Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms.

ABSTRACT:

Objectives

Alopecia areata (AA) is an autoimmune-related non-cicatricial alopecia, with complete alopecia (AT) or generalized alopecia (AU) as severe forms of AA. However, there are limitations in early identification of AA, and intervention of AA patients who may progress to severe AA will help to improve the incidence rate and prognosis of severe AA.

Methods

We obtained two AA-related datasets from the gene expression omnibus database, identified the differentially expressed genes (DEGs), and identified the module genes most related to severe AA through weighted gene co-expression network analysis. Functional enrichment analysis, construction of a protein-protein interaction network and competing endogenous RNA network, and immune cell infiltration analysis were performed

SUBMITTER: Xiong J 

PROVIDER: S-EPMC10268596 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature