Objective. A growing body of emerging evidence indicates that metabolic processes play a pivotal role in the biological processes underlying acute myocardial infarction (AMI). The aim of the current study was to identify featured metabolism-related genes in patients with AMI using a support vector machine (SVM) and to further explore the value of these genes in the diagnosis of AMI. Methods. Gene microarray expression data related to AMI were downloaded from the GSE66360 dataset in the Gene Expression Omnibus (GEO) database. This data set consisted of 50 AMI samples and 49 normal controls that were randomly classified into a discovery cohort (21 AMI samples and 22 normal controls) and a validation cohort (28 AMI and 28 normal controls). We applied a machine learning method that combined SVM with recursive feature elimination (RFE) to discriminate AMI patients from normal controls. Based on this, an SVM classifier was constructed. Receiver operating characteristic (ROC) analysis was used to investigate the predictive value for the early diagnosis of AMI in the two cohorts and was then further verified in an independent external cohort. Results. Three metabolism-related genes were identified based on SVM-RFE (AKR1C3, GLUL, and PDE4B). The SVM classifier based on the three genes allowed for excellent discrimination between AMI and healthy samples in both the discovery cohort () and the validation cohort (), and this was further confirmed in the GSE68060 dataset (). Additionally, the SVM classifier allowed for perfect discrimination between recurrent AMI events and nonrecurrent events in the GSE68060 cohort (). GO and KEGG pathway enrichment analysis of the identified featured genes revealed significant enrichment of specific metabolic pathways. Conclusion. The identified metabolism-related genes may play important roles in the development of AMI and may represent diagnostic and therapeutic biomarkers of AMI.

中文翻译：

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急性心肌梗死患者特征性代谢相关基因的鉴定

客观。越来越多的新证据表明，代谢过程在急性心肌梗死 (AMI) 的生物学过程中发挥着关键作用。本研究的目的是利用支持向量机（SVM）识别 AMI 患者的特征代谢相关基因，并进一步探讨这些基因在 AMI 诊断中的价值。方法. 从基因表达综合 (GEO) 数据库中的 GSE66360 数据集下载与 AMI 相关的基因微阵列表达数据。该数据集由 50 个 AMI 样本和 49 个正常对照组成，随机分为发现队列（21 个 AMI 样本和 22 个正常对照）和验证队列（28 个 AMI 和 28 个正常对照）。我们应用了一种机器学习方法，将 SVM 与递归特征消除 (RFE) 相结合，将 AMI 患者与正常对照区分开来。在此基础上，构建了一个 SVM 分类器。接受者操作特征 (ROC) 分析用于研究两个队列中 AMI 早期诊断的预测价值，然后在独立的外部队列中进一步验证。结果. 基于 SVM-RFE 鉴定了三个代谢相关基因（AKR1C3、GLUL和PDE4B）。基于这三个基因的 SVM 分类器允许在两个发现队列中很好地区分 AMI 和健康样本（)和验证队列 (），这在 GSE68060 数据集中得到了进一步证实（）。此外，SVM 分类器可以完美区分 GSE68060 队列中的复发性 AMI 事件和非复发性事件（）。对鉴定的特征基因进行 GO 和 KEGG 途径富集分析揭示了特定代谢途径的显着富集。结论。已鉴定的代谢相关基因可能在 AMI 的发展中发挥重要作用，并可能代表 AMI 的诊断和治疗生物标志物。