Background: The purpose of this study was to identify possible diagnostic indicators for heart failure (HF) and to investigate the function of immune cell infiltration in this pathophysiology.
Methods: HF datasets from the Gene Expression Metascape database were utilized. R software was used to the identify differentially-expressed genes (DEGs) and perform functional correlation analysis. Least absolute shrinkage and selection operator (LASSO) and Boruta algorithms elimination algorithms were then employed to screen and validate the HF diagnostic variables. Finally, Single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to assess immune cell infiltration in HF tissues, and the Spearman association between gene expression and immune cell concentration was investigated.
Results: A total of 239 DEGs were screened in this study. SERPINA3 (area under the curve, AUC =0.958), FCN3 (AUC =0.972), FREM1 (AUC =0.954), and MNS1 (AUC =0.948) were identified as diagnostic factors of HF. The gene set differentiation analysis (GSVA) (R package "GSVA") results showed that the high expression of FREM1 and MNS1 genes was involved in bile acid, fatty acid, and heme metabolism, suggesting that the core gene affects the progression of HF by regulating metabolism. Meanwhile, the high expression of FCN3 and SERPINA3 was related to xenobiotic metabolism, inflammatory response, and adipogenesis.
Conclusions: Given the importance of immune cell infiltration in the genesis and progression of HF, SERPINA3, FCN3, FREM1, and MNS1 may be used as diagnostic variables for HF.
Keywords: Boruta algorithms; Gene Expression Omnibus (GEO); Heart failure (HF); immune cells; least absolute shrinkage and selection operator (LASSO).
2022 Journal of Thoracic Disease. All rights reserved.