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Abstract：
OBJECTIVE: Glycolysis and immune metabolism play important roles in acute myocardial infarction (AMI). Therefore, this study aimed to identify and experimentally validate the glycolysis-related hub genes in AMI as diagnostic biomarkers, and further explore the association between hub genes and immune infiltration.
METHODS: Differentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs.
RESULTS: A total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting.
CONCLUSIONS: We concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.
METHODS: Differentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs.
RESULTS: A total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting.
CONCLUSIONS: We concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.
摘要：
目的：糖酵解和免疫代谢在急性心肌梗死（AMI）中发挥重要作用。因此,这项研究旨在鉴定和实验验证AMI中糖酵解相关的hub基因作为诊断生物标志物，并进一步探讨hub基因与免疫浸润的关系。
方法：使用R软件分析AMI外周血单个核细胞（PBMC）的差异表达基因（DEGs）。糖酵解相关的DEGs（GRDEGs）使用注释数据库进行识别和分析，可视化,和集成发现(DAVID)功能丰富。使用STRING数据库构建蛋白质-蛋白质相互作用网络，并使用Cytoscape软件进行可视化。使用CIBERSORT进行AMI患者和稳定型冠状动脉疾病（SCAD）对照组之间的免疫浸润分析，GRDEGs与免疫细胞浸润的相关性分析。我们还绘制了列线图和受试者工作特征（ROC）曲线，以评估GRDEG对AMI发生的预测准确性。最后,使用逆转录-定量聚合酶链反应(RT-qPCR)和使用PBMC的蛋白质印迹对关键基因进行了实验验证。
结果：在AMI后的第一天和4-6天，共鉴定出132个GRDEGs和56个GRDEGs，分别。富集分析表明，这些GRDEGs主要聚集在糖酵解/糖异生和代谢途径中。五个中心基因(HK2，PFKL，PKM,G6PD,和ALDOA）使用cytoHubba插件选择。免疫细胞和hub基因之间的联系表明HK2，PFKL，PKM,ALDOA与单核细胞和中性粒细胞呈显著正相关,而G6PD与中性粒细胞呈显著正相关。校正曲线,决策曲线分析,和ROC曲线表明五个中心GRDEGs对AMI具有较高的预测价值。此外，通过RT-qPCR和Western印迹对5个中心GRDEGs进行了验证.
结论：我们得出的结论是HK2、PFKL、PKM,G6PD,ALDOA是AMI的中枢GRDEGs，在AMI的进展中起重要作用。本研究为AMI的治疗提供了一种新的潜在的免疫治疗方法。
方法：使用R软件分析AMI外周血单个核细胞（PBMC）的差异表达基因（DEGs）。糖酵解相关的DEGs（GRDEGs）使用注释数据库进行识别和分析，可视化,和集成发现(DAVID)功能丰富。使用STRING数据库构建蛋白质-蛋白质相互作用网络，并使用Cytoscape软件进行可视化。使用CIBERSORT进行AMI患者和稳定型冠状动脉疾病（SCAD）对照组之间的免疫浸润分析，GRDEGs与免疫细胞浸润的相关性分析。我们还绘制了列线图和受试者工作特征（ROC）曲线，以评估GRDEG对AMI发生的预测准确性。最后,使用逆转录-定量聚合酶链反应(RT-qPCR)和使用PBMC的蛋白质印迹对关键基因进行了实验验证。
结果：在AMI后的第一天和4-6天，共鉴定出132个GRDEGs和56个GRDEGs，分别。富集分析表明，这些GRDEGs主要聚集在糖酵解/糖异生和代谢途径中。五个中心基因(HK2，PFKL，PKM,G6PD,和ALDOA）使用cytoHubba插件选择。免疫细胞和hub基因之间的联系表明HK2，PFKL，PKM,ALDOA与单核细胞和中性粒细胞呈显著正相关,而G6PD与中性粒细胞呈显著正相关。校正曲线,决策曲线分析,和ROC曲线表明五个中心GRDEGs对AMI具有较高的预测价值。此外，通过RT-qPCR和Western印迹对5个中心GRDEGs进行了验证.
结论：我们得出的结论是HK2、PFKL、PKM,G6PD,ALDOA是AMI的中枢GRDEGs，在AMI的进展中起重要作用。本研究为AMI的治疗提供了一种新的潜在的免疫治疗方法。
