Department of Thoracic-Cardiovascular Surgery, Shanghai Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
J Cell Mol Med. 2024 Apr;28(8):e18264. doi: 10.1111/jcmm.18264.
Acute myocardial infarction (AMI) increasingly precipitates severe heart failure, with diagnoses now extending to progressively younger demographics. The focus of this study was to pinpoint critical genes linked to both AMI and anoikis, thereby unveiling potential novel biomarkers for AMI detection and intervention. Differential analysis was performed to identify significant differences in expression, and gene functionality was explored. Weighted gene coexpression network analysis (WGCNA) was used to construct gene coexpression networks. Immunoinfiltration analysis quantified immune cell abundance. Protein-protein interaction (PPI) analysis identified the proteins that interact with theanoikis. MCODE identified key functional modules. Drug enrichment analysis identified relevant compounds explored in the DsigDB. Through WGCNA, 13 key genes associated with anoikis and differentially expressed genes were identified. GO and KEGG pathway enrichment revealed the regulation of apoptotic signalling pathways and negative regulation of anoikis. PPI network analysis was also conducted, and 10 hub genes, such as IL1B, ZAP70, LCK, FASLG, CD4, LRP1, CDH2, MERTK, APOE and VTN were identified. IL1B were correlated with macrophages, mast cells, neutrophils and Tcells in MI, and the most common predicted medications were roxithromycin, NSC267099 and alsterpaullone. This study identified key genes associated with AMI and anoikis, highlighting their role in immune infiltration, diagnosis and medication prediction. These findings provide valuable insights into potential biomarkers and therapeutic targets for AMI.
急性心肌梗死（AMI）越来越容易引发严重心力衰竭，现在的诊断范围已经扩展到越来越年轻的人群。本研究的重点是确定与 AMI 和 anoikis 相关的关键基因，从而为 AMI 的检测和干预揭示潜在的新型生物标志物。进行差异分析以确定表达的显著差异，并探索基因功能。使用加权基因共表达网络分析（WGCNA）构建基因共表达网络。免疫浸润分析量化了免疫细胞的丰度。蛋白质-蛋白质相互作用（PPI）分析确定了与 anoikis 相互作用的蛋白质。MCODE 确定了关键功能模块。药物富集分析确定了在 DsigDB 中探索的相关化合物。通过 WGCNA，确定了与 anoikis 相关的 13 个关键基因和差异表达基因。GO 和 KEGG 通路富集揭示了凋亡信号通路的调节和 anoikis 的负调节。还进行了 PPI 网络分析，确定了 10 个枢纽基因，如 IL1B、ZAP70、LCK、FASLG、CD4、LRP1、CDH2、MERTK、APOE 和 VTN。IL1B 与 MI 中的巨噬细胞、肥大细胞、中性粒细胞和 T 细胞相关，最常见的预测药物是罗红霉素、NSC267099 和 alsterpaullone。本研究确定了与 AMI 和 anoikis 相关的关键基因，强调了它们在免疫浸润、诊断和药物预测中的作用。这些发现为 AMI 的潜在生物标志物和治疗靶点提供了有价值的见解。
