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Identification of the Molecular Mechanisms Underlying Dilated Cardiomyopathy Via Bioinformatic Analysis of Gene Expression Profiles

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Identification of the Molecular Mechanisms Underlying Dilated Cardiomyopathy Via Bioinformatic Analysis of Gene Expression Profiles EXPERIMENTAL AND THERAPEUTIC MEDICINE 13: 273-279, 2017 Identification of the molecular mechanisms underlying dilated cardiomyopathy via bioinformatic analysis of gene expression profiles HU ZHANG1, ZHUO YU2, JIANCHAO HE1, BAOTONG HUA2 and GUIMING ZHANG1 Departments of 1Cardiaovascular Surgery and 2Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China Received March 26, 2015; Accepted April 21, 2016 DOI: 10.3892/etm.2016.3953 Abstract. In the present study, gene expression profiles of binding protein 3 (IGFBP3)], ‘muscle organ development’ patients with dilated cardiomyopathy (DCM) were re‑analyzed (SMAD7) and ‘regulation of cell migration’ [SMAD7, IGFBP3 with bioinformatics tools to investigate the molecular mecha- and insulin receptor (INSR)]. Notably, signal transducer and nisms underlying DCM. Gene expression dataset GSE3585 activator of transcription 3, SMAD7, INSR, CTGF, exportin 1, was downloaded from Gene Expression Omnibus, which IGFBP3 and phosphatidylinositol‑4,5‑bisphosphate 3‑kinase, included seven heart biopsy samples obtained from patients catalytic subunit alpha were hub nodes with the higher degree with DCM and five healthy controls. Differential analysis was in the PPI network. Therefore, the results of the present study performed using a Limma package in R to screen for differen- suggested that DEGs may alter the biological processes of tially expressed genes (DEGs). Functional enrichment analysis ‘nucleosome formation’, ‘cell adhesion’, ‘skeletal system devel- was subsequently conducted for DEGs using the Database opment’, ‘muscle organ development’ and ‘regulation of cell for Annotation, Visualization and Integration Discovery. A migration’ in the development of DCM. protein‑protein interaction (PPI) network was constructed using information from Search Tool for the Retrieval of Introduction Interacting Genes software. A total of 89 DEGs were identi- fied in the patients with DCM, including 67 upregulated and Dilated cardiomyopathy (DCM) is the most frequent type 22 downregulated genes. Functional enrichment analysis of non‑ischemic cardiomyopathy worldwide, with an esti- demonstrated that the downregulated genes predominantly mated prevalence of 1 in 2,500 people and an incidence of encoded chromosomal proteins and transport‑related proteins, 7/100,000 people annually (1,2). DCM is characterized by which were significantly associated with the biological dilatation and reduced contractile function of the left and right processes of ‘nucleosome assembly’, ‘chromatin assembly’, ventricles, which may lead to progressive heart failure and ‘protein‑DNA complex assembly’, ‘nucleosome organization’ sudden cardiac‑associated mortality in 4.5‑79.3% of sufferers and ‘DNA packaging’ (H1 histone family member 0, histone at 3 years (3). Therefore, the early diagnosis and treatment of cluster 1 H1c, histone cluster 1 H2bd and H2A histone family DCM is important in order to prevent a poor prognosis. member Z). The upregulated genes detected in the present The molecular mechanisms underlying DCM have been study encoded secreted proteins or phosphotransferase, which extensively explored in an attempt to provide effective diag- were associated with biological processes including ‘cell nostic and therapeutic methods for DCM. These previous adhesion’ [connective tissue growth factor (CTGF)], ‘skeletal studies have demonstrated that DCM is associated with muta- system development’ [CTGF and insulin‑like growth factor tions in genes encoding cytoskeletal, contractile or inner‑nuclear membrane proteins (4,5), including actin, desmin (6) and lamins A and C (7). Furthermore, it has been demonstrated that mutations in sarcomere protein genes account for ~10% of cases of familial DCM (8,9). Mitochondrial DNA mutations Correspondence to: Dr Guiming Zhang, Department of and mitochondrial abnormalities have also been implicated in Cardiaovascular Surgery, The First Affiliated Hospital of Kunming the pathogenesis of DCM by altering myocardial ATP genera- Medical University, 295 Xichang Road, Kunming, Yunnan 650032, tion (10), including mitochondrial Hsp40 which has a crucial P.R. China E‑mail: [email protected] role in preventing DCM (11). As a result, various potential biomarkers have been elucidated, including high‑sensitivity Key words: dilated cardiomyopathy, gene expression profile, cardiac troponin T (12), serum matrix metalloproteinase‑3 (13) differentially expressed genes, functional enrichment analysis, and serum Tenascin‑C (14). However, the mechanisms and protein‑protein interaction network pathogenesis underlying DCM remain poorly understood. Microarray technology enables the measurement of global gene expression levels, and thus facilitates the identification of 274 ZHANG et al: GENE EXPRESSION PROFILING OF DILATED CARDIOMYOPATHY A B Figure 1. Box plots of gene expression data (A) before and (B) after normalization. Dilated cardiomyopathy samples are presented in blue, whereas normal controls are pink. Medians are represented as black horizontal lines in the boxes. crucial genes and altered biological processes in DCM (15‑17). for Annotation, Visualization and Integration Discovery Although Barth et al (16) have previously identified a common (version 6.7; http://david.abcc.ncifcrf.gov/) (22), with a gene expression signature in DCM via microarray data, deeper two‑tailed Fisher exact test based on the hypergeometric analysis of the massive gene expression dataset is required via distribution. Benjamini corrected P‑values of <0.05 were set as bioinformatics. Therefore, the present study reanalyzed gene the cut‑off to screen out significant Gene Ontology biological expression data (16) to identify differentially expressed genes process (GOBP) terms (23) and Kyoto Encyclopedia of Genes (DEGs) which may contribute to the development of DCM. and Genomes (KEGG) pathways (24). Following this, functional enrichment and protein‑protein interaction (PPI) analyses of the DEGs were performed using Construction of PPI network. A PPI network was constructed for various bioinformatics tools. These findings may advance the DEGs using the Search Tool for the Retrieval of Interacting understanding of the molecular mechanisms underlying DCM. Genes (STRING; version 9.1; http://string‑db.org/) (25). Interacting pairs with a combined score>0.4 were selected Materials and methods for the construction of the PPI network. Proteins serve as the ‘nodes’ in the network and each pairwise protein interaction is Gene expression data. Gene expression dataset (accession represented by an undirected link. Degree was calculated for number, GSE3585) (16) was downloaded from Gene Expression each node, which corresponds to the number of interactions Omnibus (http://ncbi.nlm.nih.gov/geo/). The GSE3585 dataset of a protein with other proteins. Hub genes were subsequently included seven heart biopsy samples obtained from patients selected according to the degree. with DCM at time of transplantation (GSM82386‑GSM82392) and five non‑failing (NF) heart biopsies from NF donor Results hearts (GSM82381‑GSM82386). Gene expression levels were measured using Affymetrix Human Genome U133A Array Microarray data analysis identified DEGs between the DCM (Affymetrix Inc., Santa Clara, CA, USA). and control groups. Following data normalization, the mean expression levels of the gene expression profiles were presented Data pre‑processing and differential analysis. Raw data in one consistent line (Fig. 1). Subsequently, according to the (CEL file) were read by an Affy package (http://biocon- criteria of |log FC| >0.3 and FDR <0.05, 89 DEGs were detected ductor.org/packages/release/bioc/html/affy.html) (18) of R between the DCM and NF hearts, including 22 downregulated (version 3.1.2; http://r‑project.org/) (19). Probes detected in and 67 upregulated genes. A greater number of upregulated >50% of samples were retained. Background correction, genes were detected, as compared with the downregulated data normalization and determination of expression levels genes, which demonstrated that the DEGs had a tendency to was conducted using a Robust Multi‑array Average analysis upregulate in DCM. Based on the DEGs, the DCM samples method (20). were predominantly separated from the NF controls, implying Differential analysis was performed using the linear model the reliability of the DEGs (Fig. 2). of the lmFit and empirical Bayes moderated t test provided by the Limma package (http://bioconductor.org/pack- GO and KEGG pathway analysis revealed the functions of ages/release/bioc/html/limma.html) (21). |log Fold Change DEGs involved in DCM. Functional enrichment analysis was (FC) |>0.3 and false discovery rate (FDR) <0.05 were set as performed for the downregulated and upregulated genes, the cut‑offs to screen DEGs between DCM and NF hearts. respectively. For the downregulated genes, two clusters were obtained using the Protein Information Resource. The Functional enrichment analysis. Functional enrichment first cluster asw demonstrated to be associated with ‘chro- analysis of the DEGs was conducted using the Database mosomal proteins’, including H1 histone family member 0 EXPERIMENTAL AND THERAPEUTIC MEDICINE 13: 273-279, 2017 275 Figure 2. Heat map of hierarchical clustering analysis of the differentially expressed genes. (H1F0; logFC=‑0.61; FDR=1.91E‑02), H2A histone family six GOBP terms were detected in the upregulated genes member Z (H2AFZ; logFC=‑0.47; FDR=1.94E‑02), histone (P<0.05), including ‘cell adhesion’ (CTGF), ‘skeletal
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