Identification of signature genes and functional genetic variants in heart failure by integrated bioinformatics analysis

Heart failure (HF) is a syndrome which the heart fails to meet the metabolic needs of the tissues and affects millions of people all over the world, bringing a great burden to families and society. Studies have shown that genetic factors play an important role in the occurrence and development of HF, but the genetic molecular mechanisms of HF remain to be explored. In our study, the bioinformatics methods were used in combination, the microarray datasets of GSE57338, GSE76701 were retrieved from the gene expression comprehensive database. After merging the above two microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. We identified 181 patients with HF and 140 normal controls (NC). There were 408 DEGs among HF samples, including 224 up-regulated genes and 184 down-regulated genes. The GO and KEGG enrichment analysis revealed the molecular mechanism of HF. GSEA enrichment analysis showed that most DEGs were significantly enriched in wnt signal pathway, histidine metabolism, beta alanine metabolism and so on. PPI networks showed that target genes CXCL10, DDX60, HERC6, IFI44L, IFIT1, IFIT2, IFIT3, MX1, RSAD2, XAF1 are expected to become new targets for HF. The eQTL analysis showed that the hub genes DDX60, HERC6, IFI44L, IFIT1, IFIT2, IFIT3, MX1, RSAD2, XAF1 are regulated by the eight genetic variations of single nucleotide polymorphisms, including rs55730499, rs140570886, rs600038, rs740363, rs1520832, rs10812610, rs6473383 and rs563519. Our findings provide potential biomarkers or therapeutic targets that are genetically regulated for the further study of HF, which contribute to the development of advanced prediction, diagnosis and treatment strategies.