Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis
YiXuan Lin,1 Jinju Li,1 Di Wu,1 FanJing Wang,1 ZhaoHui Fang,2,3 GuoMing Shen1 1Graduate School of Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of China; 2Department of Endocrinology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine,...
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oai:doaj.org-article:49dd6bcd2df3456cb4d743d78f92d4602021-12-02T10:26:14ZIdentification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis1178-7007https://doaj.org/article/49dd6bcd2df3456cb4d743d78f92d4602020-05-01T00:00:00Zhttps://www.dovepress.com/identification-of-hub-genes-in-type-2-diabetes-mellitus-using-bioinfor-peer-reviewed-article-DMSOhttps://doaj.org/toc/1178-7007YiXuan Lin,1 Jinju Li,1 Di Wu,1 FanJing Wang,1 ZhaoHui Fang,2,3 GuoMing Shen1 1Graduate School of Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of China; 2Department of Endocrinology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, Anhui, People’s Republic of China; 3Anhui Academic of Traditional Chinese Medicine Diabetes Research Institute, Hefei, Anhui, People’s Republic of ChinaCorrespondence: ZhaoHui Fang; GuoMing Shen Tel +86-13085513100Email fangzhaohui9097@163.com; shengm_66@163.comBackground: Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in the world with complicated pathogenesis. This study aimed to identify differentially expressed genes (DEGs) and molecular pathways in T2DM using bioinformatics analysis.Materials and Methods: To explore potential therapeutic targets for T2DM, we analyzed three microarray datasets (GSE50397, GSE38642, and GSE44035) acquired from the Gene Expression Omnibus (GEO) database. DEGs between T2DM islet and normal islet were picked out by the GEO2R tool and Venn diagram software. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to identify the pathways and functional annotation of DEGs. Then, protein–protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes/Proteins (STRING).Results: In total, we identified 36 DEGs in the three datasets, including 32 up-regulated genes and four down-regulated genes. The improved functions and pathways of the DEGs enriched in cytokine–cytokine receptor interaction, pathways in cancer, PI3K-Akt signaling pathway, and Rheumatoid arthritis. Among them, ten hub genes with a high degree of connectivity were selected. Furthermore, via the re-analysis of DAVID, four genes (IL6, MMP3, MMP1, and IL11) were significantly enriched in the Rheumatoid arthritis pathway.Conclusion: Our study, based on the GEO database, identified four significant up-regulated DEGs and provided novel targets for diagnosis and treatment of T2DM.Keywords: bioinformatics analysis, microarray, differentially expressed genes, type 2 diabetes mellitusLin YLi JWu DWang FFang ZShen GDove Medical Pressarticlebioinformatics analysismicroarraydifferentially expressed genestype 2 diabetesSpecialties of internal medicineRC581-951ENDiabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Vol Volume 13, Pp 1793-1801 (2020) |
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bioinformatics analysis microarray differentially expressed genes type 2 diabetes Specialties of internal medicine RC581-951 |
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bioinformatics analysis microarray differentially expressed genes type 2 diabetes Specialties of internal medicine RC581-951 Lin Y Li J Wu D Wang F Fang Z Shen G Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis |
| description |
YiXuan Lin,1 Jinju Li,1 Di Wu,1 FanJing Wang,1 ZhaoHui Fang,2,3 GuoMing Shen1 1Graduate School of Anhui University of Chinese Medicine, Hefei, Anhui, People’s Republic of China; 2Department of Endocrinology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, Anhui, People’s Republic of China; 3Anhui Academic of Traditional Chinese Medicine Diabetes Research Institute, Hefei, Anhui, People’s Republic of ChinaCorrespondence: ZhaoHui Fang; GuoMing Shen Tel +86-13085513100Email fangzhaohui9097@163.com; shengm_66@163.comBackground: Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in the world with complicated pathogenesis. This study aimed to identify differentially expressed genes (DEGs) and molecular pathways in T2DM using bioinformatics analysis.Materials and Methods: To explore potential therapeutic targets for T2DM, we analyzed three microarray datasets (GSE50397, GSE38642, and GSE44035) acquired from the Gene Expression Omnibus (GEO) database. DEGs between T2DM islet and normal islet were picked out by the GEO2R tool and Venn diagram software. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to identify the pathways and functional annotation of DEGs. Then, protein–protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes/Proteins (STRING).Results: In total, we identified 36 DEGs in the three datasets, including 32 up-regulated genes and four down-regulated genes. The improved functions and pathways of the DEGs enriched in cytokine–cytokine receptor interaction, pathways in cancer, PI3K-Akt signaling pathway, and Rheumatoid arthritis. Among them, ten hub genes with a high degree of connectivity were selected. Furthermore, via the re-analysis of DAVID, four genes (IL6, MMP3, MMP1, and IL11) were significantly enriched in the Rheumatoid arthritis pathway.Conclusion: Our study, based on the GEO database, identified four significant up-regulated DEGs and provided novel targets for diagnosis and treatment of T2DM.Keywords: bioinformatics analysis, microarray, differentially expressed genes, type 2 diabetes mellitus |
| format |
article |
| author |
Lin Y Li J Wu D Wang F Fang Z Shen G |
| author_facet |
Lin Y Li J Wu D Wang F Fang Z Shen G |
| author_sort |
Lin Y |
| title |
Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis |
| title_short |
Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis |
| title_full |
Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis |
| title_fullStr |
Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis |
| title_full_unstemmed |
Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis |
| title_sort |
identification of hub genes in type 2 diabetes mellitus using bioinformatics analysis |
| publisher |
Dove Medical Press |
| publishDate |
2020 |
| url |
https://doaj.org/article/49dd6bcd2df3456cb4d743d78f92d460 |
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