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.

中文翻译：

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FCER1G和PTGS2担任综合生物信息学分析急性心肌梗死基于潜在的诊断性生物标记

本研究旨在探索潜在的急性心肌梗死 (AMI) 诊断生物标志物和机制。我们从 Gene Expression Omnibus 数据库下载了四个数据集（GSE19339、GSE48060、GSE66360 和 GSE97320），并将它们组合为一个集成数据集。通过微阵列分析线性模型 (LIMMA) 包分析了总共 153 个差异表达基因 (DEG)。加权基因共表达网络分析用于筛选显着基因模块。最显著模块中DEGS和基因的交点被称为“共同基因”（CGS）。皮质颗粒主要通过功能富集分析富集“炎性反应”，“中性粒细胞趋”和“IL-17信号转导途径”。接着，15个基因被鉴定为蛋白 - 蛋白相互作用网络中的集线器基因。FCER1G）和前列腺素内过氧化物合酶2（PTGS2）在我们的实验中12小时时间点显示出显著增加AMI病人和小鼠表达。接收器操作特性（ROC）曲线来评估的诊断价值FCER1G和PTGS2。的ROC曲线下的面积FCER1G和PTGS2分别为77.6％和80.7％。此外，微（MI）RNA信使（米）RNA网络也被可视化; 结果表明miRNA的-143，的miRNA-144和miRNA-26可以定位PTGS2在AMI进展。