A novel machine learning-derived four-gene signature predicts STEMI and post-STEMI heart failure

Jialu Yao; Yujia Zhou; Zhichao Yao; Ye Meng; Wangjianfei Yu; Xinyu Yang; Dayong Zhou; Xiaoqin Yang; Yafeng Zhou

doi:10.17305/bb.2023.9629

Authors

Jialu Yao 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
Yujia Zhou 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
Zhichao Yao Department of Vascular Surgery, Gusu School of Nanjing Medical University, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital (HQ), Suzhou, Jiangsu Province, China
Ye Meng 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
Wangjianfei Yu 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
Xinyu Yang 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
Dayong Zhou Department of Vascular Surgery, Gusu School of Nanjing Medical University, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital (HQ), Suzhou, Jiangsu Province, China
Xiaoqin Yang 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
Yafeng Zhou 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

DOI:

https://doi.org/10.17305/bb.2023.9629

Keywords:

ST-elevation myocardial infarction, heart failure, monocyte, machine learning, prediction model

Abstract

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.