ArticleJournal of inflammation research2024
The Role of Epicardial Adipose Tissue-Derived Proteins in Heart Failure with Preserved Ejection Fraction and Atrial Fibrillation: A Bioinformatics Analysis.
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The Heart's Hidden Influencer: Epicardial Adipose Tissue in the Pathogenesis of Heart Failure.Cardiology discovery · 2026Review
- Systematic bibliometric and visualized analysis of global research trends, impact, emerging areas, and hotspots of artificial intelligence in personalized medicine.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Comprehensive bioinformatics analysis of hub genes in ischemic heart failure and atrial fibrillation.Frontiers in cardiovascular medicine · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The accumulation of epicardial adipose tissue (EAT) is associated with cardiometabolic risks and adverse outcomes in heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF). This study aims to identify genes secreted by EAT that contribute to the shared pathogenesis of HFpEF and AF, potentially serving as biomarkers for diagnosis. Methods: Data sets from the GEO database for HFpEF-EAT, HFpEF-heart tissue, AF-EAT, AF-PBMC, and AF-heart tissue were analyzed. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) identified key genes in EAT linked to HFpEF and AF. Functional enrichment and connectivity map analyses explored common pathways and therapeutic targets. Machine learning techniques, including LASSO regression, random forest, and support vector machine, identified shared biomarkers. CIBERSORT was used to assess immune cell infiltration, while gene set enrichment analysis identified pathways related to hub genes. Receiver operating characteristic (ROC) curve analysis and experimental validation assessed the bioinformatics findings. Results: In the HFpEF dataset, 200 key genes were identified by intersecting HFpEF-EAT, HFpEF-heart tissue, WGCNA analyses, and secretory proteins. For AF, 232 related genes were identified through similar methods. Thirteen genes were common between HFpEF and AF, with two central genes, ITPKA and WNT9B, selected as potential biomarkers through machine learning and ROC analysis. Immune cell infiltration and gene set enrichment analysis revealed pathways related to ITPKA/WNT9B. These patterns were confirmed in human samples. Conclusion: This study identified EAT-derived secretory proteins as potential biomarkers for HFpEF and AF, with ITPKA and WNT9B as central hub genes. These findings offer insights into potential diagnostic and therapeutic strategies for HFpEF and AF.
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