Bioinformatics analysis of differentially expressed genes on alopecia
Received date: 2019-10-02
Revised date: 2019-12-13
Online published: 2019-12-23
Supported by
the Shenzhen Science and Technology Innovation Committee Basic Science Research Grant(JCYJ20180507182250795);China Postdoctoral Science Foundation Grand(2019M663173);the Shenzhen Peacock Team Project(KQTD20170331160605510)
The molecular mechanism of alopecia areata (AA) is still elusive and here we utilized bioinformatics methods to analyze AA-related differentially expressed genes. In this study, GSE45512 and GSE45513 were downloaded from the NCBI sub-database Gene Expression Omnibus (GEO). The gene expressions of AA and normal samples were analyzed using the R package limma, which showed significant differences between AA and normal samples in two species. These genes were subject to functional annotation and protein interaction networks. At the same time, gene set enrichment analysis was conducted for all differentially expressed genes. The study revealed that a total of 225 differentially expressed genes were screened from human AA samples, and a total of 337 differentially expressed genes were screened from spontaneous AA skin samples in C3H/HeJ mice. There are 23 differentially expressed genes in the two species. GO and protein interaction network analysis shown gene enrichment in immune-related functions, and these proteins interact with each other. Gene set enrichment analysis showed that differential genes from both species were significantly enriched to chemokine signaling pathways, cytokine-cytokine receptor interactions, staphylococcus aureus infection, and antigen processing and presentation. Moreover, the human down-regulated differential gene not only maps to the alopecia in human phenotype ontology, but also maps to the pathologically relevant phenotype of the skin appendage. In brief, 23 significant differentially expressed genes were screened out coexisting in AA human and mouse by bioinformatics methods. In addition, the result demonstrated that AA is closely related to the immune process and skin appendage lesions. These results provide new ideas for the diagnosis and treatment of AA.
Key words： hair loss; alopecia areata; GEO; bioinformatics
Hong Xiang, Xiaohu Yang, Liangxia Ai, Yanping Pan, Yong Hu . Bioinformatics analysis of differentially expressed genes on alopecia[J]. Hereditas(Beijing), 2020 , 42(2) : 172 -182 . DOI: 10.16288/j.yczz.19-214
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