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

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 microRNA (miRNA) expression profiles in patients with AA to identify biological pathways influenced by miRNAs and potential therapeutic targets.

A total of 50 patients with AA were categorized as severe or mild on the basis of Severity of Alopecia Tool (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 (5 severe AA, 5 mild AA, and 10 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 real-time polymerase chain reaction (RT-PCR). Machine learning was used to classify patients on the basis of their miRNA profiles, and pathway enrichment analysis and drug targeting were conducted to explore therapeutic opportunities.

In total, 19 miRNAs were significantly downregulated in AA, with 9 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 non-segmental vitiligo and AD. Pathway enrichment analysis highlighted immune-related pathways, including the interferon-gamma and Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathways. Drug repositioning identified kinase inhibitors showing the most significant promise for reversing miRNA dysregulation.

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.