Identification of candidate genes in atopic dermatitis based on bioinformatic methods.

BACKGROUND: Atopic dermatitis (AD) is a chronic or relapsing inflammatory disorder of the skin that frequently precedes asthma and allergic disorders. This study aimed to identify candidate genes related to AD using bioinformatic methods.

METHODS: The microarray data of GSE32924, including 12 nonlesional AD (ANL) and 13 lesional AD (AL) skin samples obtained from 14 patients with AD as well as eight other normal human skin samples, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened in ANL and AL skin samples compared with normal controls, followed by gene ontology (GO) and pathway enrichment analyses. Furthermore, the selected overlapping DEGs were analyzed to identify co-expressed genes, and a co-expression network was established. GeneCodis database was selected for functional annotation of the differentially co-expressed genes and a regulatory network was constructed.

RESULTS: Compared with normal controls, 438 DEGs were identified in ANL skin samples and were mainly enriched in two pathways and the GO terms associated with epidermis development. Besides, 779 DEGs were identified in AL skin samples and were mainly enriched in four pathways and GO terms associated with immune response. Stimulated by retinoic acid 13 (STRA13), presenilin enhancer gamma secretase subunit (PSENEN), and nucleosome assembly protein 1-like 2 (NAP1L2) were significantly enriched by integration analysis of the co-expression and regulatory network.

CONCLUSION: Genes STRA13, PSENEN, and NAP1L2 were presumed to play critical roles in AD and they may serve as potential targets for the treatment of AD.