Diagnostic Performance and Potential Molecular Mechanisms of IGF-1-related genes in Myocardial Infarction

Abstract Backgrounds Myocardial infarction (MI) is ischemic necrosis of the myocardium due to the formation of plaque on the inner walls of the arteries. Insulin-like growth factor 1 (IGF-1) plays a prominent role in maintaining cardiac homeostasis. Investigating the molecular mechanism of the genes associated with IGF-1 in MI and the identification of diagnostic markers and therapeutic drugs could assist in treating MI. Methods Two datasets in Gene Expression Omnibus (GEO), GSE66360 and GSE60993, were subjected into this study. Primarily, the candidate genes were analyzed by differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The machine learning algorithms were used to select hub genes from candidate genes. In addition, the signaling pathway of hub genes were explored by Gene Set Enrichment Analysis (GSEA). The databases Diana-microT, miRNet, StarBase, and GeneMANIA were used to predict miRNAs, lncRNAs, transcription factors (TFs) and related genes, respectively. Eventually, the Comparative Toxicogenomics Database (CTD) was analyzed to investigate the relationship between hub genes and MI. Small-molecule drugs prediction through the utilization of the Drug-Gene Interaction Database (DGIdb). Results 5 hub genes associated with IGF-1 (ALDH2, NLRP3, CLEC4E, ACSL1, FCER1G) have been discovered in MI. These genes exhibited promising diagnostic potential and were implicated in the occurrence of MI. The regulatory network disclosed that ACSL1 predicted 7 miRNAs, 372 lncRNAs and 1 TF. Furthermore, 14 small-molecule drugs targeting hub genes were predicted. Conclusion Integrating bioinformatics approaches, we found 5 hub genes with diagnostic efficacy for MI. Moreover, the study discovered signaling pathways connected to MI, offering novel perspectives on the molecular causes of MI. Additionally, drugs targeting hub genes were predicted, which also able to supply new treatment options for MI in clinical settings.