CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration

TL;DR

This study identifies CYLD as a key gene involved in the transition from heart attack to heart failure, using advanced data analysis of gene expression patterns.

Contribution

The novel contribution is identifying CYLD as a critical regulator in the transition from myocardial infarction to heart failure through multi-omics integration.

Findings

413 differentially expressed genes were identified between MI and HF.

CYLD was found to have the strongest correlation with the transition from MI to HF.

Machine learning confirmed CYLD's predictive value in this transition.

Abstract

Heart 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.

The 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.

Our 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.

In 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.

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