Identification of blood-based inflammatory biomarkers for the early-stage detection of acute myocardial infarction

Acute myocardial infarction (AMI) is a coronary artery disease that prevents blood and oxygen flow from reaching the heart. The underlying complex pathophysiology of AMI has made the early detection process very challenging. A potential biomarker with high accuracy in determining AMI can help us in the risk stratification of patients with chest pain and also reduce treatment costs. This study aimed to identify the key genes for early AMI detection from the expression data of peripheral blood samples. We retrieved three GEO datasets from NCBI that represent expression data of healthy individuals and early-stage AMI patients. The differentially expressed genes (DEG) were determined from three datasets by the GEO2R tool on the NCBI webpage. The significant DEGs common in at least 2 datasets were identified by VENNY 2.1 web tool. We then performed a functional enrichment analysis of the selected genes and also the potential hub genes possibly involved in AMI were predicted by a protein–protein interaction network. Finally, a drug–gene interaction network was constructed. We found 5360, 2049, and 579 genes, respectively, from the GSE61144, GSE60993, and GSE29532 datasets to be the significant DEGs in GEO2R analysis. A total of 214 genes were found common in at least two datasets. CD59, FCAR, CLEC5A, CKAP4, and CEACAM8 are the most significant hub genes predicted by protein–protein network analysis that has a close relationship with the early response to inflammation in AMI. Our study suggests CD59, FCAR, CLEC5A, CKAP4, and CEACAM8 as the potential inflammatory biomarkers for the early-stage detection of AMI.