中国麻风皮肤病杂志 ›› 2019, Vol. 35 ›› Issue (9): 520-525.doi: 10.12144/zgmfskin201909520

• 论著 • 上一篇    下一篇

特应性皮炎患儿血清中microRNAs芯片的生物信息学分析

韩悦  姚煦   

  1. 中国医学科学院北京协和医学院皮肤病研究所，南京，210042
  • 出版日期:2019-09-15 发布日期:2019-09-11
  • 通讯作者: 姚煦，E-mail: dryao_xu@126.com

Bioinformatics analysis of differentially expressed microRNAs in children with atopic dermatitis

HAN Yue, YAO Xu   

  1. Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing 210042, China
  • Online:2019-09-15 Published:2019-09-11
  • Contact: YAO Xu, E-mail: dryao_xu@126.com

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摘要： 目的：利用生物信息学方法分析特应性皮炎(AD)患儿血清中表达差异的microRNAs (miRNAs)，探讨其靶基因的功能及调控的信号通路在疾病的发生发展中的作用。方法：从基因表达综合数据库（GEO）中下载8例AD患儿的血清和8名健康儿童的血清样本中miRNAs的表达数据，利用R 3.4.1软件包筛选差异表达的miRNA，选取其中最具差异表达的miRNA进行后续分析并利用荧光定量PCR进行验证。利用mirTarbase数据库预测其靶基因，采用cytoscape 3.5.1软件及其插件ClueGO、CluePedia进行靶基因的GO功能、KEGG信号通路富集。结果：在AD患儿血清中共筛选出相对于正常对照的差异表达miRNAs 7个，其中上调miRNAs 2个、下调miRNAs 5个。结合文献选取其中最具差异表达的miRNA-126进行下一步分析。荧光定量PCR结果显示，较于健康儿童，AD患儿血清中miRNA-126表达水平下调。数据库共预测出靶基因110个，GO分析显示差异表达基因主要涉及血管通透性的调节、RAC蛋白信号转导、内皮细胞增殖的调节、基质黏附依赖性细胞扩散、活化MAPKK活性、磷蛋白结合等功能，KEGG 分析提示其在包括FoxO信号通路、NF-κB信号通路、B细胞受体信号通路、VEGF信号通路等17个通路中富集。结论：利用生物信息学方法能有效对AD患儿血清中miRNAs芯片数据进行挖掘，为进一步研究AD发生发展机制及寻找潜在的药物治疗靶点提供参考。

关键词: 生物信息学分析, microRNA, 特应性皮炎

Abstract: Objective: The expression of microRNAs (miRNAs) in serum of children with atopic dermatitis (AD) was analyzed by bioinformatics, and the function and signal pathways of target genes in the pathogenesis of AD were discussed. Methods: Data of miRNAs in serum of 8 AD children and 8 healthy volunteers were downloaded from Gene Expression Omnibus (GEO). The differentially expressed miRNAs were screened using R3.4.1 software package. The most differentially expressed microRNA selected for subsequent analysis was verified using fluorescence quantitative PCR. The target genes were predicted on Mir Tarbase database of which the GO function and KEGG signal pathway were enriched by cytoscape 3.5.1 software and its plug-ins ClueGO and CluePedia. Results: Seven miRNAs differentially expressed in the serum of children with AD were screened, including 2 up-regulated miRNAs and 5 down-regulated miRNAs. The most differentially expressed miRNA-126 was selected for further analysis. Fluorescence quantitative PCR showed that the expression of microRNA126 in serum of children with AD was lower than that of healthy children. The database predicted 110 target genes. GO analysis showed that the target genes mainly involved in regulation of vascular permeability, RAC protein signal transduction, endothelial cell proliferation, matrix-dependent cell proliferation, activation of MAPKK activity, phosphoprotein binding and so on. KEGG analysis indicated that target genes enriched in 17 signaling pathways, such as FoxO signaling pathway, NF-κB signaling pathway, B cell receptor signaling pathway, VEGF signaling pathway. Conclusion: Bioinformatics may effectively analyze the microRNAs data of the serum in children with AD, and provide evidence for further research on the mechanism of the disease and potential drug treatment targets.

Key words: bioinformatics analysis, microRNA, atopic dermatitis