Buzdin Anton A, Prassolov Vladimir, Zhavoronkov Alex A, Borisov Nikolay M
Pathway Pharmaceuticals, Wan Chai, Hong Kong SAR.
Centre for Convergence of Nano-, Bio-, Information and Cognitive Sciences and Technologies, National Research Centre "Kurchatov Institute", Bldg 140, Suite 415, 1, Akademika Kurchatova sq., Moscow, 123182, Russia.
Methods Mol Biol. 2017;1613:53-83. doi: 10.1007/978-1-4939-7027-8_4.
We propose a biomathematical approach termed OncoFinder (OF) that enables performing both quantitative and qualitative analyses of the intracellular molecular pathway activation. OF utilizes an algorithm that distinguishes the activator/repressor role of every gene product in a pathway. This method is applicable for the analysis of any physiological, stress, malignancy, and other conditions at the molecular level. OF showed a strong potential to neutralize background-caused differences between experimental gene expression data obtained using NGS, microarray and modern proteomics techniques. Importantly, in most cases, pathway activation signatures were better markers of cancer progression compared to the individual gene products. OF also enables correlating pathway activation with the success of anticancer therapy for individual patients. We further expanded this approach to analyze impact of micro RNAs (miRs) on the regulation of cellular interactome. Many alternative sources provide information about miRs and their targets. However, instruments elucidating higher level impact of the established total miR profiles are still largely missing. A variant of OncoFinder termed MiRImpact enables linking miR expression data with its estimated outcome on the regulation of molecular processes, such as signaling, metabolic, cytoskeleton, and DNA repair pathways. MiRImpact was used to establish cancer-specific and cytomegaloviral infection-linked interactomic signatures for hundreds of molecular pathways. Interestingly, the impact of miRs appeared orthogonal to pathway regulation at the mRNA level, which stresses the importance of combining all available levels of gene regulation to build a more objective molecular model of cell.
我们提出了一种名为OncoFinder（OF）的生物数学方法，该方法能够对细胞内分子途径激活进行定量和定性分析。OF利用一种算法来区分途径中每个基因产物的激活/抑制作用。这种方法适用于在分子水平上分析任何生理、应激、恶性肿瘤和其他情况。OF显示出强大的潜力，可消除使用NGS、微阵列和现代蛋白质组学技术获得的实验基因表达数据之间由背景引起的差异。重要的是，在大多数情况下，与单个基因产物相比，途径激活特征是癌症进展的更好标志物。OF还能够将途径激活与个体患者的抗癌治疗成功情况相关联。我们进一步扩展了这种方法，以分析微小RNA（miR）对细胞相互作用组调控的影响。许多替代来源提供了有关miR及其靶标的信息。然而，仍严重缺乏能够阐明已建立的总miR谱更高水平影响的工具。OncoFinder的一个变体MiRImpact能够将miR表达数据与其对分子过程调控的估计结果联系起来，这些分子过程包括信号传导、代谢、细胞骨架和DNA修复途径等。MiRImpact被用于为数百种分子途径建立癌症特异性和巨细胞病毒感染相关的相互作用组特征。有趣的是，miR的影响在mRNA水平上似乎与途径调控呈正交关系，这强调了结合所有可用的基因调控水平以构建更客观的细胞分子模型的重要性。
