Abstract
Alopecia areata (AA) and androgenetic alopecia (AGA) both present with hair loss but require different therapies, and reliable biomarkers to guide treatment remain lacking. We integrated bulk and single-cell RNA-seq to compare JAK-STAT signaling in AA versus AGA. In AA, 257 immune-enriched differentially expressed genes (DEGs) were identified; WGCNA and consensus machine learning (LASSO, SVM-RFE, random forest) yielded six candidate hub genes, and external validation narrowed these to four key genes-granzyme A (GZMA), interleukin-2 receptor β (IL2RB) and γ (IL2RG) chains, and eomesodermin (EOMES). Building on these biology-anchored features, we introduced an interpretable few-shot deep learning classifier as an explainable AI alternative to a nomogram: bulk expression profiles are projected onto pathway/cell-type-aligned MultiPLIER latent variables (a frozen prior), the latent channels are re-weighted via element-wise multiplication with the expression levels of the key hub genes, and a Relation-style set-to-set comparator then aggregates support-query similarities (Hadamard mapping + permutation-invariant aggregation) before a shallow head predicts AA versus control. This prior-informed approach enables robust discrimination under limited sample conditions while retaining mechanistic interpretability, thereby exemplifying a next-generation XAI solution for small-cohort genomic diagnosis. Cross-database functional annotation and wet-lab validation (RT-qPCR and Western blot) in independent AA/AGA/healthy scalp samples confirmed that the IL2RB/IL2RG-EOMES-GZMA axis is selectively activated at both mRNA and protein levels in AA. Single-cell analysis localized GZMA to cytotoxic T cells and IL2RG to proliferating lymphocytes, outlining an EOMES+ CD8+ T-cell GZMA-IL2RB/IL2RG cytotoxic loop driving JAK-STAT hyperactivation in AA. Drug-gene network analysis linked these targets to JAK inhibitors and cyclosporine. AGA showed no comparable JAK-STAT perturbation, consistent with its androgen-centric biology. In summary, this four-gene loop provides a non-invasive AA biomarker and a tractable target for precision JAK blockade, while the proposed few-shot framework offers a general, prior-driven alternative to nomograms for transcriptomic diagnosis in small cohorts, illustrating an XAI-driven diagnostic approach for precision medicine.