Karnatak University, Dharwad, Karnataka, India.

出版信息

Med Oncol. 2017 Sep 26;34(11):182. doi: 10.1007/s12032-017-1043-x.


DOI:10.1007/s12032-017-1043-x
PMID:28952134
Abstract

The aim of this study was to identify key genes associated with gliomas and glioblastoma and to explore the related signaling pathways. Gene expression profiles of three glioma stem cell line samples, three normal astrocyte samples, three astrocyte overexpressing 4 iPSC-inducing and oncogenic factors (myc(T58A), OCT-4, p53DD, and H-Ras(G12V)) samples, three astrocyte overexpressing 7 iPSC-inducing and oncogenic factors (OCT4, H-Ras(G12V), myc(T58A), p53DD, cyclin D1, CDK4(RC24) and hTERT) samples and three glioblastoma cell line samples were downloaded from the ArrayExpress database (accession: E-MTAB-4771). The differentially expressed genes (DEGs) in gliomas and glioblastoma were identified using FDR and t tests, and protein-protein interaction (PPI) networks for these DEGs were constructed using the protein interaction network analysis. The GeneTrail2 1.5 tool was used to identify potentially enriched biological processes among the DEGs using gene ontology (GO) terms and to identify the related pathways using the Kyoto Encyclopedia of Genes and Genomes, Reactome and WikiPathways pathway database. In addition, crucial modules of the constructed PPI networks were identified using the PEWCC1 plug-in, and their topological properties were analyzed using NetworkAnalyzer, both available from Cytoscape. We also constructed microRNA-target gene regulatory network and transcription factor-target gene regulatory network for these DEGs were constructed using the miRNet and binding and expression target analysis. We identified 200 genes that could potentially be involved in the gliomas and glioblastoma. Among them, bioinformatics analysis identified 137 up-regulated and 63 down-regulated DEGs in gliomas and glioblastoma. The significant enriched pathway (PI3K-Akt) for up-regulated genes such as COL4A1, COL4A2, EGFR, FGFR1, LAPR6, MYC, PDGFA, SPP1 were selected as well as significant GO term (ear development) for up-regulated genes such as CELSR1, CHRNA9, DDR1, FGFR1, GLI2, LGR5, SOX2, TSHR were selected, while the significant enriched pathway (amebiasis) for down-regulated gene such as COL3A1, COL5A2, LAMA2 were selected as well as significant GO term (RNA polymerase II core promoter proximal region sequence-specific binding (5) such as MEIS2, MEOX2, NR2E1, PITX2, TFAP2B, ZFPM2 were selected. Importantly, MYC and SOX2 were hub proteins in the up-regulated PPI network, while MET and CDKN2A were hub proteins in the down-regulated PPI network. After network module analysis, MYC, FGFR1 and HOXA10 were selected as the up-regulated coexpressed genes in the gliomas and glioblastoma, while SH3GL3 and SNRPN were selected as the down-regulated coexpressed genes in the gliomas and glioblastoma. MicroRNA hsa-mir-22-3p had a regulatory effect on the most up DEGs, including VSNL1, while hsa-mir-103a-3p had a regulatory effect on the most down DEGs, including DAPK1. Transcription factor EZH2 had a regulatory effect on the both up and down DEGs, including CD9, CHI3L1, MEIS2 and NR2E1. The DEGs, such as MYC, FGFR1, CDKN2A, HOXA10 and MET, may be used for targeted diagnosis and treatment of gliomas and glioblastoma.

摘要

本研究旨在鉴定与神经胶质瘤和胶质母细胞瘤相关的关键基因,并探讨相关的信号通路。从 ArrayExpress 数据库(访问号:E-MTAB-4771)下载了三个神经胶质瘤干细胞系样本、三个正常星形胶质细胞样本、三个过度表达 4 个 iPSC 诱导和致癌因子(myc(T58A)、OCT-4、p53DD 和 H-Ras(G12V))的星形胶质细胞样本、三个过度表达 7 个 iPSC 诱导和致癌因子(OCT4、H-Ras(G12V)、myc(T58A)、p53DD、cyclin D1、CDK4(RC24)和 hTERT)的星形胶质细胞样本和三个胶质母细胞瘤细胞系样本的基因表达谱。使用 FDR 和 t 检验鉴定神经胶质瘤和胶质母细胞瘤中的差异表达基因(DEGs),并使用蛋白质相互作用网络分析构建这些 DEGs 的蛋白质-蛋白质相互作用(PPI)网络。使用 GeneTrail2 1.5 工具,使用基因本体(GO)术语识别 DEGs 中可能富集的生物过程,使用京都基因与基因组百科全书、Reactome 和 WikiPathways 途径数据库识别相关途径。此外,使用 PEWCC1 插件识别构建的 PPI 网络中的关键模块,并使用 Cytoscape 中的 NetworkAnalyzer 分析其拓扑性质。我们还构建了这些 DEGs 的 microRNA-靶基因调控网络和转录因子-靶基因调控网络。我们确定了 200 个可能参与神经胶质瘤和胶质母细胞瘤的基因。其中,生物信息学分析鉴定了神经胶质瘤和胶质母细胞瘤中 137 个上调和 63 个下调的 DEGs。上调基因(如 COL4A1、COL4A2、EGFR、FGFR1、LAPR6、MYC、PDGFA、SPP1)的显著富集途径(PI3K-Akt)以及上调基因(如 CELSR1、CHRNA9、DDR1、FGFR1、GLI2、LGR5、SOX2、TSHR)的显著 GO 术语(耳发育)均被选择,下调基因(如 COL3A1、COL5A2、LAMA2)的显著富集途径(阿米巴病)以及下调基因(如 MEIS2、MEOX2、NR2E1、PITX2、TFAP2B、ZFPM2)的显著 GO 术语(RNA 聚合酶 II 核心启动子近端区域序列特异性结合(5))也均被选择。重要的是,MYC 和 SOX2 是上调 PPI 网络中的枢纽蛋白,而 MET 和 CDKN2A 是下调 PPI 网络中的枢纽蛋白。经过网络模块分析,选择 MYC、FGFR1 和 HOXA10 作为神经胶质瘤和胶质母细胞瘤中上调的共表达基因,而选择 SH3GL3 和 SNRPN 作为神经胶质瘤和胶质母细胞瘤中下调的共表达基因。microRNA hsa-mir-22-3p 对大多数上调的 DEGs 具有调控作用,包括 VSNL1,而 hsa-mir-103a-3p 对大多数下调的 DEGs 具有调控作用,包括 DAPK1。转录因子 EZH2 对上调和下调的 DEGs 都具有调控作用,包括 CD9、CHI3L1、MEIS2 和 NR2E1。DEGs,如 MYC、FGFR1、CDKN2A、HOXA10 和 MET,可用于神经胶质瘤和胶质母细胞瘤的靶向诊断和治疗。

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