Identification of Potential Targets of Stress Cardiomyopathy by a Machine Learning Algorithm

Abstract

Background: Stress cardiomyopathy (SCM) is a reversible, self-limiting condition that manifests as left ventricular insufficiency. The incidence of stress cardiomyopathy has increased because of increasing mental and social stress, but the exact pathophysiological mechanisms remain unclear. Methods: To elucidate the critical molecules in the pathogenesis of SCM and the functional changes that they mediate, we downloaded data for a healthy control group and stress cardiomyopathy (SCM) group from the Gene Expression Omnibus database, performed differential analysis, and analyzed the results of GO and KEGG enrichment analysis to describe SCM-associated genes and functions. Lasso, random forest, SVM-RFM, and Friends analysis were used to screen hub genes; CIBERSORT and MCPcounter were used to explore the relationship between SCM and immunity; and an animal model of SCM was constructed to conduct bidirectional verification of the obtained results. Results: In total, 21 samples (6 healthy, 15 SCM) were used in this study. Overall, 39 DEGs (absolute fold change ≥ 1; P < 0.05), including 23 upregulated and 16 downregulated genes in SCM, were extracted. Three common hub genes (PLAT, SEMA6B, and CRP) were finally screened. We further confirmed that functional changes in SCM were concentrated in immunity and coagulation functions. Conclusion: Three key genes (PLAT, SEMA6B, and CRP) in SCM were identified by machine learning, and the major functional changes leading to SCM, and relationships of SCM with immunity, were identified.