Tong Xiao, Zhao Xinyi, Dang Xuan, Kou Yan, Kou Junjie
Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Key Laboratory of Myocardial Ischemia, Chinese Ministry of Education, Harbin, China.
Front Cardiovasc Med. 2022 Feb 11;9:836067. doi: 10.3389/fcvm.2022.836067. eCollection 2022.
This study aimed to determine early diagnosis genes of acute myocardial infarction (AMI) and then validate their association with ferroptosis, immune checkpoints, and N6-methyladenosine (m6A), which may provide a potential method for the early diagnosis of AMI. Firstly, we downloaded microarray data from NCBI (GSE61144, GSE60993, and GSE42148) and identified differentially expressed genes (DEGs) in samples from healthy subjects and patients with AMI. Also, we performed systematic gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used STRING to predict protein interactions. Moreover, MCC and MCODE algorithms in the cytoHubba plug-in were used to screen nine key genes in the network. We then determined the diagnostic significance of the nine obtained DEGs by plotting receiver operating characteristic curves using a multiscale curvature classification algorithm. Meanwhile, we investigated the relationship between AMI and immune checkpoints, ferroptosis, and m6A. In addition, we further validated the key genes through the GSE66360 dataset and consequently obtained nine specific genes that can be used as early diagnosis biomarkers for AMI. Through screening, we identified 210 DEGs, including 53 downregulated and 157 upregulated genes. According to GO, KEGG, and key gene screening results, and could be used for early prediction of AMI. Finally, we found that AMI was associated with ferroptosis, immune checkpoints, and m6A and and are effective markers for the diagnosis of AMI, which can provide new prospects for future studies on the pathogenesis of AMI.
本研究旨在确定急性心肌梗死(AMI)的早期诊断基因,然后验证它们与铁死亡、免疫检查点和N6-甲基腺苷(m6A)的关联,这可能为AMI的早期诊断提供一种潜在方法。首先,我们从NCBI下载了微阵列数据(GSE61144、GSE60993和GSE42148),并鉴定了健康受试者和AMI患者样本中的差异表达基因(DEG)。此外,我们进行了系统的基因本体(GO)和京都基因与基因组百科全书(KEGG)分析,并使用STRING预测蛋白质相互作用。此外,利用cytoHubba插件中的MCC和MCODE算法筛选网络中的9个关键基因。然后,我们使用多尺度曲率分类算法绘制受试者工作特征曲线,确定所获得的9个DEG的诊断意义。同时,我们研究了AMI与免疫检查点、铁死亡和m6A之间的关系。此外,我们通过GSE66360数据集进一步验证了关键基因,从而获得了9个可作为AMI早期诊断生物标志物的特异性基因。通过筛选,我们鉴定出210个DEG,包括53个下调基因和157个上调基因。根据GO、KEGG和关键基因筛选结果,[具体基因名称1]和[具体基因名称2]可用于AMI的早期预测。最后,我们发现AMI与铁死亡、免疫检查点和m6A相关,[具体基因名称1]和[具体基因名称2]是诊断AMI的有效标志物,这可为未来AMI发病机制的研究提供新的前景。