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Integrated microarray analysis to identify potential biomarkers and therapeutic targets in dilated cardiomyopathy.

ABSTRACT: Dilated cardiomyopathy (DCM) is a primary cardiomyopathy with high mortality. The aim of the present study was to identify the related genes in DCM. The four expression profiles (GSE17800, GSE21610, GSE42955 and GSE79962) downloaded from the Gene Expression Omnibus (GEO) database were analyzed using RankProd and metaMA R packages to identify differentially expressed genes (DEGs). DEGs were uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID), for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein?protein interaction (PPI) network of the DEGs was constructed using the STRING database. In addition, hub genes were identified using the Cytoscape plugin cytoHubba. A mouse DCM model, which established via intraperitoneal injection with doxorubicin (DOX), was used to validate the hub genes. A total of 898 DEGs were identified across the four microarrays. Furthermore, GO analysis demonstrated that these DEGs were mainly enriched in cell adhesion, negative regulation of cell proliferation, negative regulation of apoptotic process and potassium ion transport. In addition, KEGG analysis revealed that DEGs were mainly enriched in the ECM?receptor interaction, the p53 signaling pathway, cardiac muscle contraction and the hypoxia?inducible factor signaling pathway. Proenkephalin (PENK), chordin like 1 (CHRDL1), calumenin (CALU), apolipoprotein L1, insulin?like growth factor binding protein 3 (IGFBP3) and ceruloplasmin (CP) were identified as hub genes in the PPI network. Furthermore, the expression levels of PENK, CHRDL1, IGFBP3, CP and CALU in hearts with DCM were validated using a mouse model. In conclusion, the present study identified six hub genes related to DCM. Therefore, the present results may provide a potential mechanism for DCM involving these hub genes, which may serve as biomarkers for screening and diagnosis in the clinic.

SUBMITTER: Zhang H

PROVIDER: S-EPMC7339620 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Integrated microarray analysis to identify potential biomarkers and therapeutic targets in dilated cardiomyopathy.

Zhang Hao H Huo Junyu J Jiang Wanying W Shan Qijun Q

Molecular medicine reports 20200514 2

Dilated cardiomyopathy (DCM) is a primary cardiomyopathy with high mortality. The aim of the present study was to identify the related genes in DCM. The four expression profiles (GSE17800, GSE21610, GSE42955 and GSE79962) downloaded from the Gene Expression Omnibus (GEO) database were analyzed using RankProd and metaMA R packages to identify differentially expressed genes (DEGs). DEGs were uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID), for Gene Ontology ...[more]

PMID: 32626989

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Project description:BackgroundDilated cardiomyopathy (DCM) is currently the major cause of systolic heart failure. This study explored potential therapeutic targets and investigated the role of immune cell infiltration in DCM.MethodsThree DCM datasets (GSE3585, GSE9800, and GSE84796) from the Gene Expression Omnibus (GEO) database were merged into an integrated dataset, and batch effects were removed. Differentially expressed genes (DEGs) were screened and the associations between gene co-expression modules and clinical traits were assessed by weighted gene co-expression network analysis (WGCNA) in R software. Any DEGs from the integrated dataset overlapped with the significant module genes were defined as common genes (CGs). Enrichment analysis of the CGs was performed. The protein-protein interaction (PPI) network of the CGs was visualized and the hub gene was identified by using Cytoscape 3.8.2 software. The miRNA-transcription factor-mRNA (miRNA-TF-mRNA) network was constructed using Cytoscape to unveil the regulatory relationships in DCM. Finally, the CIBERSORT method (https://cibersort.stanford.edu/) was used to investigate immune cell infiltration in DCM.ResultsA total of 53 DEGs were identified, and 5 gene co-expression modules were detected by WGCNA of the DCM and control group samples of cardiac tissue. Genes such as FRZB, ASPN, and PHLDA1 were significantly upregulated, whereas IDH2 and ENDOG were significantly downregulated. Functional enrichment analysis showed that CGs were mainly enriched in the extracellular matrix (ECM) signaling pathway. ASPN was the hub gene in the PPI network. The miRNA-TF-mRNA network revealed that FRZB and ASPN were targeted by paired related homeobox 2 (Prrx2). We also found that miR-129-5p could regulate ASPN, PHLDA1, and IDH2 simultaneously. The immune infiltration analysis revealed higher levels of M1 macrophages in DCM samples than in the control samples.ConclusionsIn conclusion, we speculate that miR-129-5p might target ASPN in regulating DCM via the ECM signaling pathway. Macrophage infiltration may be involved in ECM remodeling and eventually lead to DCM.

Project description:Despite the availability of several effective and promising treatment methods, heart failure (HF) remains a significant public health concern that requires advanced therapeutic strategies and techniques. Dilated cardiomyopathy (DCM) is a crucial factor that contributes to the development and deterioration of HF. The aim of the present study was to identify novel biomarkers and biological pathways to enhance the diagnosis and treatment of patients with DCM-induced HF using weighted gene co-expression network analysis (WGCNA). A total of 24 co-expressed gene modules connected with DCM-induced HF were obtained by WGCNA. Among these, the blue module had the highest correlation with DCM-induced HF (r=0.91; P<0.001) and was enriched in the AGE-RAGE signaling pathway in diabetic complications, the p53 and MAPK signaling pathway, adrenergic signaling in cardiomyocytes, the Janus kinase-STAT signaling pathway and cGMP/PKG signaling. Eight key genes, including secreted protein acidic and rich in cysteine-related modular calcium-binding protein 2 (SMOC2), serpin family A member 3 (SERPINA3), myosin heavy chain 6 (MYH6), S100 calcium binding protein A9 (S100A9), tubulin α (TUBA)3E, TUBA3D, lymphatic vessel endothelial hyaluronic acid receptor 1 (LYVE1) and phospholipase C ε1 (PLCE1), were selected as the therapeutic targets of DCM-induced HF based on WGCNA and differentially expressed gene analysis. Immune cell infiltration analysis revealed that the proportion of naive B cells and CD4-activated memory T cells was markedly upregulated in DCM-induced HF tissues compared with tissues from healthy controls. Furthermore, reverse transcription-quantitative PCR in AC16 human cardiomyocyte cells treated with doxorubicin showed that among the eight key genes, only SERPINA3, MYH6, S100A9, LYVE1 and PLCE1 exhibited expression levels identical to those revealed by bioinformatics analysis, suggesting that these genes may be involved in the development of DCM-induced HF.

Project description:Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "limma" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "Consensus Cluster Plus" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.

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Integrated microarray analysis to identify potential biomarkers and therapeutic targets in dilated cardiomyopathy.

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Integrated microarray analysis to identify potential biomarkers and therapeutic targets in dilated cardiomyopathy.

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