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Updated: Jun 25, 2026
In Vitro Differentiation of Human Mesenchymal Stem Cells into Functional Cardiomyocyte-like Cells
Published on: August 9, 2017
Clinical application and immune infiltration landscape of stemness-related genes in heart failure
Wenting Yan1, Yanling Li2, Gang Wang3
1Gansu University of Traditional Chinese Medicine, Lanzhou, China.
Insights
This study identifies seven stemness-related genes to predict heart failure (HF) risk. The findings offer potential for improved HF diagnosis and treatment strategies.
Area of Science:
- Biomedical research
- Genomics
- Cardiovascular disease
Background:
- Heart failure (HF) is a major global health concern.
- The role of stemness in cardiac function and HF remains largely unexplored.
- This study investigates stemness-related biomarkers for HF prediction and immune infiltration.
Purpose of the Study:
- To identify novel stemness-related biomarkers for heart failure (HF).
- To develop a predictive model for HF risk stratification.
- To characterize immune cell infiltration patterns in HF.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) datasets for training and validation.
- Employed machine learning algorithms for feature selection.
- Constructed nomogram models and performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Variation Analysis (GSVA), and Gene Set Enrichment Analysis (GSEA).
Main Results:
- Identified seven key genes (SMOC2, LUM, FNDC1, SCUBE2, CD163, BLM, S1PR3) for HF prediction.
- The nomogram demonstrated robust predictive performance.
- Key genes are linked to ageing and inflammation; enriched immune pathways were observed, highlighting adaptive immunity's role in HF.
Conclusions:
- A clinically significant stemness-related signature for HF risk prediction was identified.
- This signature may enhance early HF diagnosis and risk stratification.
- Potential for novel therapeutic strategies for HF patients.
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.
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01:01Regulation of Hematopoietic Stem Cells
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01:29Heart Failure II: Pathophysiology