{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:30:48Z","timestamp":1768206648365,"version":"3.49.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Mol. Biosci."],"abstract":"\n 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\u2013STAT 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\u2013granzyme A (GZMA), interleukin-2 receptor\n \n \n \n \u03b2<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n (IL2RB) and\n \n \n \n \u03b3<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n (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\u2013aligned 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\u2013query 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\u2013EOMES\u2013GZMA 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\n \n \n \n \n \n EOMES<\/mml:mtext>\n <\/mml:mrow>\n \n +<\/mml:mo>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n \n \n \n \n \n CD<\/mml:mtext>\n 8<\/mml:mn>\n <\/mml:mrow>\n \n +<\/mml:mo>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n T-cell GZMA\u2013IL2RB\/IL2RG cytotoxic loop driving JAK\u2013STAT hyperactivation in AA. Drug\u2013gene network analysis linked these targets to JAK inhibitors and cyclosporine. AGA showed no comparable JAK\u2013STAT 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.\n <\/jats:p>","DOI":"10.3389\/fmolb.2025.1753206","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T05:40:10Z","timestamp":1768196410000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["JAK-centric explainable few-shot gene-expression diagnosis framework for alopecia via MultiPLIER priors and relation-style set-to-set comparison"],"prefix":"10.3389","volume":"12","author":[{"given":"Nanlan","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Ran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinrong","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfei","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shulei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1093\/intimm\/dxz039","article-title":"Pathomechanisms of immune-mediated alopecia","volume":"31","author":"Anzai","year":"2019","journal-title":"Int. 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