Identification of potential biomarkers and immune cell infiltration in acute myocardial infarction (AMI) using bioinformatics strategy

Abstract

Acute myocardial infarction (AMI) was considered a fatal disease resulting in high morbidity and mortality; platelet activation or aggregation plays a critical role in participating in the pathogenesis of AMI. The current study aimed to reveal the underlying mechanisms of platelets in the confrontation of AMI and potential biomarkers that separate AMI from other cardiovascular diseases and healthy people with bioinformatic strategies. Immunity analysis revealed that the neutrophil was significantly decreased in patients with SCAD compared with patients with STsegment elevation myocardial infarction (STEMI) or healthy controls; monocytes and neutrophils showed potential in distinguishing patients with STEMI from patients with SCAD. Six differentially expressed genes (DEGs) showed great performances in differentiating STEMI patients from SCAD patients with AUC greater than 0.9. Correlation analysis showed that these six DEGs were significantly positively correlated with neutrophils; three genes were negatively correlated with monocytes. Weighted gene co-expression network analysis (WGCNA) found that module 'royalblue' had the highest correlation with STEMI; genes in STEMI-related module were enriched in cell-cell interactions, blood vessels' biological processes, and peroxisome proliferator-activated receptor (PPAR) signaling pathway; four genes (FN1, CD34, LPL, and WWTR1) represented the capability of identifying patients with STEMI from healthy controls and patients with SCAD; two genes (ARG1 and NAMPTL) were considered as novel biomarkers for identifying STEMI from SCAD; FN1 represented the potential as a novel biomarker for STEMI. Our findings indicated that the distribution of neutrophils could be considered as a potential molecular trait for separating patients with STEMI from SCAD.

Analysis of susceptibility genes and myocardial infarction risk correlation of ischemic cardiomyopathy based on bioinformatics

“…In this study, it was shown that calcitriol can reduce the expression of the key gene FCGR3A. Previous studies (30)(31)(32) have shown that calcitriol can reduce the expression level of RAGE in cardiomyocytes, reduce the accumulation of AGE in the aortic wall of diabetic rats, reduce the expression level of AGE-induced interleukin 6 (IL-6), and inhibit the activity of NF-κB.…”

Identification of marophage-related biomarkers in acute myocardial infarction (AMI) by bioinformatic analysis and clinical validation

“…Nevertheless, there is a lack of agreement on the optimal cardiac biomarkers. The immune response plays an essential role in the development and repair of AMI [13]., and macrophages, as an important component of intrinsic immunity, can be divided into pro-in ammatory M1-type macrophages and anti-in ammatory M2-type macrophages [14]. Different subtypes of macrophages are capable of performing essential functions in tissue repair after AMI, including phagocytosis of necrotic tissue cell debris, regulation of angiogenesis, and in uence on brosis and scar formation.…”

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

“…They found that Aqp1, Armcx1, Gsta4, Hist3 h2 a, and Il17 as hub genes of STEMI were mainly enriched in cell membrane signal transduction, while Olr1, Nap1 l3, Gfer, Dohh, Crispld1, and Ccdc8 b as hub genes of NSTEMI were markedly related to energy metabolism [36]. 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].…”