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Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis

Xuanqiang Che, Ran Zhao, Hua Xu, Xue Liu, Shumiao Zhao, Hongwei Ma

(Department of Endocrinology, Fifth People’s Hospital of Jinan, Jinan, Shandong, China (mainland))

Med Sci Monit 2019; 25:9237-9244

DOI: 10.12659/MSM.918407

BACKGROUND: The aim of this study was to evaluate the differently expressed genes (DEGs) relevant to type 2 diabetes mellitus (T2DM) and pathway by performing integrated bioinformatics analysis.
MATERIAL AND METHODS: The gene expression datasets GSE7014 and GSE29221 were downloaded in GEO database, and DEGs from type 2 diabetes mellitus and normal skeletal muscle tissues were identified. Biological function analysis of the DEGs was enriched by GO and KEEG pathway. A PPI network for the identified DEGs was built using the STRING database.
RESULTS: Thirty top DEGs were identified from 2 datasets: GSE7014 and GSE29221. Of the 30 top DEGs, 20 were up-regulated and 10 were down-regulated. The 20 up-regulated genes were enriched in regulation of mRNA, protein biding, and phospholipase D signaling pathway. The 10 down-regulated genes were enriched in telomere maintenance via semi-conservative replication, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance pathway. In the PPI network of 20 up-regulated DEGs, there were 40 nodes and 84 edges, with an average node degree of 4.2. For the 10 down-regulated DEGs, we found a total of 30 nodes and 105 edges, with an average node degree of 7.0 and local clustering coefficient of 0.812. Among the 30 DEGs, 10 hub genes (CNOT6L, CNOT6, CNOT1, CNOT7, RQCD1, RFC2, PRIM1, RFC4, RFC5, and RFC1) were also identified through Cytoscape.
CONCLUSIONS: DEGs of T2DM may play an essential role in disease development and may be potential pathogeneses of T2DM.

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