Li Ming, Chen Fuli, Zhang Yaling, Xiong Yan, Li Qiyong, Huang Hui
Department of Cardiology, Eastern Hospital, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Front Physiol. 2020 Jun 3;11:483. doi: 10.3389/fphys.2020.00483. eCollection 2020.
Myocardial infarction (MI) is a type of serious heart attack in which the blood flow to the heart is suddenly interrupted, resulting in injury to the heart muscles due to a lack of oxygen supply. Although clinical diagnosis methods can be used to identify the occurrence of MI, using the changes of molecular markers or characteristic molecules in blood to characterize the early phase and later trend of MI will help us choose a more reasonable treatment plan. Previously, comparative transcriptome studies focused on finding differentially expressed genes between MI patients and healthy people. However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. Subsequently, functional enrichment analyses followed by protein-protein interaction analysis on 134 genes identified several hub genes (IL1R1, TLR2, and TLR4) associated with progression of MI, which can be used as new diagnostic molecules for MI.
心肌梗死（MI）是一种严重的心脏病发作类型，其中流向心脏的血液突然中断，由于缺氧供应导致心肌损伤。虽然临床诊断方法可用于识别MI的发生，但利用血液中分子标志物或特征分子的变化来表征MI的早期阶段和后期趋势将有助于我们选择更合理的治疗方案。以前，比较转录组研究侧重于寻找MI患者和健康人之间差异表达的基因。然而，在MI不同阶段改变的标志性分子尚未得到充分挖掘。我们开发了一套整合多种机器学习算法的计算方法，包括蒙特卡罗特征选择（MCFS）、增量特征选择（IFS）和支持向量机（SVM），以识别MI不同阶段的基因表达特征。确定了134个基因作为构建最佳SVM分类器以区分急性MI和MI后阶段的特征。随后，对134个基因进行功能富集分析，然后进行蛋白质-蛋白质相互作用分析，确定了几个与MI进展相关的枢纽基因（IL1R1、TLR2和TLR4），它们可作为MI的新诊断分子。
