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A novel method to identify differential pathways in uterine leiomyomata based on network strategy

机译:基于网络策略的子宫平霉病鉴别差异途径的一种新方法

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The aim of the present study was to identify differential pathways in uterine leiomyomata (UL) using a novel method based on protein-protein interaction networks and pathway analysis. The pathway networks were constructed by examining the intersections of the Reactome database and the Search Tool for the Retrieval of Interacting Genes/proteins (STRING) protein-protein interaction (PPI) networks. The Objective network was defined as the differential expressed genes (DEGs) associated with the interactions identified by STRING. Topological centrality (degree) analysis was performed for the Objective network to explore the hub genes and hub networks. Subsequent to isolating the intersections between the Pathway and Objective networks, randomization tests were conducted to identify the differential pathways. There were 559,598 interactions in the Pathway networks. A total of 657 genes with 3,835 interactions were mapped in the Objective network, which included 20 hub genes. It was identified that 358 pathways demonstrated interaction with the Objective network, such as Signal Transduction, Immune System and Signaling by G-protein-coupled receptor (GPCR). By accessing the randomization tests, P-values of these pathways were close to 0, which indicated that they were significantly different. The present study successfully identified differential pathways (such as signal transduction, immune system and signaling by GPCR) in UL, which may be potential biomarkers in the detection and treatment of UL.
机译:本研究的目的是使用基于蛋白质 - 蛋白质相互作用网络和途径分析的新方法识别子宫平霉(UL)的差动途径。通过检查反应组数据库的交叉点和检索相互作用基因/蛋白(串)蛋白质相互作用(PPI)网络的搜索工具来构建途径网络。目标网络定义为与串鉴定的相互作用相关的差异表达基因(DEG)。对客观网络进行拓扑中心(度)分析,用于探索集线器基因和集线器网络。在分离途径与客观网络之间的交叉点之后,进行随机化测试以识别差分途径。路线网络中有559,598个交互。在目标网络中映射了总共657个基因,其中包括20个枢纽基因。鉴定了358个途径证明了与目标网络的相互作用,例如通过G蛋白偶联受体(GPCR)的信号转导,免疫系统和信号传导。通过访问随机化测试,这些途径的p值接近0,表明它们显着不同。本研究成功地鉴定了UL中的差分途径(如信号转导,免疫系统和通过GPCR信号传导,这可能是UL检测和治疗中的潜在生物标志物。

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