Copyright (c) 2023 Pengfei Yang, Yanmei Li, Tingbao Li, Liang Yuan, Sinong Wang
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Screening differentially expressed genes and the pathogenesis in atopic dermatitis using bioinformatics
Corresponding Author(s) : Sinong Wang
Cellular and Molecular Biology,
Vol. 69 No. 15: New discoveries in inflammatory factors
Abstract
Atopic dermatitis (AD) is a common chronic skin inflammation. It was to screen differentially expressed genes (DEGs) and related biological functional pathways in atopic dermatitis (AD) by bioinformatics methods, and to understand the pathogenesis of AD. gene chip datasets GSE120721 and GSE32924 in the public database NCBI Gene Expression Omnibus (GEO) were adopted. Differential expression analysis between the patient group and controls was performed by applying the zero-code differential expression analysis tool GEO2R, and a few DEGs were screened. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out. In addition, the STRING online database was employed to predict the potential relationship among DEGs, the protein-protein interaction network (PPI) was drawn, and the module analysis of PPI was performed using the Cytoscape plugin MCODE. 233 DEGs were screened out, including 134 up-regulated genes and 99 down-regulated genes. GO analysis suggested that these DEGs were mainly involved in biological processes (BP), cellular components (CC), and molecular function (MF). KEGG analysis displayed that these DEGs were mainly involved in NF-kappa B signaling, cell cycle, T cell receptor signaling, and other pathways. PPI analysis indicated that there were complex interactions among DEGs, and module analysis further revealed the important roles of DEGs in regulating immune response, inflammatory response, and skin barrier function. The above findings provide a valuable reference for the development of new treatment options.
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