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

• Published in: Biomolecules & Biomedicine Vol. 24; no. 2; pp. 423 - 433
• Main Authors: Yao, Jialu, Zhou, Yujia, Yao, Zhichao, Meng, Ye, Yu, Wangjianfei, Yang, Xinyu, Zhou, Dayong, Yang, Xiaoqin, Zhou, Yafeng
• Format: Journal Article
• Language: English
• Published: Bosnia and Herzegovina Association of Basic Medical Sciences 11.03.2024; Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina
• Subjects: Analysis; Cardiovascular Diseases - complications; Care and treatment; Complications and side effects; Coronary Artery Disease - complications; Coronary heart disease; Diagnosis; Gene expression; Genetic aspects; Heart failure; Heart Failure - complications; Histocompatibility antigens; HLA histocompatibility antigens; Humans; Machine Learning; Methods; monocyte; prediction model; ST Elevation Myocardial Infarction - complications; ST-elevation myocardial infarction; Testing; China
• ISSN: 2831-0896; 2831-090X; 2831-090X
• 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.