Abstract
INTRODUCTION: Alopecia areata (AA) is an immune-mediated inflammatory skin disease that targets hair follicles. Current research yields varied lists of differentially expressed genes (DEGs). A meta-analytic approach is essential to consolidate these findings into a consistent tissue signature. This study aimed to perform a meta-analysis of gene expression datasets to establish a comprehensive molecular signature of AA lesional scalp. METHODS: We conducted a meta-analysis of transcriptomic data from human studies on lesional skin gene expression in AA. Reanalyzing 132 samples (82 patients with AA and 50 controls) from five Gene Expression Omnibus (GEO) datasets (GSE68801, GSE45512, GSE80342, GSE58573, GSE74761), we employed an effect size approach within a random-effects model to identify unique and shared DEGs and enriched biological pathways. The protocol is registered in PROSPERO (CRD42024559847). RESULTS: The meta-analysis identified 5109 DEGs, with 2710 upregulated and 2399 downregulated genes, significantly more than the 120 DEGs shared across the five studies. Consistently expressed genes included CXCL9, CCL18, CXCL10, CD8A, and GZMB (FDR < 0.05). The analysis highlighted chemokines/receptors (CCL13, CCR1, XCL1) and markers of cytotoxic T lymphocytes (GZMA, GZMH, GZMK) and NK cells (NKG2A, NKG2D). Downregulated genes involved type I (KRT31-35, KRT38) and type II (KRT81-86) keratins and proteins crucial for hair follicle structure and function (PADI3, GPRC5D, DSG4, FGF18). Functional analysis showed enrichment in Th1, Th2, and Th17 pathways, particularly through JAK-STAT signaling (p < 0.01). CONCLUSION: This core transcriptome of AA lesions provides new insights into the disease's pathogenesis and identifies potential targets for treatment.