Identification of Signature Genes of Dilated Cardiomyopathy Using Integrated Bioinformatics Analysis

Abstract

Dilated cardiomyopathy (DCM) is characterized by left ventricular or biventricular enlargement with systolic dysfunction. To date, the underlying molecular mechanisms of dilated cardiomyopathy pathogenesis have not been fully elucidated, although some insights have been presented. In this study, we combined public database resources and a doxorubicin-induced DCM mouse model to explore the significant genes of DCM in full depth. We first retrieved six DCM-related microarray datasets from the GEO database using several keywords. Then we used the "LIMMA" (linear model for microarray data) R package to filter each microarray for differentially expressed genes (DEGs). Robust rank aggregation (RRA), an extremely robust rank aggregation method based on sequential statistics, was then used to integrate the results of the six microarray datasets to filter out the reliable differential genes. To further improve the reliability of our results, we established a doxorubicin-induced DCM model in C57BL/6N mice, using the "DESeq2" software package to identify DEGs in the sequencing data. We cross-validated the results of RRA analysis with those of animal experiments by taking intersections and identified three key differential genes (including BEX1, RGCC and VSIG4) associated with DCM as well as many important biological processes (extracellular matrix organisation, extracellular structural organisation, sulphur compound binding, and extracellular matrix structural components) and a signalling pathway (HIF-1 signalling pathway). In addition, we confirmed the significant effect of these three genes in DCM using binary logistic regression analysis. These findings will help us to better understand the pathogenesis of DCM and may be key targets for future clinical management.

Keywords: DEGs; RNA-Seq; dilated cardiomyopathy; microarray; robust rank aggregation.

Figure 1

Volcano plots of the DEGs for the six datasets, where red dots represent upregulated genes, blue dots represent downregulated genes, and grey dots represent genes that did not change significantly. (A) GSE3585, (B) GSE29819, (C) GSE42955, (D) GSE43435, (E) GSE79962, (F) GSE84796.

Figure 2

GO enrichment analysis results of RRA-analysed differential genes.

Figure 3

KEGG pathway enrichment analysis of RRA-analysed differential genes.

Figure 4

Representative echocardiograms (A) and related cardiac function indices (B–E) of healthy and DCM mice. * represent p-value < 0.05; ** represent p-value < 0.01; *** represent p-value < 0.001.

Figure 5

Volcano diagram of RNA-Seq results. Red dots indicate upregulated genes, blue dots downregulated genes, and grey dots indicate genes with no significant change.

Figure 6

The Venn diagram indicating the intersection of RRA and RNA-Seq analysis results. (A) Upregulated genes, (B) downregulated gene, (C) the intersection of GO, and (D) the intersection of KEGG.

Figure 7

RGC32 may impair cardiac function through the Rho/ROCK axis and immune cell pathways.

Figure 8

Degradation of HIF under normoxic conditions and the HIF signalling pathway under acute ischaemic conditions mediates protection through VEGF, EPO, and CD73.