[Identify the characteristic genes of dilated cardiomyopathy and immune cell infiltration based on bioinformatics and machine learning].

Abstract

Objective To identify the characteristic genes and immune infiltration in dilated cardiomyopathy (DCM) by bioinformatic analysis. Methods We identified differentially expressed genes (DEG) on two DCM gene expression data sets, and performed gene ontology (GO), disease ontology (DO), and gene set enrichment analysis (GSEA) functional enrichment to obtain potential pathways. Two machine learning algorithms including support vector machine recursive feature elimination (SVM-RFE) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm were used to determine diagnostic markers. Finally, we used the cell type analysis tool CIBERSORT for immune cell infiltration analysis. Results A total of 51 DEGs were finally identified. Thioredoxin interacting protein (TXNIP), crystallin Mu (CRYM), heat shock 70kDa protein 1-like (HSPA1L), and eukaryotic elongation factor 1A-1 (EEF1A1) were considered candidate diagnostic markers. Enrichment analysis focused on features including cardiac processes, outer membranes of mitochondria and organelles, ubiquitin-like protein ligase, natural killer cell-mediated cytotoxicity, Th1, and Th2 cell differentiation, T cell receptor signaling pathways, and Th17 cell differentiation. Immune cell infiltration found naive B cells, neutrophils, and γT cells may be involved in the pathogenesis of DCM. Besides, neutrophils, T follicular helper cells, and M1 macrophages were highly correlated with four characteristic genes. Conclusion The four characteristic genes identified by machine learning, TXNIP, CRYM, HSPA1L, and EEF1A1, show potentially close relation to DCM. At the same time, immune cell infiltration analysis can better showcase the pathophysiological process of DCM.