论文部分内容阅读
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Pub Med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。
Objective: To explore possible risk genes of Myasthenia gravis (MG). METHODS: Myasthenia gravis gene was collected by manual mining in the Pub Med database, and gene risk of myasthenia gravis was obtained by Gene database to express the gene or its corresponding protein. Using the gene function analysis software DAVID (http://david.abcc.ncifcrf.gov/), KEGG pathway enrichment analysis was performed on the gene of myasthenia gravis, and the myasthenia gravis risk pathway was excavated, then any two pathways were analyzed. Gene Ontology of DAVID, a gene function analysis software, was used to annotate functional genes of MG risk and determine whether the annotation is significant by P <0.01. Results: (1) In our study, we screened out 97 myasthenia gravis risk genes. Forty-four pathways that were significantly associated with myasthenia gravis were identified by KEGG gene enrichment analysis, including multiple autoimmune diseases related pathways, Lead-related pathways, tumor-related pathways, antigen presenting pathways and the like. (2) The above 44 risk pathways were correlated between two pathways (P <.01.01). CONCLUSIONS: A total of 44 myasthenia gravis risk pathways and 8 myasthenia gravis risk genes were excised in this study, including NF-kB, TNFR, MEK, AP-1, Raf, MEK1 / 2, MSK1 and TAPBP. Among them, MEK appears in multiple risk pathways at the same time, considering its risk is higher.