Wong Yung-Hao, Chen Ru-Hong, Chen Bor-Sen
Lab of Control and Systems Biology, Department of Electrical Engineering National Tsing Hua University, Hsinchu 30013, Taiwan.
J Theor Biol. 2014 Dec 7;362:17-34. doi: 10.1016/j.jtbi.2014.05.045. Epub 2014 Jul 9.
Cancer is the leading cause of death worldwide and is generally caused by mutations in multiple proteins or the dysregulation of pathways. Understanding the causes and the underlying carcinogenic mechanisms can help fight this disease. In this study, a systems biology approach was used to construct the protein-protein interaction (PPI) networks of four cancers and the non-cancers by their corresponding microarray data, PPI modeling and database-mining. By comparing PPI networks between cancer and non-cancer samples to find significant proteins with large PPI changes during carcinogenesis process, core and specific network markers were identified by the intersection and difference of significant proteins, respectively, with carcinogenesis relevance values (CRVs) for each cancer. A total of 28 significant proteins were identified as core network markers in the carcinogenesis of four types of cancer, two of which are novel cancer-related proteins (e.g., UBC and PSMA3). Moreover, seven crucial common pathways were found among these cancers based on their core network markers, and some specific pathways were particularly prominent based on the specific network markers of different cancers (e.g., the RIG-I-like receptor pathway in bladder cancer, the proteasome pathway and TCR pathway in liver cancer, and the HR pathway in lung cancer). Additional validation of these network markers using the literature and new tested datasets could strengthen our findings and confirm the proposed method. From these core and specific network markers, we could not only gain an insight into crucial common and specific pathways in the carcinogenesis, but also obtain a high promising PPI target for cancer therapy.
癌症是全球主要死因，通常由多种蛋白质的突变或信号通路失调引起。了解其病因和潜在致癌机制有助于对抗这种疾病。在本研究中，采用系统生物学方法，通过相应的微阵列数据、蛋白质 - 蛋白质相互作用（PPI）建模和数据库挖掘，构建四种癌症及非癌症组织的PPI网络。通过比较癌症与非癌症样本的PPI网络，找出在致癌过程中具有显著PPI变化的重要蛋白质，分别通过重要蛋白质的交集和差异，并结合每种癌症的致癌相关值（CRV），确定核心网络标志物和特异性网络标志物。在四种癌症的致癌过程中，共鉴定出28种重要蛋白质作为核心网络标志物，其中两种是新的癌症相关蛋白质（如泛素结合酶E2C和蛋白酶体亚基α型3）。此外，基于这些核心网络标志物，在这些癌症中发现了七条关键的共同信号通路，并且基于不同癌症的特异性网络标志物，一些特定信号通路尤为突出（如膀胱癌中的视黄酸诱导基因I样受体信号通路、肝癌中的蛋白酶体信号通路和T细胞受体信号通路以及肺癌中的同源重组信号通路）。利用文献和新的测试数据集对这些网络标志物进行进一步验证，可强化我们的研究结果并证实所提出的方法。从这些核心和特异性网络标志物中，我们不仅可以深入了解致癌过程中的关键共同和特异性信号通路，还能获得极具潜力的癌症治疗PPI靶点。
