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MicroRNAs in Heart Failure Pathogenesis and Progression: Mechanistic Control, Biomarker Potential, and Translational Perspectives

Heart failure (HF) remains a leading cause of morbidity and mortality worldwide and is driven by complex, interconnected pathophysiological processes, including maladaptive cardiac remodeling, fibrosis, hypertrophy, metabolic dysregulation, and cardiomyocyte loss. MicroRNAs (miRNAs), small non-coding RNAs that act as key post-transcriptional regulators of gene expression, have emerged as important coordinators of these processes across cardiomyocytes and non-myocyte cardiac cell populations. In addition to altered expression patterns, accumulating evidence indicates that miRNA activity is dynamically influenced by regulated biogenesis, maturation, and context-dependent mechanisms of action. Through reversible translational repression and longer-term mRNA destabiliza-tion, miRNAs support adaptive responses to acute cardiac stress, whereas their persistent dysregulation contributes to remodeling pathways that promote HF progression. This comprehensive narrative review provides an integrative overview of current knowledge on the role of miRNA networks in shaping the molecular heterogeneity of heart failure across disease stages, phenotypes, and cardiac cell types. Drawing on a broad body of experimental and clinical literature, we discuss advances in understanding miRNA biogenesis, post-transcriptional control, and cell-specific effects, while highlighting conceptual developments rather than applying systematic selection criteria. We further examine the translational and clinical implications of miRNA biology, critically considering the progress of miRNA-based therapeutics alongside the biological and practical challenges that continue to limit their widespread clinical implementation. In parallel, we explore the emerging potential of circulating miRNAs as minimally invasive biomarkers that reflect up-stream regulatory stress at the level of RNA processing and post-transcriptional regulation. Finally, we address the growing application of artificial intelligence and machine learning approaches to high-dimensional miRNA datasets, which enable integrative analyses across clinical, imaging, and multi-omics domains and support biomarker discovery, patient strat-ification, and prediction of therapeutic response. Collectively, miRNA biology, supported by systems-level and AI-driven analytical frameworks, offers a unifying perspective for understanding, classifying, and monitoring cardiac remodeling in heart failure.