通过鲁棒网络成分分析检测癌症发展过程中磷酸化决定的活性蛋白相互作用网络

T. Zeng, Ziming Wang, Luonan Chen
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摘要

动机:在最近的疾病研究中,发现许多关键的病原体基因/蛋白没有显著的差异表达,因此在常规的差异表达分析或网络分析中往往被忽略。与此同时,我们提出了干燥实验中的活性而不是湿实验中的表达,以有效地估计转录因子等重要生物分子的实际调控能力。但是,一个隐藏的因素(如磷酸化)是什么以及如何决定这种虚拟调节能力作为活性[1]仍然是未知的。特别是在癌症发展研究中,重构活性蛋白相互作用网络,动态检测潜在的磷酸化模式已迫在眉睫[2-7]。方法:基于肝癌小鼠c-Myc模型,我们首先收集了多个发育时间点的蛋白表达和蛋白磷酸化数据。然后,我们利用条件互信息构建了一个粗略的蛋白质相互作用网络作为背景。接下来,我们对传统的网络成分分析方法进行鲁棒性改进,采用RNCA (Robust network component analysis,鲁棒网络成分分析)方法重构时间依赖性蛋白相互作用网络,同时估计目标蛋白在不同时间的活性。最后,考虑到蛋白质表达和磷酸化数据的实验质量不同,我们使用典型相关分析检测一组蛋白质(如蛋白质网络模块)的表达与磷酸化之间的最大相关性,从而揭示活性蛋白质子网络及其磷酸化的决定因素。结果:在初步研究中,我们通过与其他常规方法的比较,评估了RNCA的稳健性。在真实的生物学数据上,我们发现了癌症发展过程中重新连接的蛋白质相互作用网络,它对应的活性蛋白质,以及它们的驱动因素是蛋白质磷酸化。这项工作可以进一步应用于边缘生物标志物[1-2]、网络生物标志物[3-4]和动态网络生物标志物[5-7]的疾病早期诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Phosphorylation Determined Active Protein Interaction Network during Cancer Development by Robust Network Component Analysis
Motivation: In recent disease study, many key pathogen genes/proteins are found to have not significant differential expressions, and thus, they tend to be disregarded in conventional differential expression analysis or network analysis. Meanwhile, the activity in dry-experiment rather than expression in wet-experiment have been proposed to effectively estimate the actual regulation power of such important biomolecules, e.g. transcriptional factors. But, it is still unknown what and how a hidden factor (e.g. phosphorylation) determines this kind of virtual regulation power as activity [1]. Especially, for the cancer development study, it is emergent to reconstruct the active protein interaction network and detect the underlying phosphorylation pattern in a dynamic manner [2-7]. Methods: Based on the c-Myc mouse model of liver cancer, we have first collected protein expression and protein phosphorylation data at several developmental time points. Then, we constructed a rough protein interaction network as background by conditional mutual information. Next, we improved the conventional network component analysis on its robustness, and used this advanced approach RNCA (Robust Network Component Analysis) to reconstruct the time-dependent protein interaction networks and estimate the activity of target protein at different times simultaneously. Finally, considering the different experiment-qualities of protein expression and phosphorylation data, we used canonical correlation analysis to detect the maximal correlation between the expression and phosphorylation of a group of proteins (e.g. protein network module), which could reveal the active protein sub-network and its determinate factor as phosphorylation. Results: In the preliminary study, we have evaluated the robustness of RNCA by comparing with other conventional methods. And on the real biological data, we have found the rewired protein interaction network during cancer development, its corresponding active proteins, and their drivers as protein phosphorylation. This work can be further used in early diagnosis of diseases by edge biomarkers [1-2], network biomarkers [3-4] and dynamical network biomarkers [5-7].
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