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在癌症中使用多个蛋白质-蛋白质相互作用网络识别显著扰乱的子网络

Le Yang¹, Runpu Chen¹, Thomas Melendy¹

¹Department of Microbiology and Immunology, The State University of New York at Buffalo, Buffalo, NY 14203, USA.

|August 26, 2023

概括

这项研究介绍了MultiFDRnet,这是一种使用多重网络识别癌症途径的新方法. 它通过整合多个蛋白质-蛋白质相互作用网络以提高准确性,有效地检测扰乱的子网络.

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
癌症基因组学癌症基因组学

背景情况:

大规模的癌症基因组研究旨在确定癌症驱动基因和分子途径.
基于网络的方法使用基因组学数据和蛋白质-蛋白质相互作用 (PPI) 网络拓来检测扰乱的子网络作为潜在的癌症途径.
PPI网络结构的变化和特定环境网络的不完整性挑战了现有的子网络检测算法.

研究的目的:

提出一种新的方法,MultiFDRnet,以解决癌症途径识别中现有的子网络检测算法的局限性.
开发一种能够有效利用多个PPI网络并处理不完整的拓结构的方法.
提高检测癌症相关子网络的准确性和可靠性.

主要方法:

建模了一组PPI网络作为多重网络,以保留单个网络拓,并引入网络间的依赖性.
开发了MultiFDRnet,通过同时使用来自多重网络的所有结构信息来检测明显扰乱的子网络.
集成的基因组学数据与多个PPI网络的集成拓信息.

主要成果:

对模拟和真实癌症数据的基准分析表明了MultiFDRnet.net的有效性.
该方法成功检测了由多个PPI网络支持的显著扰乱的子网络.
MultiFDRnet在特定环境的PPI网络中发现了新的模块化结构.

结论:

通过整合来自多个PPI网络的信息,MultiFDRnet提供了一种强大的方法来识别癌症途径.
多重网络建模有效地解决了由不同的和不完整的网络结构所带来的挑战.
该方法增强了与癌症相关的分子子网络和潜在的治疗点的发现.

关键词:

癌症癌症癌症癌症癌症驱动基因驱动基因驱动基因司机的路径司机的路径蛋白质蛋白质相互作用严重扰乱的子网络显著受到干扰.

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