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

摘要

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 theymediate,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.