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Cardiol J. 2018 Sep 24. doi: 10.5603/CJ.a2018.0113. [Epub ahead of print]

Identification of biomarkers and mechanisms of diabetic cardiomyopathy using microarray data.

Author information

1
Department of Endocrinology, Shaanxi Provincial People's Hospital. yangweiwang15@hotmail.com.

Abstract

BACKGROUND:

The study aimed to uncover the regulation mechanisms of diabetic cardiomyopathy (DCM) and provide novel prognostic biomarkers.

METHODS:

The dataset GSE62203 downloaded from the Gene Expression Omnibus database was utilized in the present study. After pretreatment using the Affy package, differentially expressed genes (DEGs) were identified by the limma package, followed by functional enrichment analysis and protein-protein interaction (PPI) network analysis. Furthermore, module analysis was conducted using MCODE plug-in of Cytoscape, and functional enrichment analysis was also performed for genes in the modules.

RESULTS:

A set of 560 DEGs were screened, mainly enriched in the metabolic process and cell cycle related process. Hub nodes in the PPI network were LDHA (lactate dehydrogenase A), ALDOC (aldolase C, fructose-bisphosphate) and ABCE1 (ATP Binding Cassette Subfamily E Member 1), which were also highlighted in Module 1 or Module 2 and predominantly enriched in the processes of glycolysis and ribosome biogenesis. Additionally, LDHA were linked with ALDOC in the PPI network. Besides, activating transcription factor 4 (ATF4) was prominent in Module 3; while myosin heavy chain 6 (MYH6) was highlighted in Module 4 and was mainly involved in muscle cells related biological processes.

CONCLUSIONS:

Five potential biomarkers including LDHA, ALDOC, ABCE1, ATF4 and MYH6 were identified for DCM prognosis.

KEYWORDS:

diabetic cardiomyopathy; differential analysis; expression profile; glycolysis; module analysis; ribosome biogenesis

PMID:
30246236
DOI:
10.5603/CJ.a2018.0113
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