Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

Abstract

Background. In the general population, acute myocardial infarction (AMI) represents a significant cause of mortality. This study is aimed at identifying novel diagnostic biomarkers to aid in treating and diagnosing AMI. Methods. The Gene Expression Omnibus (GEO) database was explored to extract two microarray datasets, GSE66360 and GSE48060, which were subsequently merged into a single cohort. Both AMI and control samples were analyzed for differentially expressed genes (DEGs), which were subsequently subjected to weighed gene coexpression network analysis (WGCNA) to identify the most significant module. Gene Ontology (GO) and pathway analyses subsequently carried out the most significant gene modules along with construction of a protein-protein interaction network (PPI). Cytoscape plugin cytoHubba allowed for the prediction of the top 4 key genes according to the network maximal clique centrality (MCC) algorithm. The expression levels and diagnostic value of the four key genes were additionally verified in the GSE62646 dataset. Results. A WCGNA analysis revealed 878 DEGs which were clustered into 6 modules. The module with the most significance in AMI was colored blue. Subsequent GO and KEGG pathway enrichment analysis on blue module genes revealed that they were primarily enriched in the inflammation-related pathways. These findings, in combination with PPI and coexpression networks, resulted in the identification of the top four genes by cytoHubba, which included leukocyte immunoglobulin-like receptor B2 (LILRB2), toll-like receptor 2 (TLR2), neutrophil cytosolic factor 2 (NCF2), and S100A9. Among them, LILRB2, NCF2, and S100A9 were validated in the GSE62646 dataset. Conclusions. The results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.

The Identification and Validation of Hub Genes Associated with Acute Myocardial Infarction Using Weighted Gene Co-Expression Network Analysis

“…Intriguingly, Xie et al inferred that four genes (FN1, CD34, LPL, and WWTR1) were capable of distinguishing STEMI patients from healthy controls and SCAD [37]. Wang et al identified 4 hub genes (LILRB2, TLR2, NCF2, and S100 A9) related to AMI based on three databases (GSE, GSE, and GSE) [38]. However, the identification of RPL9 and RPL26 in our study has not been previously investigated to be related to AMI.…”

Identification of Potential Molecular Mechanism Related to Infertile Endometriosis

“…WGCNA can integrate the results of sequencing into biologically significant co-expressed gene modules and analyze the correlation between these gene modules and diseases ( 12 ). Therefore, we used WGCNA to explore modules related to infertile endometriosis.…”

“…WGCNA can explore the relationship between diseases and gene modules, which act as significant markers and signaling pathways in the occurrence and development of diseases ( 7 , 8 ). Therefore, WGCNA is frequently employed to investigate the molecular mechanisms of various diseases including coronary artery disease, neuropathic pain, colon adenocarcinoma, and acute myocardial infarction ( 9 – 12 ).…”

Shuangxinfang Prevents S100A9-Induced Macrophage/Microglial Inflammation to Improve Cardiac Function and Depression-Like Behavior in Rats After Acute Myocardial Infarction

“…Besides, 174 components have been identified from Chuanxiong Rhizoma, among which phthalides and alkaloids would be the main bioactive ingredients for the pharmacological properties (Chen et al, 2018). Our previous clinical trials have already confirmed that PCF could relieve angina pectoris and improve depressive symptoms (Wang et al, 2021). The pharmacological mechanism of PCF concentrates on the regulation of inflammatory response and the neuroendocrinology system.…”

“…To systematically identify possible targets and explore the biological mechanism of PCF in depression after AMI, we have performed pharmacoproteomic profiling of the myocardium and hippocampus in rats from the sham, AMI, and PCF groups using label-free liquid chromatography-mass spectrometry (LC-MS/ MS) (Sun et al, 2021). The intersection of differentially expressed proteins (DEPs) in the peri-infarct border zone and hippocampus produces a unique protein, that is S100A9, which has become a topic molecule in the cardiovascular field during these years (Nagareddy et al, 2020;Wang et al, 2021). The role of S100A9 in driving inflammatory response after MI has attracted much attention and has been identified as a potential therapeutic target (Li et al, 2019).…”