Identification of molecular signatures in acute myocardial infarction based on integrative analysis of proteomics and transcriptomics

Abstract

Background: Myocardial infarction (MI) is one of the most serious cardiovascular diseases, characterized by a rapid and irreversible decline in myocardial function. Early detection of patients with MI and prolonging the optimal therapeutic window of acute myocardial infarction (AMI) are particularly important. This study aimed to identify the diagnostic biomarkers and novel therapeutic targets for acute myocardial infarction.

Method: We generated the AMI mouse models by ligating the proximal left anterior descending coronary artery. Six time points-Sham, AMI 10-min, 1-h, 6-h, 24-h, and 72-h-were chosen to examine the molecular changes that occur during the AMI process. RNA-seq and DIA-MS were performed on the infarcted left ventricular tissues of AMI mice at each time point. Co-expression pattern genes were screened from myocardial infarction samples at different time points by time-series analysis. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to examine these genes. Using the Interactive Gene/Protein Retrieval Tool (STRING) database, the protein-protein interaction network (PPI) was constructed and the hub genes were identified. In order to evaluate the diagnostic value of hub genes, a receiver operating characteristic (ROC) curve was constructed. An independent data set, GSE163772, confirmed the diagnostic value of hub genes further.

Result: We obtained the expression profiles at different time points after the occurrence of heart failure through high-throughput sequencing, and found 167 genes with similar expression patterns through time series analysis. The immune response and immune-related pathways had the greatest enrichment of these genes. Among them, Itgb2 Syk, Tlr4, Tlr2, Itgax, and Lcp2 may play key roles as hub genes. Combined with the results of proteomic analysis, it was found that the expression of Coro1a in both omics increased with time. The results of external validation showed that TLR2, ITGAX, and LCP2 had good predictive ability for AMI diagnosis.

Conclusion: Itgb2, Syk, Tlr4, Tlr2, Itgax, Lcp2 and Coro1a are considered to be the seven key genes significantly associated with AMI. Our results may provide potential targets for the prevention of adverse ventricular remodeling and the treatment of AMI.

Keywords: Acute myocardial infarction; Coro1a; Proteomics; Transcriptomics; itgb2.