Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis
Mingmei Yang,1 Xiaolei Zhang,1 Chao Zhou,2 Yuanyun Du,1 Mengyuan Zhou,1 Wenting Zhang1 1Department of Dermatology, Affiliated Changzhou Children’s Hospital of Nantong University, Changzhou, People’s Republic of China; 2Institute of Biomedical Engineering and Health Sciences, Changzhou University, Ch...
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Dove Medical Press
2025-05-01
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| author | Yang M Zhang X Zhou C Du Y Zhou M Zhang W |
| author_facet | Yang M Zhang X Zhou C Du Y Zhou M Zhang W |
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| description | Mingmei Yang,1 Xiaolei Zhang,1 Chao Zhou,2 Yuanyun Du,1 Mengyuan Zhou,1 Wenting Zhang1 1Department of Dermatology, Affiliated Changzhou Children’s Hospital of Nantong University, Changzhou, People’s Republic of China; 2Institute of Biomedical Engineering and Health Sciences, Changzhou University, Changzhou, People’s Republic of ChinaCorrespondence: Wenting Zhang, Email saiyukeita@163.comBackground: 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.Keywords: atopic dermatitis, AD, gene expression analysis, immune cell infiltration, functional enrichment analysis, Mendelian randomization analysis |
| format | Article |
| id | doaj-art-30b025ae829e41c883c0c7bcd090dbc1 |
| institution | OA Journals |
| issn | 1178-7015 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Dove Medical Press |
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| series | Clinical, Cosmetic and Investigational Dermatology |
| spelling | doaj-art-30b025ae829e41c883c0c7bcd090dbc12025-08-20T02:15:29ZengDove Medical PressClinical, Cosmetic and Investigational Dermatology1178-70152025-05-01Volume 18Issue 110711085102612Bioinformatics&#x2011;Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic DermatitisYang M0Zhang X1Zhou C2Du Y3Zhou M4Zhang WDepartment of DermatologyDepartment of DermatologyBiomedical EngineeringdermatologyDepartment of dermatologyMingmei Yang,1 Xiaolei Zhang,1 Chao Zhou,2 Yuanyun Du,1 Mengyuan Zhou,1 Wenting Zhang1 1Department of Dermatology, Affiliated Changzhou Children’s Hospital of Nantong University, Changzhou, People’s Republic of China; 2Institute of Biomedical Engineering and Health Sciences, Changzhou University, Changzhou, People’s Republic of ChinaCorrespondence: Wenting Zhang, Email saiyukeita@163.comBackground: 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.Keywords: atopic dermatitis, AD, gene expression analysis, immune cell infiltration, functional enrichment analysis, Mendelian randomization analysishttps://www.dovepress.com/bioinformaticsbased-analysis-reveals-diagnostic-biomarkers-and-immune--peer-reviewed-fulltext-article-CCIDAtopic DermatitisGene Expression AnalysisImmune Cell InfiltrationFunctional Enrichment AnalysisMendelian Randomization Analysis |
| spellingShingle | Yang M Zhang X Zhou C Du Y Zhou M Zhang W Bioinformatics&#x2011;Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis Clinical, Cosmetic and Investigational Dermatology Atopic Dermatitis Gene Expression Analysis Immune Cell Infiltration Functional Enrichment Analysis Mendelian Randomization Analysis |
| title | Bioinformatics&#x2011;Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis |
| title_full | Bioinformatics&#x2011;Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis |
| title_fullStr | Bioinformatics&#x2011;Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis |
| title_full_unstemmed | Bioinformatics&#x2011;Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis |
| title_short | Bioinformatics&#x2011;Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis |
| title_sort | bioinformatics amp x2011 based analysis reveals diagnostic biomarkers and immune landscape in atopic dermatitis |
| topic | Atopic Dermatitis Gene Expression Analysis Immune Cell Infiltration Functional Enrichment Analysis Mendelian Randomization Analysis |
| url | https://www.dovepress.com/bioinformaticsbased-analysis-reveals-diagnostic-biomarkers-and-immune--peer-reviewed-fulltext-article-CCID |
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