CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration
IntroductionHeart failure (HF) is the most common complication following myocardial infarction (MI) and frequently occurs during the postinfarction recovery phase. Despite the well-established association between HF and MI, the underlying molecular mechanisms driving their transition remain poorly u...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1592985/full |
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| author | Jingya Xu Jingya Xu Zhonghua Dong Zhaodong Li Xuan Wang Xuan Wang |
| author_facet | Jingya Xu Jingya Xu Zhonghua Dong Zhaodong Li Xuan Wang Xuan Wang |
| author_sort | Jingya Xu |
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| description | IntroductionHeart failure (HF) is the most common complication following myocardial infarction (MI) and frequently occurs during the postinfarction recovery phase. Despite the well-established association between HF and MI, the underlying molecular mechanisms driving their transition remain poorly understood.MethodsThe aim of this study was to identify key regulatory genes involved in this transition via advanced computational tools. We conducted a comprehensive analysis of differentially expressed genes (DEGs) via Limma software, leveraging five independent datasets retrieved from the Gene Expression Omnibus (GEO) database: GSE59867, GSE62646, GSE168281, GSE267644, and GSE269269. Our multistep analytical pipeline included weighted gene coexpression network analysis (WGCNA) to map interacting genes, machine learning algorithms for robust classification, functional annotation via Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore biological pathways, CIBERSORT correlation analysis linking hub genes with immune cell states, transcriptional regulation profiling of key hubs, and single-cell sequencing to assess the functional relevance of these hubs.ResultsOur findings revealed that 413 DEGs were significantly different between MI and HF. WGCNA identified 98 genes associated with both conditions. Machine learning filtering further prioritized 10 hub genes: GPER1, E2F5, DZIP3, CYLD, ADAMTS2, ZNF366, ST14, SNORD28, LHFPL2, and HIVEP2. These hubs were significantly associated with immune-related processes, suggesting their potential role in the pathogenesis of HF after MI. Single-cell transcriptomic analysis demonstrated that CYLD exhibited the strongest correlation with the transition from MI to HF; using random forest modelling, we validated its predictive value in this context.DiscussionIn conclusion, our study identified CYLD as a critical regulator of the transition from MI to HF. Our findings underscore the potential of hub genes as targets for novel therapeutic interventions aimed at mitigating MI-to-HF progression and improving patient outcomes. |
| format | Article |
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| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj-art-520ae27ea5854c3e861f31f740bceb4f2025-08-20T03:47:19ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-06-011610.3389/fgene.2025.15929851592985CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integrationJingya Xu0Jingya Xu1Zhonghua Dong2Zhaodong Li3Xuan Wang4Xuan Wang5Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, ChinaCollege of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, ChinaDepartment of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, ChinaGuangdong Key Laboratory of Regional Immunity and Diseases, Department of Pathogen Biology, Shenzhen University School of Medicine, Shenzhen, ChinaDepartment of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, ChinaCollege of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, ChinaIntroductionHeart failure (HF) is the most common complication following myocardial infarction (MI) and frequently occurs during the postinfarction recovery phase. Despite the well-established association between HF and MI, the underlying molecular mechanisms driving their transition remain poorly understood.MethodsThe aim of this study was to identify key regulatory genes involved in this transition via advanced computational tools. We conducted a comprehensive analysis of differentially expressed genes (DEGs) via Limma software, leveraging five independent datasets retrieved from the Gene Expression Omnibus (GEO) database: GSE59867, GSE62646, GSE168281, GSE267644, and GSE269269. Our multistep analytical pipeline included weighted gene coexpression network analysis (WGCNA) to map interacting genes, machine learning algorithms for robust classification, functional annotation via Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore biological pathways, CIBERSORT correlation analysis linking hub genes with immune cell states, transcriptional regulation profiling of key hubs, and single-cell sequencing to assess the functional relevance of these hubs.ResultsOur findings revealed that 413 DEGs were significantly different between MI and HF. WGCNA identified 98 genes associated with both conditions. Machine learning filtering further prioritized 10 hub genes: GPER1, E2F5, DZIP3, CYLD, ADAMTS2, ZNF366, ST14, SNORD28, LHFPL2, and HIVEP2. These hubs were significantly associated with immune-related processes, suggesting their potential role in the pathogenesis of HF after MI. Single-cell transcriptomic analysis demonstrated that CYLD exhibited the strongest correlation with the transition from MI to HF; using random forest modelling, we validated its predictive value in this context.DiscussionIn conclusion, our study identified CYLD as a critical regulator of the transition from MI to HF. Our findings underscore the potential of hub genes as targets for novel therapeutic interventions aimed at mitigating MI-to-HF progression and improving patient outcomes.https://www.frontiersin.org/articles/10.3389/fgene.2025.1592985/fullheart failuremyocardial infarctionWGCNAmachine learningsingle-cell sequencing analysishub genes |
| spellingShingle | Jingya Xu Jingya Xu Zhonghua Dong Zhaodong Li Xuan Wang Xuan Wang CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration Frontiers in Genetics heart failure myocardial infarction WGCNA machine learning single-cell sequencing analysis hub genes |
| title | CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration |
| title_full | CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration |
| title_fullStr | CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration |
| title_full_unstemmed | CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration |
| title_short | CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration |
| title_sort | cyld as a key regulator of myocardial infarction to heart failure transition revealed by multi omics integration |
| topic | heart failure myocardial infarction WGCNA machine learning single-cell sequencing analysis hub genes |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1592985/full |
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