[1]傅惠佳,王孟皓,刘西茹.反复着床失败子宫内膜组织差异表达基因的生物信息学分析[J].第三军医大学学报,2019,41(13):1246-1252.
 FU Huijia,WANG Menghao,LIU Xiru.Bioinformatics analysis of differentially expressed genes in endometrium with recurrent implantation failure[J].J Third Mil Med Univ,2019,41(13):1246-1252.
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反复着床失败子宫内膜组织差异表达基因的生物信息学分析(/HTML )
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《第三军医大学学报》[ISSN:1000-5404/CN:51-1095/R]

卷:
41卷
期数:
2019年第13期
页码:
1246-1252
栏目:
基础医学
出版日期:
2019-07-15

文章信息/Info

Title:
Bioinformatics analysis of differentially expressed genes in endometrium with recurrent implantation failure
作者:
傅惠佳1王孟皓2刘西茹1
400016 重庆,重庆医科大学附属第一医院生殖医学中心1;400010 重庆,重庆医科大学附属第二医院肝胆外科2
Author(s):
FU Huijia1 WANG Menghao2 LIU Xiru1  

1Center for Reproductive Medicine, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016; 2Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China

关键词:
反复着床失败子宫内膜生物信息学基因
Keywords:
recurrent implantation failure endometrium bioinformatics gene
分类号:
R318.04; R394.3; R711.6
文献标志码:
A
摘要:

目的应用生物信息学方法分析体外受精(in vitro fertilization,IVF)后反复着床失败(recurrent implantation failure,RIF)的相关基因,以探讨反复着床失败的发病机制。方法从美国国立生物技术信息中心(National Center for Biotechnology Information, NCBI)公共数据平台(Gene Expression Omnibus,GEO)下载基因芯片数据集GSE111974,使用R语言筛选出反复着床失败和正常生育女性子宫内膜组织的差异表达基因(differentiallyexpressed genes, DEGs)。再利用生物信息学分析工具DAVID(The database forannotation,visualization and integrated discovery)对差异基因进行基因本体论(Gene Ontology,GO)功能富集分析和京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)通路分析,利用STRING(Search Tool for the Retrieval of Interacting Genes)在线数据库和Cytoscape软件进行蛋白互作网络分析。结果初筛出170个明显差异表达的基因,其中上调者127个,下调者43个。GO功能富集显示:这些DEGs主要富集于细胞膜、内质网等区域,主要参与氧化还原过程、细胞周期阻滞、脂质代谢过程等;主要与血红素结合、信号传递器活性、维甲酸结合、过氧化物酶活性等功能簇相关。KEGG分析显示其主要参与代谢途径信号通路、催产素信号通路、肿瘤坏死因子信号通路等。STRING在线数据库和Cytoscape软件分析发现PTGS2、TOP2A、ACTA2等为反复着床失败的关键基因。结论采用生物信息学方法筛选出RIF患者子宫内膜组织差异表达的关键基因有PTGS2、TOP2A、ACTA2等,可能在RIF的发病机制中起重要作用。

Abstract:

ObjectiveTo investigate the related genes in the endometrium with recurrent implantation failure (RIF) after in vitro fertilization (IVF) by bioinformatics analysis in order to illustrate the underlying pathogenesis of RIF. MethodsThe dataset GSE111974 was downloaded from Gene Expression Omnibus (GEO) in National Center for Biotechnology Information (NCBI). R/Bioconductor packages “marray” and “limma” were applied to this model to screen out the differentially expressed gene (DEGs) in the patients with RIF compared with normal fertile controls. Then, the database for annotation, visualization and integrated discovery (DAVID) was employed to make Gene Ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis to analyze the protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes (STRING) online database and Cytoscape software. ResultsThere were 170 DEGs turning out, of which 127 genes were up-regulated and 43 were down-regulated. GO analysis shows that the DEGs mainly concentrate in cell membrane, endoplasmic reticulum and other areas, which primarily participate in oxidation-reduction process, cell cycle arrest and lipid metabolic process, etc. Their molecular functions are involved in heme binding, signal transducer activity, retinoic acid binding and peroxidase activity. KEGG pathway analysis showed that these genes are mainly involved in metabolic pathways, oxytocin signaling pathway, and TNF signaling pathway, etc. STRING online database and Cytoscape software found out that DEGs PTGS2, TOP2A and ACTA2 were the hub genes of RIF. ConclusionThe key genes PTGS2, TOP2A and ACTA2 and transcription factors CEBPB, CDX2 and ESR1 may play important role in the pathogenesis of RIF.

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更新日期/Last Update: 2019-07-08