...
首页> 外文期刊>Cancer gene therapy >Screening genes crucial for pediatric pilocytic astrocytoma using weighted gene coexpression network analysis combined with methylation data analysis
【24h】

Screening genes crucial for pediatric pilocytic astrocytoma using weighted gene coexpression network analysis combined with methylation data analysis

机译:加权基因共表达网络分析与甲基化数据分析相结合筛选对小儿毛细胞星形细胞瘤至关重要的基因

获取原文
   

获取外文期刊封面封底 >>

       

摘要

To identify novel genes associated with pediatric pilocytic astrocytoma (PA) for better understanding the molecular mechanism underlying the pediatric PA pathogenesis. Gene expression profile data of GSE50161 and GSE44971 and the methylation data of GSE44684 were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between PA and normal control samples were screened using the limma package in R, and then used to construct weighted gene coexpression network (WGCN) using the WGCN analysis (WGCNA) package in R. Significant modules of DEGs were selected using the clustering analysis. Function enrichment analysis of the DEGs in significant modules were performed using the WGCNA package and clusterprofiler package in R. Correlation between methylation sites of DEGs and PA was analyzed using the CpGassoc package in R. Totally, 3479 DEGs were screened in PA samples. Thereinto, 3424 DEGs were used to construct the WGCN. Several significant modules of DEGs were selected based on the WGCN, in which the turquoise module was positively related to PA, whereas blue module was negatively related to PA. DEGs (for example, DOCK2 (dedicator of cytokinesis 2), DOCK8 and FCGR2A (Fc fragment of IgG, low affinity IIa)) in blue module were mainly involved in Fc gamma R-mediated phagocytosis pathway and natural killer cell-mediated cytotoxicity pathway. Methylations of 14 DEGs among the top 30 genes in blue module were related to PA. Our data suggest that DOCK2, DOCK8 and FCGR2A may represent potential therapeutic targets in PA that merits further investigation.
机译:为了识别与小儿毛细胞星形细胞瘤(PA)相关的新基因,以更好地了解小儿PA发病机理的分子机制。 GSE50161和GSE44971的基因表达谱数据以及GSE44684的甲基化数据是从Gene Expression Omnibus下载的。使用R中的limma软件包筛选PA和正常对照样品之间的差异表达基因(DEG),然后使用R中的WGCN分析(WGCNA)软件包构建加权基因共表达网络(WGCN)。DEG的重要模块是使用聚类分析选择。使用R中的WGCNA软件包和clusterprofiler软件包对重要模块中的DEG进行功能富集分析。使用R中的CpGassoc软件包分析DEG和PA的甲基化位点之间的相关性。总共在PA样品中筛选了3479个DEG。其中,使用3424个DEG来构建WGCN。基于WGCN选择了几个重要的DEG模块,其中绿松石模块与PA正相关,而蓝色模块与PA正相关。蓝色模块中的DEG(例如DOCK2(细胞分裂的专用2),DOCK8和FCGR2A(IgG的Fc片段,低亲和力IIa))主要参与FcγR介导的吞噬作用途径和自然杀伤细胞介导的细胞毒性途径。蓝色模块前30个基因中14个DEG的甲基化与PA相关。我们的数据表明,DOCK2,DOCK8和FCGR2A可能代表PA中潜在的治疗靶标,值得进一步研究。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号