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首页> 外文期刊>Radiation Oncology Journal >Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach
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Exploring the differentially expressed genes in human lymphocytes upon response to ionizing radiation: a network biology approach

机译:响应电离辐射时探索人淋巴细胞中的差异表达基因:网络生物学方法

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Purpose The integration of large-scale gene data and their functional analysis needs the effective application of various computational tools. Here we attempted to unravel the biological processes and cellular pathways in response to ionizing radiation using a systems biology approach. Materials and Methods Analysis of gene ontology shows that 80, 42, 25, and 35 genes have roles in the biological process, molecular function, the cellular process, and immune system pathways, respectively. Therefore, our study emphasizes gene/protein network analysis on various differentially expressed genes (DEGs) to reveal the interactions between those proteins and their functional contribution upon radiation exposure. Results A gene/protein interaction network was constructed, which comprises 79 interactors with 718 interactions and TP53, MAPK8, MAPK1, CASP3, MAPK14, ATM, NOTCH1, VEGFA, SIRT1, and PRKDC are the top 10 proteins in the network with high betweenness centrality values. Further, molecular complex detection was used to cluster these associated partners in the network, which produced three effective clusters based on the Molecular Complex Detection (MCODE) score. Interestingly, we found a high functional similarity from the associated genes/proteins in the network with known radiation response genes. Conclusion This network-based approach on DEGs of human lymphocytes upon response to ionizing radiation provides clues for an opportunity to improve therapeutic efficacy.
机译:目的大规模基因数据的整合及其功能分析需要有效应用各种计算工具。在这里,我们试图响应于使用系统生物学方法的电离辐射来解开生物过程和细胞途径。基因本体的材料和方法分析表明,80,42,25和35个基因分别具有生物学过程,分子功能,细胞过程和免疫系统途径的作用。因此,我们的研究强调了各种差异表达基因(DEGS)的基因/蛋白质网络分析,揭示这些蛋白质之间的相互作用及其在辐射暴露时的功能贡献。结果构建了基因/蛋白质相互作用网络,其包含79个交互因子,其中79个交互式和TP53,MAPK8,MAPK1,CASP3,MAPK14,ATM,NOTCH1,VEGFA,SIRT1和PRKDC是网络中的前10个蛋白质,之间的高度中心地位高价值观。此外,使用分子复数检测来在网络中聚类这些相关合作伙伴,其基于分子复数检测(MCODE)得分产生三种有效簇。有趣的是,我们发现从网络中的相关基因/蛋白质的高函数相似性,具有已知的放射响应基因。结论这种基于网络在响应电离辐射的人淋巴细胞上的方法提供了提高治疗疗效的机会的线索。

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