Identification of biomarkers, pathways and potential therapeutic targets for heart failure using bioinformatics analysis

Abstract

Heart failure (HF) is a complex cardiovascular diseases associated with high mortality. To discover key molecular changes in HF, we analyzed next-generation sequencing (NGS) data of HF. In this investigation, differentially expressed genes (DEGs) were analyzed using limma in R package from GSE161472 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network, miRNA-hub gene regulatory network and TF-hub gene regulatory network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), REACTOME pathway, STRING, HiPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, we performed receiver operating characteristic curve (ROC) analysis of hub genes. A total of 930 DEGs 9464 up regulated genes and 466 down regulated genes) were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections and the citric acid (TCA) cycle and respiratory electron transport. Subsequently, the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and 10 hub genes in these network were focused on by centrality analysis and module analysis. Furthermore, data showed that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B were good diagnostic values. In summary, this study suggests that HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4 and KAT2B may act as the key genes in HF.