The Features of Shared Genes among Transcriptomes Probed in Atopic Dermatitis, Psoriasis, and Inflammatory Acne: S100A9 Selection as the Target Gene

Wei Wang; Sungbo Hwang; Daeui Park; Yong Doo Park

doi:10.2174/0109298665290166240426072642

The Features of Shared Genes among Transcriptomes Probed in Atopic Dermatitis, Psoriasis, and Inflammatory Acne: S100A9 Selection as the Target Gene

Protein Pept Lett. 2024 May 17. doi: 10.2174/0109298665290166240426072642. Online ahead of print.

Authors

Wei Wang¹, Sungbo Hwang², Daeui Park², Yong Doo Park^{1

3

4}

Affiliations

¹ College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo, P.R. China.
² Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon 34114, Korea.
³ Zhejiang Provincial Key Laboratory of Applied Enzymology, Yangtze Delta Region Institute of Tsinghua University, Jiaxing 314006, P.R. China.
⁴ Skin Diseases Research Center, Yangtze Delta Region Institute of Tsinghua University, Jiaxing, P.R. China.

PMID: 38766834
DOI: 10.2174/0109298665290166240426072642

Abstract

Background: Atopic dermatitis (AD), psoriasis (PS), and inflammatory acne (IA) are well-known as inflammatory skin diseases. Studies of the transcriptome with altered expression levels have reported a large number of dysregulated genes and gene clusters, particularly those involved in inflammatory skin diseases.

Objective: To identify genes commonly shared in AD, PS, and IA that are potential therapeutic targets, we have identified consistently dysregulated genes and disease modules that overlap with AD, PS, and IA.

Methods: Microarray data from AD, PS, and IA patients were downloaded from Gene Expression Omnibus (GEO), and identification of differentially expressed genes from microarrays of AD, PS, and IA was conducted. Subsequently, gene ontology and gene set enrichment analysis, detection of disease modules with known disease-associated genes, construction of the protein-protein interaction (PPI) network, and PPI sub-mapping analysis of shared genes were performed. Finally, the computational docking simulations between the selected target gene and inhibitors were conducted.

Results: We identified 50 shared genes (36 up-regulated and 14 down-regulated) and disease modules for each disease. Among the shared genes, 20 common genes in PPI network were detected such as LCK, DLGAP5, SELL, CEP55, CDC20, RRM2, S100A7, S100A9, MCM10, AURKA, CCNB1, CHEK1, BTC, IL1F7, AGTR1, HABP4, SERPINB13, RPS6KA4, GZMB, and TRIP13. Finally, S100A9 was selected as the target gene for therapeutics. Docking simulations between S100A9 and known inhibitors indicated several key binding residues, and based on this result, we suggested several cannabinoids such as WIN-55212-2, JZL184, GP1a, Nabilone, Ajulemic acid, and JWH-122 could be potential candidates for a clinical study for AD, PS, and IA via inhibition of S100A9-related pathway.

Conclusion: Overall, our approach may become an effective strategy for discovering new disease candidate genes for inflammatory skin diseases with a reevaluation of clinical data.

Keywords: Atopic dermatitis; S100A9.; cDNA microarray; inflammatory acne; psoriasis.