Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis.

BACKGROUND

Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disorder with a complex pathogenesis involving genetic predisposition, environmental factors, and immune dysregulation. This study aimed to investigate key differentially expressed genes (DEGs) in AD and their association with immune cell infiltration patterns.

METHODS

The GSE32924 dataset comprises gene expression data from 25 AD samples and 8 control samples. Differential expression analysis was performed using the R package limma. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using clusterProfiler. Weighted gene co-expression network analysis (WGCNA) was employed to identify gene modules. Least Absolute Shrinkage and Selection Operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen hub genes. Immune cell infiltration was evaluated using CIBERSORT. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed to validate DEG expression in peripheral blood samples from AD patients and healthy controls. Potential microRNA (miRNA)-messenger RNA (mRNA) and miRNA-long non-coding RNA (lncRNA) interactions were predicted using miRanda and TargetScan tools.

RESULTS

We identified 381 DEGs (217 upregulated, 164 downregulated). GO analysis revealed enrichment in skin barrier formation, epidermal development, and inflammatory response. KEGG analysis showed significant involvement of sphingolipid metabolism and Toll-like receptor signaling pathways. Five hub genes (ATP6V1A, CLDN23, ECSIT, LRFN5, USP16) were identified. Immune cell infiltration demonstrated significant differences in activated dendritic cells (aDCs) and regulatory T cells (Tregs) between AD and controls. RT-qPCR confirmed elevated ECSIT and decreased LRFN5 and USP16 expression in AD patients (P < 0.05). A competing endogenous RNA (ceRNA) network involving lncRNA-miRNA-mRNA interactions for the key gene ECSIT was also constructed.

CONCLUSION

ECSIT, LRFN5, and USP16 represent promising diagnostic biomarkers for AD and are involved in immune cell infiltration, providing new insights into AD pathogenesis.