中华皮肤科杂志 ›› 2022, Vol. 55 ›› Issue (5): 421-425.doi: 10.35541/cjd.20200391

• 研究报道 • 上一篇    下一篇

采用生物信息学方法筛选和分析白癜风差异表达基因

艾尼瓦尔·塔力甫1    熊成1    热甫哈提·赛买提1    玉苏甫·买提努尔1    吐尔逊·吾甫尔1    阿肯木江·艾尔肯1    居来提·阿不都瓦衣提1    买买提艾力·卡的2   

  1. 1新疆维吾尔自治区维吾尔医医院，乌鲁木齐  830049；2新疆医科大学维吾尔医学院，乌鲁木齐  830011
  • 收稿日期:2020-04-21 修回日期:2021-01-14 发布日期:2022-04-29
  • 通讯作者: 玉苏甫·买提努尔 E-mail:M13209938480@163.com
  • 基金资助:
    新疆维吾尔自治区重点研发计划项目（2016B03038-2）;中国科学院重点实验室干旱区植物资源化学实验室开放课题

Screening and analysis of differentially expressed genes in vitiligo using bioinformatics methods

Ainiwaer·Talifu1, Xiong Cheng1, Refuhati·Saimaiti1, Yusufu·Maitinuer1, Tuerxun·Wufuer1, Akenmujiang·Aierken1, Julaiti·Abuduwayiti1, Maimaitiaili·Kade2   

  1. 1Hospital of Xinjiang Traditional Uyghur Medicine, Urumqi 830049, China; 2Uyghur Medical College of Xinjiang, Urumqi 830011, China
  • Received:2020-04-21 Revised:2021-01-14 Published:2022-04-29
  • Contact: Yusufu·Maitinuer E-mail:M13209938480@163.com
  • Supported by:
    The Key Research and Development Project of Xinjiang Uygur Autonomous Region（2016B03038-2）;The Project of Key Laboratory of Plant Resource Chemistry in Arid Regions, Chinese Academy of Sciences

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摘要： 【摘要】 目的 通过生物信息学方法探索与白癜风进展相关的信号通路和基因。方法 从GEO数据库中下载白癜风芯片检测数据集GSE75819，利用R语言软件中limma包的LMFit和eBayes函数筛选15例印度白癜风患者皮损与非皮损组织间的差异表达基因（DEG）。通过京都基因与基因组数据库（KEGG）、基因本体论（GO）分析和基因集富集分析（GSEA）评估DEG的富集途径和功能。通过蛋白-蛋白相互作用网络从DEG中筛选中心基因。2019年1 - 6月于新疆维吾尔自治区维吾尔医医院收集8例汉族寻常型白癜风患者皮损及非皮损皮肤组织标本，采用实时定量PCR法验证上述上调及下调差异最大的10个DEG的表达。结果 与15例非皮损组织相比，在15例白癜风皮损组织中共发现148个DEG，其中KRT9、CXCL10、C8ORF59、TPSAB1、RPL26为前5位上调基因，SILV、RPPH1、TYRP1、MLANA、LOC401115为前5位下调基因，且经实时定量PCR在8例汉族白癜风患者皮损及非皮损组织中验证。GO分析显示，DEG主要富集于翻译起始、细胞对脂多糖的反应、核糖体、核糖体亚基和核糖体的结构组成等。KEGG通路分析显示，DEG主要富集于酪氨酸代谢、PPAR信号通路、氧化磷酸化和Toll样受体信号通路。蛋白-蛋白相互作用分析筛选出UPF3B、SNRPG、MRPL13和RPL26L1 4个中心基因。结论 KRT9、CXCL10、C8ORF59、TPSAB1、RPL26、SILV、RPPH1、TYRP1、MLANA及LOC401115可能作为白癜风潜在的诊断标记分子和治疗靶点。

关键词: 白癜风, 生物信息学, 差异表达基因, 信号通路

Abstract: 【Abstract】 Objective To explore potential signaling pathways and genes related to vitiligo progression by using bioinformatics methods. Methods A vitiligo genechip dataset GSE75819 was downloaded from the Gene Expression Omnibus （GEO） database. Differentially expressed genes （DEGs）were screened between lesional and non-lesional skin tissues from 15 Indian patients with vitiligo with the dataset GSE75819 by using LMFit and eBayes functions in R LIMma package. The Kyoto Encyclopedia of Genes and Genomes （KEGG）-based pathway analysis, Gene Ontology （GO） analysis and Gene set enrichment analysis （GSEA） were carried out to identify enriched pathways and functions of the DEGs. Protein-protein interaction networks were established to screen hub genes from the DEGs. In addition, lesional and non-lesional skin tissue specimens were obtained from 8 patients of Han nationality with vitiligo vulgaris in Hospital of Xinjiang Traditional Uyghur Medicine between January and June in 2019, and real-time quantitative PCR was performed to verify the expression of the top 10 up- or down-regulated DEGs. Results Compared with the 15 non-lesional skin tissues, a total of 148 DEGs were identified in the 15 lesional skin tissues. Among these DEGs, KRT9, CXCL10, C8ORF59, TPSAB1 and RPL26 were the top 5 up-regulated genes, and SILV, RPPH1, TYRP1, MLANA and LOC401115 were the top 5 down-regulated genes, which were all verified by real-time quantitative PCR in the lesional and non-lesional skin tissues from the 8 patients of Han nationality with vitiligo. GO analysis showed that the DEGs were chiefly enriched in translational initiation, cellular response to lipopolysaccharide, ribosomes, ribosomal subunits and structural constituents of ribosomes. KEGG analysis showed that the DEGs were chiefly enriched in tyrosine metabolism, peroxisome proliferator-activated receptor signaling pathway, oxidative phosphorylation and Toll-like receptor signaling pathway. Four hub genes, including UPF3B, SNRPG, MRPL13 and RPL26L1, were screened out by protein-protein interaction analysis. Conclusion KRT9, CXCL10, C8ORF59, TPSAB1, RPL26, SILV, RPPH1, TYRP1, MLANA and LOC401115 genes may serve as potential diagnostic molecular markers and therapeutic targets for vitiligo.

Key words: Vitiligo, Bioinformatics, Differentially expressed genes, Signaling pathways

引用本文

艾尼瓦尔·塔力甫 熊成 热甫哈提·赛买提 玉苏甫·买提努尔 吐尔逊·吾甫尔 阿肯木江·艾尔肯 居来提·阿不都瓦衣提 买买提艾力·卡的. 采用生物信息学方法筛选和分析白癜风差异表达基因[J]. 中华皮肤科杂志, 2022,55(5):421-425. doi:10.35541/cjd.20200391

Ainiwaer·Talifu, Xiong Cheng, Refuhati·Saimaiti, Yusufu·Maitinuer, Tuerxun·Wufuer, Akenmujiang·Aierken, Julaiti·Abuduwayiti, Maimaitiaili·Kade. Screening and analysis of differentially expressed genes in vitiligo using bioinformatics methods[J]. Chinese Journal of Dermatology, 2022, 55(5): 421-425.doi:10.35541/cjd.20200391