SkinDB: A Curated Resource for Dermatological Data Warehousing and Bioinformatics Exploration

ABSTRACT

Dermatology lacks a centralized analysis‐ready transcriptomic resource, leaving publicly available datasets fragmented and difficult to reuse without bioinformatics expertise. To address this gap, we developed SkinDB, an open‐access code‐free resource that brings together 220 curated human microarray datasets comprising 11,283 samples across six major skin diseases: systemic lupus erythematosus, atopic dermatitis, scleroderma, psoriasis, dermatomyositis, and vitiligo. Five analytical modules provide 18 functions spanning differential expression, expression regulation, pathway analysis, machine learning, and gene‐set analysis, with downloadable figures and result tables. The platform supports both dataset‐specific exploration and recurrence‐based cross‐dataset summaries while retaining cohort context. In a psoriasis demonstration, MKI67 was highest in lesional skin and correlated with CDC20 in GSE13355. Across nine psoriasis datasets, MKI67‐high samples showed recurrent transcription‐factor changes; across all 18 psoriasis datasets, consensus analysis identified 14 pathways associated with high MKI67 expression, led by cell cycle, DNA replication, and DNA repair. By converting dispersed dermatological transcriptomes into an accessible analytical resource, SkinDB supports cross‐cohort exploration, biomarker research, and hypothesis generation.