Bioinformatics Analyses Reveal the Autophagy-related Feature Biomarkers in Dilated Cardiomyopathy with Heart Failure

Abstract

Abstract Objective Through the bioinformatics analysis of public data sets, to explore the potential biomarkers related to autophagy in the development of dilated cardiomyopathy (DCM) with heart failure (HF). Methods Microarray datasets for heart tissue in DCM with HF or healthy control were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by R software. Enrichment analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GESA) were performed using DAVID and GSEA software. Hub genes were identified by protein-protein interaction (PPI) networks and were validated in GSE21610 through the R software ggplot2 package and IBM SPSS Statistics 25. Results A total of 222 genes were obtained from the human autophagy database (http://www.autophagy.lu/index.html). There were 32 differentially expressed autophagy-related genes identified in the GSE29819 dataset in 14 samples of complicated dilated cardiomyopathy and 12 normal samples. GO, KEGG and GESA enrichment analysis showed that DEGs were mainly enriched in autophagy-related biological processes and cytokine signaling pathway. Seven hub genes and two gene cluster modules were identified using Cytoscape. Finally, it was further determined that FOXO1, FOXO3, CTSD and RAB7A as potential biomarkers have significant expression differences and diagnostic value in the GSE21610 dataset. Conclusion According to bioinformatics analysis, we found that the candidate genes FOXO1, FOXO3, CTSD and RAB7A were potentially related to the procession of autophagy and might be new diagnostic biomarkers for DCM with HF.