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Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis
Computational and Mathematical Methods in Medicine Pub Date : 2020-10-23 , DOI: 10.1155/2020/9602016 Huijing Zhu 1, 2 , Xin Zhu 2 , Yuhong Liu 2 , Fusong Jiang 3 , Miao Chen 1, 4 , Lin Cheng 2 , Xingbo Cheng 1
Computational and Mathematical Methods in Medicine Pub Date : 2020-10-23 , DOI: 10.1155/2020/9602016 Huijing Zhu 1, 2 , Xin Zhu 2 , Yuhong Liu 2 , Fusong Jiang 3 , Miao Chen 1, 4 , Lin Cheng 2 , Xingbo Cheng 1
Affiliation
Objective. The aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms. Methods. The gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways. Results. A total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. Conclusion. Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.
中文翻译：
生物信息学分析 2 型糖尿病的基因表达谱
客观。本研究的目的是鉴定 2 型糖尿病 (T2DM) 中的候选基因并探索其潜在机制。方法。基因表达谱 GSE26168 是从 Gene Expression Omnibus (GEO) 数据库下载的。在线工具 GEO2R 用于获取差异表达基因 (DEG)。通过使用 Metascape 进行注释、可视化和全面发现，进行基因本体 (GO) 术语富集分析和京都基因和基因组百科全书 (KEGG) 通路分析。利用Cytoscape软件构建DEGs的蛋白质-蛋白质相互作用(PPI)网络，寻找候选基因和关键通路。结果. T2DM共发现981个DEG，其中上调基因301个，下调基因680个。Metascape 的 GO 分析显示，DEG 在细胞分化、细胞粘附、细胞内信号转导和蛋白激酶活性调节中显着富集。KEGG通路分析显示，DEGs主要富集于cAMP信号通路、Rap1信号通路、脂肪细胞脂解调节、PI3K-Akt信号通路、MAPK信号通路等。基于DEGs的PPI网络，确定了以下6个候选基因：PIK3R1、RAC1、GNG3、GNAI1、CDC42和ITGB1。结论。我们的数据提供了基因、功能和途径的全面生物信息学分析，这些分析可能与 T2DM 的发病机制有关。
更新日期：2020-10-30
中文翻译：
生物信息学分析 2 型糖尿病的基因表达谱
客观。本研究的目的是鉴定 2 型糖尿病 (T2DM) 中的候选基因并探索其潜在机制。方法。基因表达谱 GSE26168 是从 Gene Expression Omnibus (GEO) 数据库下载的。在线工具 GEO2R 用于获取差异表达基因 (DEG)。通过使用 Metascape 进行注释、可视化和全面发现，进行基因本体 (GO) 术语富集分析和京都基因和基因组百科全书 (KEGG) 通路分析。利用Cytoscape软件构建DEGs的蛋白质-蛋白质相互作用(PPI)网络，寻找候选基因和关键通路。结果. T2DM共发现981个DEG，其中上调基因301个，下调基因680个。Metascape 的 GO 分析显示，DEG 在细胞分化、细胞粘附、细胞内信号转导和蛋白激酶活性调节中显着富集。KEGG通路分析显示，DEGs主要富集于cAMP信号通路、Rap1信号通路、脂肪细胞脂解调节、PI3K-Akt信号通路、MAPK信号通路等。基于DEGs的PPI网络，确定了以下6个候选基因：PIK3R1、RAC1、GNG3、GNAI1、CDC42和ITGB1。结论。我们的数据提供了基因、功能和途径的全面生物信息学分析，这些分析可能与 T2DM 的发病机制有关。
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