Screening of biomarkers and key pathways in diabetic cardiomyopathy based on bioinformatics analysis
Received:February 14, 2019  
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DOI:10.11915/j.issn.1671-5403.2019.05.066
Key words:bioinformatics  diabetic cardiomyopathy  markers This work was supported by the National Natural Science Foundation of China
Author NameAffiliationE-mail
LI Ning Department of Cardiology, Cardiovascular Research Institute of Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan 430060, China  
WU Hai-Ming Department of Cardiology, Cardiovascular Research Institute of Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan 430060, China  
GENG Rong-Xin Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan 430060, China  
TANG Qi-Zhu Department of Cardiology, Cardiovascular Research Institute of Wuhan University, Hubei Key Laboratory of Cardiology, Wuhan 430060, China qztang@whu.edu.cn 
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Abstract:
      Objective To investigate the potential biomarkers and key pathways in regulation implicated with diabetic cardiomyopathy (DCM) by analyzing the gene chips in gene expression omnibus (GEO) database. Methods GSE26887 was selected from GEO database to identify the differentially expressed genes (DEGs). DAVID was applied to perform gene ontology (GO) and the Kyoto encyclopedia of genes and genomes (KEGG) analyses. A protein-protein interaction (PPI) network was also constructed to visualize the interactions among these DEGs using bioinformatics statistics STRING 10.0. Results GSE26887 contained 7 DCM patients and 5 healthy individuals. A total of 236 DEGs were captured, including 134 upregulated and 102 downregulated genes. The top-5 upregulated DEGs were NPPA, SFRP4, DSC1, NEB and FRZB, and the top-5 of down-regulation were SERPINE1, SERPINA3, ANKRD2, XRCC4 and S100A8. The results of GO and KEGG disclosed that these DEGs were significantly enriched in inflammation, immune disorders, metabolic disturbance and mitochondrial dysfunction in the development of DCM. The top 15 hub genes with the highest connectivity in the PPI network were IL-6, MYC, ACTA2, SERPINE1, ASPN, SPP1, KIT, TFRC, FMOD, PDE5A, MYH6, FPR1, C3, CDKN1A and SOCS3 in order. Conclusion Our obtained DEGs are closely associated with inflammation, immune disorders and metabolic disturbance in DCM. The top 5 upregulated and the top 5 downregulated DEGs may be regarded biomarkers for DCM diagnosis, and the 15 hub genes be the target for the treatment.
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