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Updated: Aug 10, 2026
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Integrated bioinformatics-based identification of potential diagnostic biomarkers associated with atopic dermatitis
1Department of Dermatology, Children's Hospital of Chongqing Medical University, National Clinical Research Centre for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
Introduction:
In-depth analysis of the rambling genes of atopic dermatitis may help to identify the pathologic mechanism of this disease. However, this has seldom been performed.
Aim:
Using bioinformatics approaches, we analysed 3 gene expression profiles in the gene expression omnibus (GEO) database, identified the differentially expressed genes (DEGs), and found out the overlapping DEGs (common DEGs, cDEGs) in the above 3 profiles.
Material And Methods:
We identified 91 upregulated cDEGs, which were then arranged into a protein-protein interaction (PPI) network, and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) term enrichment analyses were performed to explore the functional roles of these genes.
Results:
GO analyses revealed these DEGs to be significantly enriched in biological processes including immune system process, immune response, defence response, leukocyte activation, and response to the biotic stimulus. These DEGs were also enriched in the KEGG pathway, including influenza A, amoebiasis, primary immunodeficiency, cytokine-cytokine receptor interaction, and IL-17 signalling pathway. PPI analysis showed that 9 genes (PTPRC-CTLA4-CD274-CD1C-IL7R-GZMB-CCL5-CD83, and CCL22) were probably the novel hub genes of atopic dermatitis.
Conclusions:
Together, the findings of these bioinformatics analyses thus identified key hub genes associated with AD development.
