A novel predictive model for the recurrence of pediatric alopecia areata by bioinformatics analysis and a single-center prospective study.

ABSTRACT:

Background

Alopecia areata (AA) is a disease featured by recurrent, non-scarring hair loss with a variety of clinical manifestations. The outcome of AA patients varies greatly. When they progress to the subtypes of alopecia totalis (AT) or alopecia universalis (AU), the outcome is unfavorable. Therefore, identifying clinically available biomarkers that predict the risk of AA recurrence could improve the prognosis for AA patients.

Methods

In this study, we conducted weighted gene co-expression network analysis (WGCNA) and functional annotation analysis to identify key genes that correlated to the severity of AA. Then, 80 AA children were enrolled at the Department of Dermatology, Wuhan Children's Hospital between January 2020 to December 2020. Clinical information and serum s

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC10285523 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature