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

机译:基于Geo数据库的胶质母细胞瘤细胞和正常人脑细胞差异基因表达分析

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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 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 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.
机译:研究了胶质细胞瘤(GBM)细胞和正常人脑细胞之间的差异表达基因对差异表达基因进行途径分析和蛋白质相互作用网络分析。 GSE12657和GSE42656基因芯片,其含有GBM的基因表达谱系是从国家生物技术信息中心(NCBI)的基因表达式Omniub(Geo)数据库获得。 'R'软件中的“Limma”数据包用于分析两种基因芯片中的差异表达基因,并且使用“罗伯士格古怪”包装进行基因集成。最后,Pheatmap软件用于热爱图分析和Cytoscape,David,String和Kobas用于蛋白质 - 蛋白质相互作用,基因本体(Go)和Kegg分析。结果:I)I)在GSE12657中鉴定出702差异表达基因,其中,在这些基因中,54​​8%显着上调,并且在GSE42656中明显下调了154个,在GSE42656中鉴定了1,064个差异表达基因,显着上调1,068个和786显着下调(P 1)。在基因整合之后,鉴定了总共167个差异表达基因,包括100个上调基因和67个下调基因,与正常人脑细胞相比,基因显示GBM的显着不同的表达水平(P <0.05)。 II)使用串和表达网络确定101个差异表达基因的蛋白质产物之间的相互作用。通过Cytoscape软件识别称为Calm3的关键基因。 III)致富集分析表明,差异表达的基因主要富集为“神经递质:钠交响者活性”和“神经递质转运蛋白活性”,这可能影响神经递质运输的活性。 Kegg途径分析表明,差异表达的基因主要富集为“内质网的蛋白质处理”,这可能影响内质网中的蛋白质加工。结果表明:I)在整合后,从两种基因芯片鉴定了167个差异表达的基因;和II)建立蛋白质相互作用网络,并成功进行了GO和KEGG途径分析以识别和注释关键基因,这为基因水平的GBN研究提供了新的见解。

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