Department of Nephrology, China-Japan Friendship Hospital, Beijing, PR China.
Department of Nephrology, Fuwai Hospital, Chinese Academy of Medical Science, Beijing, PR China.
Ren Fail. 2023 Dec;45(1):2216307. doi: 10.1080/0886022X.2023.2216307.
The incidence of acute myocardial infarction (AMI) in hemodialysis (HD) patients is high and the prognosis is extremely poor. However, the potential connection between HD and AMI, and its regulatory mechanisms remain unclear. In this study, the gene expression profiles of HD (GSE15072) and AMI (GSE66360) were downloaded from the Gene Expression Omnibus database, common differentially expressed genes (DEGs) were obtained using the limma R package, the biological functions were analyzed according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, machine learning was conducted to identify hub genes. Receiver operating characteristic curves and gene set enrichment analyses were used to explore the characters and biological function of hub genes, networks were used for candidate identification of transcription factor (TF), microRNA (miRNA), and drug. After a total of 255 common DEGs were selected, GO and KEGG analyses indicated that neutrophil extracellular trap (NET) may be a potential connection between HD and AMI, LILRB2, S100A12, CYBB, ITGAM, and PPIF were finally identified as hub genes. The area under curve of LILRB2, S100A12, and PPIF was higher than 0.8 in both datasets. Networks show the relationship between hub genes, TF, and miRNA, also the relationship between potential drugs and protein. In conclusion, NETs may be the potential connection between AMI and HD. The potential hub gene, signaling pathways, and drugs provided by this study may contribute to future AMI prevention and intervention in HD patients.
血液透析（HD）患者急性心肌梗死（AMI）的发病率高，预后极差。然而，HD 与 AMI 之间的潜在联系及其调节机制尚不清楚。本研究从基因表达综合数据库中下载了 HD（GSE15072）和 AMI（GSE66360）的基因表达谱，使用 limma R 包获得共同差异表达基因（DEGs），根据基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析进行生物功能分析，采用机器学习识别关键基因。使用受试者工作特征曲线和基因集富集分析探索关键基因的特征和生物学功能，使用网络进行转录因子（TF）、微小 RNA（miRNA）和药物的候选识别。共筛选出 255 个共同 DEGs，GO 和 KEGG 分析表明中性粒细胞胞外诱捕网（NET）可能是 HD 和 AMI 之间的潜在联系，最终确定 LILRB2、S100A12、CYBB、ITGAM 和 PPIF 为关键基因。LILRB2、S100A12 和 PPIF 在两个数据集的曲线下面积均高于 0.8。网络显示了关键基因、TF 和 miRNA 之间的关系，以及潜在药物和蛋白质之间的关系。总之，NET 可能是 AMI 与 HD 之间的潜在联系。本研究提供的关键基因、信号通路和药物可能有助于未来 HD 患者 AMI 的预防和干预。
