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基于基因表达综合数据库(GEO数据库)的胶质母细胞瘤细胞与正常人类脑细胞的差异基因表达分析

Differential gene expression analysis in glioblastoma cells and normal human brain cells based on GEO database.

作者信息

Wang Anping, Zhang Guibin

机构信息

Department of Neurology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei 441000, P.R. China.

出版信息

Oncol Lett. 2017 Nov;14(5):6040-6044. doi: 10.3892/ol.2017.6922. Epub 2017 Sep 12.

Abstract

The differentially expressed genes between glioblastoma (GBM) cells and normal human brain cells were investigated to performed pathway analysis and protein interaction network analysis for the differentially expressed genes. GSE12657 and GSE42656 gene chips, which contain gene expression profile of GBM were obtained from Gene Expression Omniub (GEO) database of National Center for Biotechnology Information (NCBI). The 'limma' data packet in 'R' software was used to analyze the differentially expressed genes in the two gene chips, and gene integration was performed using 'RobustRankAggreg' package. Finally, pheatmap software was used for heatmap analysis and Cytoscape, DAVID, STRING and KOBAS were used for protein-protein interaction, Gene Ontology (GO) and KEGG analyses. As results: i) 702 differentially expressed genes were identified in GSE12657, among those genes, 548 were significantly upregulated and 154 were significantly downregulated (p<0.01, fold-change >1), and 1,854 differentially expressed genes were identified in GSE42656, among the genes, 1,068 were significantly upregulated and 786 were significantly downregulated (p<0.01, fold-change >1). A total of 167 differentially expressed genes including 100 upregulated genes and 67 downregulated genes were identified after gene integration, and the genes showed significantly different expression levels in GBM compared with normal human brain cells (p<0.05). ii) Interactions between the protein products of 101 differentially expressed genes were identified using STRING and expression network was established. A key gene, called CALM3, was identified by Cytoscape software. iii) GO enrichment analysis showed that differentially expressed genes were mainly enriched in 'neurotransmitter:sodium symporter activity' and 'neurotransmitter transporter activity', which can affect the activity of neurotransmitter transportation. KEGG pathway analysis showed that the differentially expressed genes were mainly enriched in 'protein processing in endoplasmic reticulum', which can affect protein processing in endoplasmic reticulum. The results showed that: i) 167 differentially expressed genes were identified from two gene chips after integration; and ii) protein interaction network was established, and GO and KEGG pathway analyses were successfully performed to identify and annotate the key gene, which provide new insights for the studies on GBN at gene level.

摘要

研究了胶质母细胞瘤(GBM)细胞与正常人类脑细胞之间的差异表达基因,对差异表达基因进行通路分析和蛋白质相互作用网络分析。从美国国立生物技术信息中心(NCBI)的基因表达综合数据库(GEO)中获取了包含GBM基因表达谱的GSE12657和GSE42656基因芯片。使用“R”软件中的“limma”数据包分析两个基因芯片中的差异表达基因,并使用“RobustRankAggreg”包进行基因整合。最后,使用pheatmap软件进行热图分析,使用Cytoscape、DAVID、STRING和KEGG进行蛋白质-蛋白质相互作用、基因本体论(GO)和KEGG分析。结果如下:i)在GSE12657中鉴定出702个差异表达基因,其中548个显著上调,154个显著下调(p<0.01,变化倍数>1);在GSE42656中鉴定出1854个差异表达基因,其中1068个显著上调,786个显著下调(p<0.01,变化倍数>1)。基因整合后共鉴定出167个差异表达基因,包括100个上调基因和67个下调基因,与正常人类脑细胞相比,这些基因在GBM中的表达水平存在显著差异(p<0.05)。ii)使用STRING鉴定出101个差异表达基因的蛋白质产物之间的相互作用,并建立了表达网络。通过Cytoscape软件鉴定出一个关键基因CALM3。iii)GO富集分析表明,差异表达基因主要富集在“神经递质:钠同向转运体活性”和“神经递质转运体活性”,这可能影响神经递质的运输活性。KEGG通路分析表明,差异表达基因主要富集在“内质网中的蛋白质加工”,这可能影响内质网中的蛋白质加工。结果表明:i)整合后从两个基因芯片中鉴定出167个差异表达基因;ii)建立了蛋白质相互作用网络,并成功进行了GO和KEGG通路分析以鉴定和注释关键基因,这为GBN的基因水平研究提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04fc/5661398/26c87289733f/ol-14-05-6040-g00.jpg

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