Yao Jialu, Zhou Yujia, Yao Zhichao, Meng Ye, Yu Wangjianfei, Yang Xinyu, Zhou Dayong, Yang Xiaoqin, Zhou Yafeng
Department of Cardiology, the First Affiliated Hospital of Soochow University, Suzhou, China; Department of Cardiology, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Institute for Hypertension of Soochow University, Jiangsu Engineering Laboratory of Novel Functional Polymeric Materials of Soochow University, Suzhou, Jiangsu Province, China.
Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China; Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province, China.
Biomol Biomed. 2024 Mar 11;24(2):423-433. doi: 10.17305/bb.2023.9629.
High mortality and morbidity rates associated with ST-elevation myocardial infarction (STEMI) and post-STEMI heart failure (HF) necessitate proper risk stratification for coronary artery disease (CAD). A prediction model that combines specificity and convenience is highly required. This study aimed to design a monocyte-based gene assay for predicting STEMI and post-STEMI HF. A total of 1,956 monocyte expression profiles and corresponding clinical data were integrated from multiple sources. Meta-results were obtained through the weighted gene co-expression network analysis (WGCNA) and differential analysis to identify characteristic genes for STEMI. Machine learning models based on the decision tree (DT), support vector machine (SVM), and random forest (RF) algorithms were trained and validated. Five genes overlapped and were subjected to the model proposal. The discriminative performance of the DT model outperformed the other two methods. The established four-gene panel (HLA-J, CFP, STX11, and NFYC) could discriminate STEMI and HF with an area under the curve (AUC) of 0.86 or above. In the gene set enrichment analysis (GSEA), several cardiac pathogenesis pathways and cardiovascular disorder signatures showed statistically significant, concordant differences between subjects with high and low expression levels of the four-gene panel, affirming the validity of the established model. In conclusion, we have developed and validated a model that offers the hope for accurately predicting the risk of STEMI and HF, leading to optimal risk stratification and personalized management of CAD, thereby improving individual outcomes.
与ST段抬高型心肌梗死（STEMI）及STEMI后心力衰竭（HF）相关的高死亡率和发病率使得对冠状动脉疾病（CAD）进行恰当的风险分层成为必要。非常需要一种兼具特异性和便利性的预测模型。本研究旨在设计一种基于单核细胞的基因检测方法来预测STEMI及STEMI后HF。从多个来源整合了总共1956个单核细胞表达谱及相应的临床数据。通过加权基因共表达网络分析（WGCNA）和差异分析获得荟萃结果，以识别STEMI的特征基因。基于决策树（DT）、支持向量机（SVM）和随机森林（RF）算法的机器学习模型得到训练和验证。五个基因重叠并用于模型构建。DT模型的判别性能优于其他两种方法。所建立的四基因组合（HLA-J、CFP、STX11和NFYC）能够以曲线下面积（AUC）0.86或更高来区分STEMI和HF。在基因集富集分析（GSEA）中，几个心脏发病机制途径和心血管疾病特征在四基因组合高表达和低表达受试者之间显示出具有统计学意义的一致差异，证实了所建立模型的有效性。总之，我们开发并验证了一个模型，该模型为准确预测STEMI和HF风险带来了希望，从而实现CAD的最佳风险分层和个性化管理，进而改善个体预后。
