生物
基因
小RNA
接收机工作特性
生物信息学
微阵列
基因表达
折叠变化
基因调控网络
微阵列分析技术
计算生物学
医学
内科学
遗传学
作者
Shengjue Xiao,Yufei Zhou,Qi Wu,Qiaozhi Liu,Mengli Chen,Tiantian Zhang,Zhu Hong,Jie Liu,Ting Yin,Defeng Pan
标识
DOI:10.1089/dna.2020.6447
摘要
This study aimed to explore the potential diagnostic biomarkers and mechanisms underlying acute myocardial infarction (AMI). We downloaded four datasets (GSE19339, GSE48060, GSE66360, and GSE97320) from the Gene Expression Omnibus database and combined them as an integrated dataset. A total of 153 differentially expressed genes (DEGs) were analyzed by the linear models for microarray analysis (LIMMA) package. Weighted gene co-expression network analysis was used to screen for the significant gene modules. The intersection of DEGs and genes in the most significant module was termed "common genes" (CGs). CGs were mainly enriched in "inflammatory response," "neutrophil chemotaxis," and "IL-17 signaling pathway" through functional enrichment analyses. Subsequently, 15 genes were identified as the hub genes in the protein–protein interaction network. The Fc fragment of IgE receptor Ig (FCER1G) and prostaglandin-endoperoxide synthase 2 (PTGS2) showed significantly increased expression in AMI patients and mice at the 12-h time point in our experiments. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of FCER1G and PTGS2. The area under ROC curve of FCER1G and PTGS2 was 77.6% and 80.7%, respectively. Moreover, the micro (mi)RNA-messenger (m)RNA network was also visualized; the results showed that miRNA-143, miRNA-144, and miRNA-26 could target PTGS2 in AMI progression.
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