| 孙雪松,王 宁,李 杰,王 娜,王健健,王丽华.重症肌无力风险基因挖掘[J].,2017,17(30):5944-5949 |
| 重症肌无力风险基因挖掘 |
| Identification of the Risk Genes of Myasthenia Gravis |
| 投稿时间：2017-06-15 修订日期：2017-07-06 |
| DOI：10.13241/j.cnki.pmb.2017.30.033 |
| 中文关键词: 重症肌无力 风险基因 通路 |
| 英文关键词: Myasthenia gravis Risk gene Pathway |
| 基金项目:国家自然科学基金项目（81571166，81371324）；2016年哈尔滨市应用技术研究与开发计划项目（优秀学科带头人）：2016RAXYJ067 |
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| 中文摘要: |
| 摘要 目的：挖掘重症肌无力(Myasthenia gravis，MG)可能的风险基因。方法：通过人工挖掘在PubMed数据库收集重症肌无力风险基因，通过Gene数据库获取重症肌无力风险基因编号，用以表示基因或者其相应的蛋白。应用基因功能分析软件DAVID(http://david.abcc.ncifcrf.gov/)对重症肌无力风险基因进行KEGG通路富集分析，挖掘重症肌无力风险通路，进而对任意两个通路进行关联分析。应用基因功能分析软件DAVID的Gene Ontology，对MG风险基因进行功能注释，以P<0.01来判定注释是否有显著意义。结果：(1) 本研究挖掘出97个重症肌无力的风险基因，KEGG基因富集分析共筛选出44条与重症肌无力显著相关的通路，主要包括多种自身免疫性疾病相关通路、信号转导相关通路、肿瘤相关通路、抗原的加工提呈通路等等。(2)以上44条风险通路两两通路间均具有相关性(P<.0.01)。结论：本研究共挖掘出44条重症肌无力风险通路，8个重症肌无力风险基因，分别为：NF-kB、TNFR、MEK、AP-1、Raf、MEK1/2、MSK1、TAPBP。其中，MEK同时出现在多个风险通路中，考虑其风险性更高。 |
| 英文摘要: |
| ABSTRACT Objective: To explore the possible risk genes of myasthenia gravis. Methods: In this study, the risk genes of myasthenia gravis were collected by artificial digging in the PubMed database. The myasthenia gravis risk genes number were obtained from the Gene database to express the gene or its corresponding protein. KEGG pathway enrichment analysis of myasthenia gravis risk genes were carried out by using gene functional analysis software DAVID (http://david.abcc.ncifcrf.gov/), and the risk genes of myasthenia gravis was explored, and then any two pathways were analyzed. The MG risk gene were functionally annotated with the gene functional analysis software DAVID gene Ontology. P<0.01 was used to determine whether the note has significant significance. Results: (1) In this study, 97 risk genes of myasthenia gravis were excavated. KEGG gene enrichment analysis was used to screen out 44 channels which were related to myasthenia gravis, including a variety of autoimmune diseases related pathways, signal transduction Pathways, tumor-related pathways, antigen processing pathways and so on. (2) Correlation analysis of these 44 risk pathways was conducted, we found that there is a correlation between the two pathways. Conclusion: A total of 44 myasthenia gravis risk pathways and 8 myasthenia gravis risk genes were identified: NF-kB, TNFR, MEK, AP-1, Raf, MEK1/2, MSK1, TAPBP. MEK appeared simultaneously in multiple risk pathways, which is taking intohaving a higher risk of myasthenia gravis. |
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