Liu Xingyi, Yang Bin, Huang Xinpeng, Yan Wenying, Zhang Yujuan, Hu Guang
Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, Jiangsu, China.
Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou, 215123, Jiangsu, China.
Interdiscip Sci. 2023 Dec;15(4):525-541. doi: 10.1007/s12539-023-00568-w. Epub 2023 Apr 28.
Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.
复杂疾病通常由生物网络紊乱和/或多个基因突变引起。不同疾病状态之间网络拓扑结构的比较可以突出其动态过程中的关键因素。在此,我们提出一种差异模块分析方法,该方法将蛋白质-蛋白质相互作用与基因表达谱整合用于模块分析,并引入模块间边和日期枢纽来识别量化显著表型变异的“核心网络模块”。然后,基于这个核心网络模块,通过拓扑-功能连接分数和结构建模预测关键因素,包括功能性蛋白质-蛋白质相互作用、信号通路和驱动突变。我们应用此方法分析乳腺癌中的淋巴结转移(LNM)过程。功能富集分析表明,模块间边和日期枢纽在癌症转移和侵袭以及转移特征中都发挥着重要作用。结构突变分析表明,乳腺癌的LNM可能是转染期间重排(RET)原癌基因相关相互作用功能障碍以及通过RET的变构突变导致的非经典钙信号通路的结果。我们相信,所提出的方法可以为癌症转移等疾病进展提供新的见解。