Beijing University of Chinese Medicine, Beijing 100102, China.
National Cancer Center, National Clinical Research Center for Cancer, Chinese Medicine Department of the Cancer Hospital of the Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Biomed Res Int. 2021 Sep 13;2021:5584681. doi: 10.1155/2021/5584681. eCollection 2021.
Acute coronary syndrome (ACS) is a complex syndrome of clinical symptoms. In order to accurately diagnose the type of disease in ACS patients, this study is aimed at exploring the differentially expressed genes (DEGs) and biological pathways between acute myocardial infarction (AMI) and unstable angina (UA). The GSE29111 and GSE60993 datasets containing microarray data from AMI and UA patients were downloaded from the Gene Expression Omnibus (GEO) database. DEG analysis of these 2 datasets is performed using the "limma" package in R software. DEGs were also analyzed using protein-protein interaction (PPI), Molecular Complex Detection (MCODE) algorithm, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis and "cytoHubba" were used to analyze the hub genes. A total of 286 DEGs were obtained from GSE29111 and GSE60993, including 132 upregulated genes and 154 downregulated genes. Subsequent comprehensive analysis identified 20 key genes that may be related to the occurrence and development of AMI and UA and were involved in the inflammatory response, interaction of neuroactive ligand-receptor, calcium signaling pathway, inflammatory mediator regulation of TRP channels, viral protein interaction with cytokine and cytokine receptor, human cytomegalovirus infection, and cytokine-cytokine receptor interaction pathway. The integrated bioinformatical analysis could improve our understanding of DEGs between AMI and UA. The results of this study might provide a new perspective and reference for the early diagnosis and treatment of ACS.
急性冠状动脉综合征（ACS）是一种复杂的临床症状综合征。为了准确诊断 ACS 患者的疾病类型，本研究旨在探讨急性心肌梗死（AMI）和不稳定型心绞痛（UA）之间差异表达基因（DEGs）和生物学途径。从基因表达综合数据库（GEO）中下载包含 AMI 和 UA 患者微阵列数据的 GSE29111 和 GSE60993 数据集。使用 R 软件中的“limma”包对这 2 个数据集进行 DEG 分析。还使用蛋白质-蛋白质相互作用（PPI）、分子复合物检测（MCODE）算法、基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析对 DEGs 进行分析。使用相关性分析和“cytoHubba”分析来分析枢纽基因。从 GSE29111 和 GSE60993 中获得了 286 个 DEGs，包括 132 个上调基因和 154 个下调基因。随后的综合分析确定了 20 个关键基因，这些基因可能与 AMI 和 UA 的发生和发展有关，涉及炎症反应、神经活性配体-受体相互作用、钙信号通路、TRP 通道炎症介质调节、病毒蛋白与细胞因子和细胞因子受体相互作用、人巨细胞病毒感染和细胞因子-细胞因子受体相互作用途径。综合生物信息学分析可以提高我们对 AMI 和 UA 之间 DEGs 的理解。本研究的结果可能为 ACS 的早期诊断和治疗提供新的视角和参考。
