文献类型：期刊文献

英文题名：Clinical application and immune infiltration landscape of stemness-related genes in heart failure

作者：Yan, Wenting[1];Li, Yanling[2];Wang, Gang[3];Huang, Yuan[1];Xie, Ping[2]

第一作者：Yan, Wenting

通信作者：Xie, P[1]

机构：[1]Gansu Univ Tradit Chinese Med, Lanzhou, Peoples R China;[2]Gansu Prov Hosp, Dept Cardiol, 204 Donggang West Rd, Lanzhou 730000, Peoples R China;[3]Lanzhou Univ, Clin Med Coll 1, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Hosp, Dept Cardiol, 204 Donggang West Rd, Lanzhou 730000, Peoples R China.

年份：2024

外文期刊名：ESC HEART FAILURE

收录：;Scopus(收录号：2-s2.0-85204135319);WOS:【SCI-EXPANDED(收录号:WOS:001313299500001)】；

基金：This work was supported by the Natural Science Foundation of Gansu Province (No: 22JR5RA665).

语种：英文

外文关键词：HF; Stemness; Diagnosis; Nomogram; Immune infiltration

摘要：Background: Heart 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. Methods: HF 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. Results: Seven 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. Conclusions: Our 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.