Differentially Expressed Genes in Atopic Dermatitis: Bioinformatics analysis of pooled microarray gene expression datasets in Gene Expression Omnibus

Abstract

Introduction: Atopic dermatitis (AD) is a chronic and refractory inflammatory skin disease characterized by relapsing eczematous and pruritic skin lesions. Understanding the specific gene expression patterns associated with AD is crucial for advancing diagnosis and targeted treatment development. Using bioinformatics methods, candidate genes and biological pathways involved in AD pathogenesis were identified based on gene expression profiles in the Gene Expression Omnibus (GEO) database. Materials and Methods: A comprehensive analysis of four pooled transcriptomic datasets obtained from the Gene Expression Omnibus (GEO) database were conducted. Differential gene expression analysis was performed using the GEO2R. The differentially expressed genes (DEGs) between lesion skin of AD patients and normal skin of individuals were analyzed using the Gene Ontology (GO) term enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network to explore the functional roles of these genes. Results: Among the patient-level gene expression datasets, we identified 133 shared DEGs, consisting of 48 upregulated genes and 85 downregulated genes. GO analyses revealed these DEGs to be significantly enriched in biological processes including inflammatory responses, cytokine-mediated signaling pathway. These DEGs were also enriched in the KEGG pathway, including viral protein interaction with cytokine and cytokine receptor, C-type lectin receptor signaling pathway, cytokine-cytokine receptor interaction, JAK-STAT signaling pathway, and Adipocytokine signaling pathway. Conclusion: By comparing with other studies using the same method, we found that in addition to the already confirmed pathways such as inflammatory response, different studies have found changes in different hub genes and metabolic pathways, which prompts us to develop individualized treatments for AD.

Keywords

• atopic dermatitis
• GEO database
• bioinformatics
• study comparison