ArticleFrontiers in immunology2024
Identification and validation of aging-related genes in heart failure based on multiple machine learning algorithms.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Microvascular Health as a Key Determinant of Organismal Aging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Cellular and molecular mechanisms underlying cardiovascular aging.Cellular & molecular biology letters · 2025Review
- Plin2 Coordinates Immune and Metabolic Reprogramming in Lacrimal Gland Aging.Investigative ophthalmology & visual science · 2025Article
- Epigenetic pharmacology in aging: from mechanisms to therapies for age-related disorders.Frontiers in pharmacology · 2025Review
- DPCDI: an artificial intelligent-derived indicator interpreting the diagnostic, stratification, and therapeutic implications of druggability programmed cell death in heart failure.Frontiers in genetics · 2025Article
- Commentary: Immune cell infiltration and prognostic index in cervical cancer: insights from metabolism-related differential genes.Frontiers in immunology · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
Background: In the face of continued growth in the elderly population, the need to understand and combat age-related cardiac decline becomes even more urgent, requiring us to uncover new pathological and cardioprotective pathways. Methods: We obtained the aging-related genes of heart failure through WGCNA and CellAge database. We elucidated the biological functions and signaling pathways involved in heart failure and aging through GO and KEGG enrichment analysis. We used three machine learning algorithms: LASSO, RF and SVM-RFE to further screen the aging-related genes of heart failure, and fitted and verified them through a variety of machine learning algorithms. We searched for drugs to treat age-related heart failure through the DSigDB database. Finally, We use CIBERSORT to complete immune infiltration analysis of aging samples. Results: We obtained 57 up-regulated and 195 down-regulated aging-related genes in heart failure through WGCNA and CellAge databases. GO and KEGG enrichment analysis showed that aging-related genes are mainly involved in mechanisms such as Cellular senescence and Cell cycle. We further screened aging-related genes through machine learning and obtained 14 key genes. We verified the results on the test set and 2 external validation sets using 15 machine learning algorithm models and 207 combinations, and the highest accuracy was 0.911. Through screening of the DSigDB database, we believe that rimonabant and lovastatin have the potential to delay aging and protect the heart. The results of immune infiltration analysis showed that there were significant differences between Macrophages M2 and T cells CD8 in aging myocardium. Conclusion: We identified aging signature genes and potential therapeutic drugs for heart failure through bioinformatics and multiple machine learning algorithms, providing new ideas for studying the mechanism and treatment of age-related cardiac decline.
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