The First Hospital of Jiaxing Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, 314001, People's Republic of China.
Jiaxing University Master Degree Cultivation Base, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, 310053, People's Republic of China.
Funct Integr Genomics. 2023 May 13;23(2):160. doi: 10.1007/s10142-023-01081-5.
Patients diagnosed with stable coronary artery disease (CAD) are at continued risk of experiencing acute myocardial infarction (AMI). This study aims to unravel the pivotal biomarkers and dynamic immune cell changes, from an immunological, predictive, and personalized viewpoint, by implementing a machine-learning approach and a composite bioinformatics strategy. Peripheral blood mRNA data from different datasets were analyzed, and CIBERSORT was used for deconvoluting human immune cell subtype expression matrices. Weighted gene co-expression network analysis (WGCNA) in single-cell and bulk transcriptome levels was conducted to explore possible biomarkers for AMI, with a particular emphasis on examining monocytes and their involvement in cell-cell communication. Unsupervised cluster analysis was performed to categorize AMI patients into different subtypes, and machine learning methods were employed to construct a comprehensive diagnostic model to predict the occurrence of early AMI. Finally, RT-qPCR on peripheral blood samples collected from patients validated the clinical utility of the machine learning-based mRNA signature and hub biomarkers. The study identified potential biomarkers for early AMI, including CLEC2D, TCN2, and CCR1, and found that monocytes may play a vital role in AMI samples. Differential analysis revealed that CCR1 and TCN2 exhibited elevated expression levels in early AMI compared to stable CAD. Machine learning methods showed that the glmBoost+Enet [alpha=0.9] model achieved high predictive accuracy in the training set, external validation sets, and clinical samples in our hospital. The study provided comprehensive insights into potential biomarkers and immune cell populations involved in the pathogenesis of early AMI. The identified biomarkers and the constructed comprehensive diagnostic model hold great promise for predicting the occurrence of early AMI and can serve as auxiliary diagnostic or predictive biomarkers.
患有稳定型冠状动脉疾病 (CAD) 的患者仍有发生急性心肌梗死 (AMI) 的风险。本研究旨在从免疫学、预测学和个性化角度，通过机器学习方法和综合生物信息学策略，揭示关键的生物标志物和动态免疫细胞变化。对来自不同数据集的外周血 mRNA 数据进行分析，并使用 CIBERSORT 对人类免疫细胞亚型表达矩阵进行去卷积。在单细胞和批量转录组水平上进行加权基因共表达网络分析 (WGCNA)，以探索 AMI 的可能生物标志物，特别关注单核细胞及其在细胞间通讯中的作用。对 AMI 患者进行无监督聚类分析，将其分为不同亚型，并采用机器学习方法构建综合诊断模型，以预测早期 AMI 的发生。最后，对从患者采集的外周血样本进行 RT-qPCR，验证了基于机器学习的 mRNA 特征和枢纽生物标志物的临床实用性。该研究确定了早期 AMI 的潜在生物标志物，包括 CLEC2D、TCN2 和 CCR1，并发现单核细胞可能在 AMI 样本中发挥重要作用。差异分析显示，与稳定 CAD 相比，早期 AMI 中 CCR1 和 TCN2 的表达水平升高。机器学习方法表明，在训练集、外部验证集和我院的临床样本中，glmBoost+Enet [alpha=0.9] 模型均具有较高的预测准确性。该研究全面深入地探讨了早期 AMI 发病机制中涉及的潜在生物标志物和免疫细胞群体。所鉴定的生物标志物和构建的综合诊断模型有望预测早期 AMI 的发生，可作为辅助诊断或预测生物标志物。
