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Meta-Analysis
. 2020 Jan 4;11(1):60.
doi: 10.3390/genes11010060.

Meta-Analysis of Dilated Cardiomyopathy Using Cardiac RNA-Seq Transcriptomic Datasets

Ahmad Alimadadi  1   2 , Patricia B Munroe  3   4 , Bina Joe  1   2 , Xi Cheng  1
Affiliations
Meta-Analysis

Meta-Analysis of Dilated Cardiomyopathy Using Cardiac RNA-Seq Transcriptomic Datasets

Ahmad Alimadadi et al. Genes (Basel). .

Abstract

Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Several studies have used RNA-sequencing (RNA-seq) to profile differentially expressed genes (DEGs) associated with DCM. In this study, we aimed to profile gene expression signatures and identify novel genes associated with DCM through a quantitative meta-analysis of three publicly available RNA-seq studies using human left ventricle tissues from 41 DCM cases and 21 control samples. Our meta-analysis identified 789 DEGs including 581 downregulated and 208 upregulated genes. Several DCM-related genes previously reported, including MYH6, CKM, NKX2-5 and ATP2A2, were among the top 50 DEGs. Our meta-analysis also identified 39 new DEGs that were not detected using those individual RNA-seq datasets. Some of those genes, including PTH1R, ADAM15 and S100A4, confirmed previous reports of associations with cardiovascular functions. Using DEGs from this meta-analysis, the Ingenuity Pathway Analysis (IPA) identified five activated toxicity pathways, including failure of heart as the most significant pathway. Among the upstream regulators, SMARCA4 was downregulated and prioritized by IPA as the top affected upstream regulator for several DCM-related genes. To our knowledge, this study is the first to perform a transcriptomic meta-analysis for clinical DCM using RNA-seq datasets. Overall, our meta-analysis successfully identified a core set of genes associated with DCM.

Keywords: RNA-seq; dilated cardiomyopathy; heat failure; human; meta-analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The workflow of the meta-analysis.
Figure 2
Figure 2
Venn diagram showing the number of differential expressed genes from individual studies and meta-analysis.
Figure 3
Figure 3
Toxicity pathways significantly activated in DCM through IPA-Tox: (A) failure of heart; (B) congestive heart failure; (C) dilation of left ventricle; (D) dilation of heart chamber; (E) congestive heart disease; (F) integrated toxicity pathways. As an example, in Figure 3A, the DES gene was downregulated as indicated by the green color and the downregulation of DES further promoted (indicated by the orange dash line) the activation of failure of heart, as indicated by the orange color. Other indicators are explained in the Prediction Legend section.
Figure 4
Figure 4
Network of the upstream regulator SMARCA4 and its targeted genes. The network was constructed using the differentially expressed genes identified in our meta-analysis. For example, the downregulation of SMARCA4 led to the inhibition (indicated by the blue arrow line) of the DES expression and the downregulation of DES is indicated by the green color. Other indicators are explained in the Prediction Legend section.
Figure 5
Figure 5
Significant canonical pathways with absolute z-score > 2.0. The z-score represents the activation or inhibition state of a canonical pathway. A z-score greater than 2.0 was considered as significantly activated. A z-score smaller than −2.0 was considered as significantly inhibited.
See this image and copyright information in PMC

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