Abstract
Studies have demonstrated that immune cell activation and their infiltration in the myocardium can have adverse effects on the heart, contributing to the pathogenesis of heart failure (HF). The purpose of this study is used by bioinformatics analysis to determine the potential diagnostic markers of heart failure and establish an applicable model to predict the association between heart failure and immune cell infiltration. Firstly, gene expression profiles of dilated heart disease GSE3585 and GSE120895 were obtained in Gene Expression Omnibus (GEO) database. This study then selected differentially expressed genes (DEGs) in 54 patients with HF and 13 healthy controls. In this study, biomarkers were identified using Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). Additionally, we evaluated the prognostic discrimination performance by the receiver operating characteristic (ROC) curve. Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used for analyzing immune cell infiltration in HF tissues. Lastly, immune biomarkers were correlated with each other. After 24 DEGs were analyzed using a combinatorial model of LASSO regression and SVM-RFE analysis, four key genes were obtained, namely NSG1, NPPB, PHLDA1, and SERPINE2.The area under the curve (AUC) of these four genes were greater than 0.8. Subsequently, using CIBERPORT, we also found that compared with normal people, the proportion of M1 macrophages and activated mast cells in heart failure tissues decreased. In addition, correlation analysis showed that NPPB, PHLDA1 and SERPINE2 were associated with immune cell infiltration. NSG1, NPPB, PHLDA1 and SERPINE2 were identified as potential biomarkers of heart failure. It reveals the comprehensive role of relevant central genes in immune infiltration, which provides a new research idea for the treatment and early detection in heart failure.
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