Clinical application and immune infiltration landscape of stemness‐related genes in heart failure

Abstract

AbstractBackgroundHeart failure (HF) is the leading cause of morbidity and mortality worldwide. Stemness refers to the self‐renewal and differentiation ability of cells. However, little is known about the heart's stemness properties. Thus, the current study aims to identify putative stemness‐related biomarkers to construct a viable prediction model of HF and characterize the immune infiltration features of HF.MethodsHF datasets from the Gene Expression Omnibus (GEO) database were adopted as the training and validation cohorts while stemness‐related genes were obtained from GeneCards and previously published papers. Feature selection was performed using two machine learning algorithms. Nomogram models were then constructed to predict HF risk based on the selected key genes. Moreover, the biological functions of the key genes were evaluated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) pathway analyses, and gene set variation analysis (GSVA) and enrichment analysis (GSEA) were performed between the high‐ and low‐risk groups. The immune infiltration landscape in HF was investigated, and the interaction network of key genes was analysed to predict potential targets and molecular mechanisms.ResultsSeven key genes, namely SMOC2, LUM, FNDC1, SCUBE2, CD163, BLM and S1PR3, were included in the proposed nomogram. This nomogram showed good predictive performance for HF diagnosis in the training and validation sets. GO and KEGG analyses revealed that the key genes were primarily associated with ageing, inflammatory processes and DNA oxidation. GSEA and GSVA identified various inflammatory and immune signalling pathways that were enriched between the high‐ and low‐risk groups. The infiltration of 15 immune cell subsets suggests that adaptive immunity has an important role in HF.ConclusionsOur study identified a clinically significant stemness‐related signature for predicting HF risk, with the potential to improve early disease diagnosis, optimize risk stratification and provide new strategies for treating patients with HF.