Circulating MicroRNA Signatures in Severe Alopecia Areata: Diagnostic Discrimination, Pathway Analysis, and Therapeutic Implications

Introduction: Alopecia areata (AA) is an autoimmune disorder characterized by non-scarring hair loss due to immune dysregulation. Despite advances, its precise molecular mechanisms remain unclear. This study investigates plasma miRNA expression profiles in patients with AA to identify biological pathways influenced by miRNAs and potential therapeutic targets.Methods: Fifty patients with AA were categorized as severe or mild based on SALT scores. Plasma miRNA levels were compared with those of healthy controls and individuals with other immune-mediated skin diseases. In the discovery phase, 754 miRNAs were analyzed in 20 participants (five severe AA, five mild AA, and ten controls). Key miRNAs identified were then validated in a second cohort of 90 participants, including patients with AA, non-segmental vitiligo, atopic dermatitis (AD), psoriasis (PsO), and healthy controls, using RT-PCR. Machine learning was used to classify patients based on their miRNA profiles, and pathway enrichment analysis and drug targeting were conducted to explore therapeutic opportunities.Results: Nineteen miRNAs were significantly downregulated in AA, with nine technically and clinically validated for both mild and severe forms. The top four miRNAs with the highest classification potential were miR-130b-3p, miR-296-5p, miR-424-5p, and miR-195-5p. Distinct upregulation patterns were identified in vitiligo, AD, and PsO. Machine learning models showed vital classification accuracy for AA (AUC = 0.94) and PsO (AUC = 0.88), with moderate performance for vitiligo and AD. Pathway enrichment analysis highlighted immune-related pathways, including the Interferon-gamma and JAK/STAT signaling pathways. Drug repositioning identified kinase inhibitors showing the most significant promise for reversing miRNA dysregulation.Conclusion: This study identifies distinct plasma miRNA profiles in AA, with potential applications for both diagnosis and therapy. Machine learning validated its solid predictive accuracy, and pathway analysis highlighted key immune pathways in AA. These findings should be interpreted as exploratory and hypothesis-generating, pending further functional validation of candidate miRNAs.