识别生物网络中癌症关键因子的方法研究

Microbiota is abundantly distributed in the host surface and cavity. They are involved in many of the host's normal life activities, but also can destroy the host’s normal gene regulatory network by gene transfer, which may activate oncogene expression and lead to cancer. Thus, the regulation (interaction) between microbes and host genes is an important topic in cancer research. At present, many researchers take effort to establish the corresponding relationship from genotype to phenotype using comparative genomics, but only a few works take advantage of biological networks, mining pathogenic factors of cancer. These network methods are inefficient and the results are incomplete. In order to compensate for this deficiency, we propose to use a novel method to identify risk factors that affect cancers from biological networks. In this project, firstly, we construct host-microbial networks and protein-protein interaction networks by using a variety of omics data. Secondly, we employ methods of network query and network alignment to analyze the hub nodes, driving mechanism and functional orthologs from biological networks, which may guide us to conclude pathogenic microorganisms and pathogenic genes that are involved in the development of cervical carcinoma. Finally, we use transfer annotation and enrichment analysis to predict the role of these key factors in the pathogenesis process. These results may benefit the research of drug design and medical diagnose.

微生物菌群大量分布在宿主的体表和腔道内。它们既参与很多宿主的正常生命活动，也可以通过基因转移的方式破坏宿主正常的基因调控网络，激活致癌基因的表达，导致癌症的发生。因此，微生物与宿主基因之间的调控（交互）关系成为癌症研究的重要话题。目前，大部分癌症研究工作采用基因组比较学的方法建立从基因型到表型的对应关系，而通过生物网络挖掘技术推测癌症致病因子的方法效率低且结果不完整。为了弥补这方面的不足，我们提出了利用新的生物网络分析方法识别影响癌症的关键因子。本项目首先采用多种组学数据构建宿主-微生物网络和蛋白质相互作用网络；其次结合新的网络查询和网络比对方法分析生物网络中的拓扑结构、驱动机制及功能同源蛋白，识别宿主-微生物网络中影响癌症发生、发展的致病微生物和致病基因；最后利用转移注释和富集度分析的方法预测它们在致病过程中所扮演的角色。这些研究结果将有助于提高药物设计和医疗诊断的效果。

人类对复杂疾病的认识仍然非常有限。高通量技术的出现累积了大量的生物数据，包括基因组、转录组、表冠遗传还有大量的电子病历。这些大数据的出现为生物信息学领域的研究者提出了巨大的挑战。菌群和基因是影响宫颈癌疾病的主要因素，本项目主要围绕本问题的7个方面展开研究，包括生物网络中宫颈癌关键因子识别，静态生物网络比对方法，动态生物网络比对算法，转录因子结合位点的识别，单细胞转录组数据融合等。我们首先采用多种组学数据构建宿主-微生物网络和蛋白质相互作用网络；其次结合新的网络查询和网络比对方法分析生物网络中的拓扑结构、驱动机制及功能同源蛋白，识别宿主-微生物网络中影响癌症发生、发展的致病微生物和致病基因；最后利用转移注释和富集度分析的方法预测它们在致病过程中所扮演的角色。在本项目的依托下，我们提出了一系列的理论模型和计算工具，利用多组学数据研究复杂疾病的发生发展机制，为疾病诊断和复杂疾病研究提供了很好的技术支撑。