Dataset Information

Identification of Immunological Biomarkers of Atopic Dermatitis by Integrated Analysis to Determine Molecular Targets for Diagnosis and Therapy

ABSTRACT:

Purpose

Atopic dermatitis (AD) is a common chronic inflammatory skin disorder associated with immune dysregulation and barrier dysfunction. In this study, we investigated immunological biomarkers for AD diagnosis and treatment using CIBERSORT to identify immune cell infiltration characteristics.

Patients and Methods

Common differentially expressed genes (DEGs) of lesioned (LS) vs non-lesioned (NL) groups were obtained from public datasets (GSE140684 and GSE99802). We performed functional enrichment analysis and selected hub genes from the protein–protein interaction (PPI) network. The hub genes were then subjected to transcription factor (TF), microRNA (miRNA), long non-coding RNA (lncRNA), drug interaction, and protein subcellular localization analyses. We also performed correlation analysis on differentially expressed immune cells, TFs, and hub genes. Receiver operating characteristic (ROC) curve analysis and binomial least absolute shrinkage and selection operator (LASSO) regression analysis were employed to assess the expression of hub genes in the GSE99802, GSE140684, GSE58558, GSE120721, and GSE36842 datasets.

Results

We identified 238 common DEGs and 25 hub genes. Additionally, we predicted TFs, miRNAs, lncRNA, drugs, and protein subcellular localizations. The proportions of activated dendritic cells (DCs) and CD4+ memory T cells were relatively high in the LS skin. Expression levels of the TF FOXC1 were negatively correlated with target genes and the abundance of two immune cell types. The LASSO model showed that GZMB, CXCL1, and CD274 are candidate diagnostic biomarkers.

Conclusion

Our study suggests that downregulated expression of FOXC1 expression may enhance the levels of chemokines, chemokine receptors, T cell receptor signaling molecules, activating CD4+ memory T cells and DCs in AD.

SUBMITTER: Zhong Y

PROVIDER: S-EPMC8605491 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Atopic dermatitis (AD) is a dermatological disorder characterized by symptoms such as chronically inflamed skin and frequently intolerable itching. The mechanism underlying AD development is still unclear. Our study aims to identify the diagnostic and therapeutic biomarkers for AD and provide insight into immune mechanisms at the molecular level through bioinformatics analysis. Methods: The GSE6012, GSE32924, and GSE36842 gene expression profiles were obtained for analysis from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were segregated using the “Batch correction” and “RobustRankAggreg” methods. Weighted gene co-expression network analysis (WGCNA) was performed to screen for module genes with AD traits. Then, common DEGs (co-DEGs) were screened out via combined differential expression analysis and WGCNA. Functional enrichment analysis was performed for these co-DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), followed by protein-protein interaction network analysis. Candidate hub genes were identified using the “cytoHubba” plugin in Cytoscape, and their value for AD diagnosis was validated using receiver operating characteristic 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 lesions of AD patients, to determine the extent of immune cell infiltration. The association between the 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 259 DEGs and the 331 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. Thirteen potential hub genes were identified using Cytoscape. Five hub genes (CCR7, CXCL10, IRF7, MMP1, and RRM2) were identified after screening via external dataset validation and immunohistochemical analysis. We also identified four significant differential immune cells, i.e., activated dendritic cells, plasma cells, resting mast cells, and CD4+ naïve T 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 the CCR7 expression level was positively correlated with the number of CD4+ naïve T cells (R = 0.42, p = 0.011). Conclusion: 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.