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Supplementary Material for: Identification of Candidate Genes and Pathways in Nonsegmental Vitiligo Using Integrated Bioinformatics Methods

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https://figshare.com/articles/dataset/Supplementary_Material_for_Identification_of_Candidate_Genes_and_Pathways_in_Nonsegmental_Vitiligo_Using_Integrated_Bioinformatics_Methods/13359062
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Background: Nonsegmental vitiligo (NSV) is an acquired depigmentation disorder of unknown origin. Enormous interests focus on finding novel biomarkers and pathways responsible for NSV. Methods: The gene expression level was obtained by integrating microarray datasets (GSE65127 and GSE75819) from the Gene Expression Omnibus database using the sva R package. Differentially expressed genes (DEGs) between each group were identified by the limma R package. The interaction network was constructed using STRING, and significant modules coupled with hub genes were identified by cytoHubba and molecular complex detection. Pathway analyses were conducted using generally applicable gene set enrichment and further visualized in R environment. Results: A total of 102 DEGs between vitiligo lesional skin and healthy skin, 14 lesion-specific genes, and 29 predisposing genes were identified from the integrated dataset. Except for the anticipated decrease in melanogenesis, three major functional changes were identified, including oxidative phosphorylation, p53, and peroxisome proliferator-activated receptor (PPAR) signaling in lesional skin. PPARG, MUC1, S100A8, and S100A9 were identified as key hub genes involved in the pathogenesis of vitiligo. Besides, upregulation of the T cell receptor signaling pathway was considered to be associated with susceptibility of the skin in NSV patients. Conclusion: Our study reveals several potential pathways and related genes involved in NSV using integrated bioinformatics methods. It might provide references for targeted strategies for NSV.
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2020-12-10
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