Identification of Effective Diagnostic Biomarkers and Immune Cell Infiltration in Atopic Dermatitis by Comprehensive Bioinformatics Analysis - Li, Chenyang; Lu, Yongping; Han, Xiuping (Frontiers in Molecular Biosciences, 2022)
Background: Atopic dermatitis (AD) is a dermatological disorder characterized by symptoms such as chronically inflamed skin and frequently intolerable itching. Our study aims to identify the diagnostic and therapeutic biomarkers for AD and provide insight into immune mechanisms through bioinformatics analysis. Methods: Three gene expression profiles were obtained for analysis from the GEO database. Differentially expressed genes (DEGs) were segregated using “Batch correction” and “RobustRankAggreg” methods. WGCNA was performed to screen for module genes with AD traits. Common DEGs (co-DEGs) were screened out via combined differential expression analysis and WGCNA. Functional enrichment analysis was performed for co-DEGs using GO and KEGG, followed by PPI network analysis. Candidate hub genes were identified using the “cytoHubba” plugin in Cytoscape, and their value for AD diagnosis was validated using ROC curve analysis in the external database GSE120721. Immunohistochemical staining was performed for further validation. The CIBERSORT algorithm was used to evaluate skin samples obtained from healthy controls (HCs) and AD patients, to determine the immune cell infiltration. The association between identified hub genes and significant differential immune cells was analyzed using Pearson correlation analysis. Results: A total of 259 DEGs were acquired from the intersection of DEGs obtained by the two independent procedures, and 331 AD-trait module genes were separated out from the blue module via WGCNA analysis. Then, 169 co-DEGs arising from the intersection of the DEGs and the AD-trait module genes were obtained. We found that co-DEGs were significantly enhanced in the type I interferon and IL-17 signal transduction pathways. Five hub genes were identified after screening via external dataset validation and immunohistochemical analysis. We also identified four significant differential immune cells between AD patients and HCs. Moreover, the relationship between the identified hub genes and significant differential immune cells was analyzed. The results showed that CCR7 expression level was positively correlated with the number of CD4+ naïve T cells. Conclusions: CCR7, CXCL10, IRF7, MMP1, and RRM2 could be potential diagnostic and therapeutic biomarkers for AD. CCR7 expression level was positively correlated with the number of CD4+ naïve T cells in AD. These findings need to be corroborated in future studies.