{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yan W"],"funding":["Natural Science Foundation of Gansu Province"],"pagination":["250-270"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11769652"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(1)"],"pubmed_abstract":["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. Moreov"],"journal":["ESC heart failure"],"pubmed_title":["Clinical application and immune infiltration landscape of stemness-related genes in heart failure."],"pmcid":["PMC11769652"],"funding_grant_id":["22JR5RA665"],"pubmed_authors":["Li Y","Huang Y","Wang G","Xie P","Yan W"],"additional_accession":[]},"is_claimable":false,"name":"Clinical application and immune infiltration landscape of stemness-related genes in heart failure.","description":"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. Moreov","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2026-07-15T11:05:39.08Z","creation":"2025-04-04T23:11:36.509Z"},"accession":"S-EPMC11769652","cross_references":{"pubmed":["39275894"],"doi":["10.1002/ehf2.15055"]}}