ArticleClinical, cosmetic and investigational dermatology2025
Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis.
Article in Clinical, cosmetic and investigational dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Skin Barrier Repair and Nursing Care in Patients with Atopic Dermatitis: A Narrative Review.International journal of general medicine · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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