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. 2018 Aug 16;25(1):63.
doi: 10.1186/s12929-018-0459-8.

Identification of target genes in cardiomyopathy with fibrosis and cardiac remodeling

Jianquan Zhao  1 , Tiewei Lv  2 , Junjun Quan  2 , Weian Zhao  2 , Jing Song  3 , Zhuolin Li  4 , Han Lei  4 , Wei Huang  4 , Longke Ran  5
Affiliations

Identification of target genes in cardiomyopathy with fibrosis and cardiac remodeling

Jianquan Zhao et al. J Biomed Sci. .

Abstract

Background: Identify genes probably associated with chronic heart failure and predict potential target genes for dilated cardiomyopathy using bioinformatics analyses.

Methods: Gene expression profiles (series number GSE3585 and GSE42955) of cardiomyopathy patients and healthy controls were downloaded from the Expression Omnibus Gene (GEO) database. Differential expression of genes (DEGS) between the two groups of total 14 cardiomyopathy patients and 10 healthy controls were subsequently identified by limma package of R. Database for Annotation, Visualization, and Integrated Discovery (DAVID Tool), which is an analysis of enriched biological processes. Search Tool for the Retrieval Interacting Genes (STRING) was used as well for the analysis of protein-protein interaction network (PPI). Prediction of the potential drugs was suggested based on the preliminarily identified genes using Connectivity Map (CMap).

Results: Eighty-nine DEGs were identified (57 up-regulated and 32 down-regulated). The most enrichment Gene Ontology (GO) terms (P < 0.05) contain genes involved in extracellular matrix (ECM) and biological adhesion signal pathways (P < 0.05, ES > 1.5) such as ECM-receptors, focal adhesion and transforming growth factor beta (TGF-β), etc. Fifty-one differentially expressed genes were found to encode interacting proteins. Eleven key genes along with related transcription factors were identified including CTGF, POSTN, CORIN, FIGF, etc. CONCLUSION: Bioinformatics-based analyses reveal the targeted genes probably associated with cardiomyopathy, which provide clues for pharmacological therapies aiming at the targets.

Keywords: Bioinformatics; Dilated cardiomyopathy; Heart failure; Microarray.

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Figures

Fig. 1
Fig. 1
The heat-map of differential expression genes (P < 0.05, |logFC| > 1). NF: Control group, DCM: DCM group, up-regulated genes were in red and down-regulated genes were in blue
Fig. 2
Fig. 2
a Functional analysis in KEGG pathway (P < 0.05, ES > 1.5). b A diagram performed in order to find the number of collective genes. The size of the circle in the B is not related to the number of genes in the pathway or intersection
Fig. 3
Fig. 3
The protein interaction network constructed by STRING (a). 3 sub-networks (b, c, d) of PPI network performed using Cytoscape
Fig. 4
Fig. 4
The expression level of selected 11 genes between control samples and DCM samples
Fig. 5
Fig. 5
a Functional enrichment analysis based on Gene Ontology (P < 0.05, Benjamin< 0.01). b Number of differential expression genes enriched in each term
Fig. 6
Fig. 6
Transcription factors binding site predicted by JASPAR, Hnf-4a (Fig. 6a), Myb (Fig. 6b) and Tef-1 (Fig. 6c)
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