生物分子网络分析在癌症标志物发现和基因网络演化机制探索中的应用

发布时间：2019-03-31 09:16

【摘要】：基于生物分子网络分析的系统生物学研究策略是当前生物学研究的主流范式。生物技术的迅猛发展和由此产生的海量生物学数据,以及系统生物学研究手段的日趋成熟,使得基于生物分子网络分析的系统生物学研究成为可能,并在生物学研究的各个领域中得以广泛应用。本论文中,我将详细介绍生物分子网络分析在癌症生物学和演化生物学中的研究应用工作。早期的诊断发现对癌症的预防和治疗起着至关重要的作用,因而准确有效的诊断标志物的鉴定具有极其重要的意义。这里,通过整合mi RNA和m RNA的基因表达谱信息,以及mi RNA-m RNA调控网络的拓扑学信息,我们开发了一个可用于预测癌症诊断mi RNA生物标志物的生物信息学算法,并采用Java和R两种计算机语言对该算法进行了计算机程序实现。随后,我们成功将该算法应用于前列腺癌诊断mi RNA生物标志物的鉴定中,后续的低通量实验以及多种系统生物学分析证实了预测结果的可靠性。通过对算法的完善更新,我们将该预测算法延伸应用到了包括肾透明细胞癌在内的多种癌症的研究分析中,并取得了较为理想的研究结果。基因互作网络的演化过程研究是演化生物学中的一个重要问题,同时也是我们研究生物表型的演化乃至物种的起源等问题的重要手段。本研究中,我们从新基因的角度来探索了哺乳动物(人和小鼠)中基因互作网络的演化模式。我们发现新基因加入原有基因互作网络是一个时间依赖型的演化过程:随着基因年龄的增长,基因逐渐获得更多的连通边,从而自基因互作网络的边缘区域逐渐进入网络核心部分。与新基因加入原有基因互作网络过程相一致,我们发现新基因的功能演化同样是一个时间依赖型的过程。随着基因年龄的增长,基因逐步获得多效性生物功能以及机体必需功能。通过结合基因表达信息,基因互作信息以及文献数据,我们鉴定得到了4个可能和大脑发育功能相关的人类特有的Hub基因。最后,我们详细探讨了驱动基因互作网络演化的多种潜在的机制。
[Abstract]:The strategy of system biology research based on biomolecular network analysis is the mainstream paradigm of current biological research. The rapid development of biotechnology and the resulting mass of biological data, as well as the increasing maturity of research methods in system biology, make it possible to study systems biology based on biomolecular network analysis. It has been widely used in various fields of biological research. In this paper, I will introduce in detail the application of biomolecular network analysis in cancer biology and evolutionary biology. Early diagnosis plays an important role in the prevention and treatment of cancer, so the accurate and effective identification of diagnostic markers is of great significance. Here, by integrating the gene expression profiles of mi RNA and m-RNA, as well as the topological information of mi RNA-m RNA regulatory networks, we have developed a bioinformatics algorithm that can be used to predict biomarkers of mi RNA in cancer diagnosis. Java and R are used to realize the algorithm. Subsequently, we have successfully applied this algorithm to the identification of mi RNA biomarkers for prostate cancer diagnosis. Subsequent low-throughput experiments and a variety of systems biological analysis have confirmed the reliability of the prediction results. By updating the algorithm, we extend the prediction algorithm to the research and analysis of many kinds of cancers, including renal clear cell carcinoma, and obtain satisfactory results. The study of evolution process of gene interaction network is an important problem in evolutionary biology, and it is also an important means to study the evolution of biological phenotype and even the origin of species. In this study, we explored the evolutionary pattern of gene interaction networks in mammals (human and mouse) from the perspective of new genes. We found that the addition of new genes to the existing gene interaction network is a time-dependent evolution process: as the gene grows older, the gene gradually gains more connected edges. Thus, the edge region of the self-gene interaction network gradually enters the core part of the network. It is found that the functional evolution of the new gene is also a time-dependent process, consistent with the process of adding the new gene to the original gene interaction network. With the increase of gene age, genes gradually obtain multiple biological functions as well as essential functions of the body. By combining gene expression information, gene interaction information and literature data, we identified four human-specific Hub genes that may be related to brain development. Finally, we discuss in detail many potential mechanisms that drive the evolution of gene interaction networks.
【学位授予单位】：苏州大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：R730.4

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