NDLI: Gene coexpression network analysis identified potential biomarkers in gestational diabetes mellitus progression

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Gene coexpression network analysis identified potential biomarkers in gestational diabetes mellitus progression

Content Provider	Scilit
Author	Zhao, Xiaomin Li, Wen
Copyright Year	2018
Description	Journal: Molecular Genetics & Genomic Medicine Background Gestational diabetes mellitus (GDM) is one of the most common problems during pregnancy. Lack of international consistent diagnostic procedures has limit improvement of current therapeutic effectiveness. Here, we aimed to screen potential gene biomarkers that might play vital roles in GDM progression for assistance of its diagnostic and treatment. Methods Gene expression profiles in four GDM placentae at first trimester, four GDM placentae at second trimester, and four normal placentae were obtained from the publicly available Gene Expression Omnibus (GEO). Weighted gene coexpression network analysis (WGCNA) indicated two gene modules, that is, black and brown module, that was significantly positively and negatively correlated with GDM progression time points, respectively. Additionally, a significant positive correlation between module membership (MM) and degree in protein–protein interaction network of brown module genes was observed. Results KIF2C, CENPE, CCNA2, AURKB, MAD2L1, CCNB2, CDC20, PLK1, CCNB1, and CDK1 all have degree larger than 50 and MM larger than 0.9, so they might be valuable biomarkers in GDM. Gene set enrichment analysis inferred tight relations between carbohydrate metabolism or steroid biosynthesis‐related processes and GDM progression. Conclusions All in all, our study should provide several novel references for GDM diagnosis and therapeutic.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382444/pdf
e-ISSN	23249269
DOI	10.1002/mgg3.515
Journal	Molecular Genetics & Genomic Medicine
Issue Number	1
Volume Number	7
Language	English
Publisher	Wiley-Blackwell
Publisher Date	2018-11-25
Access Restriction	Open
Subject Keyword	Journal: Molecular Genetics & Genomic Medicine Medical Laboratory Technology Gene Module
Content Type	Text
Resource Type	Article

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