基于基因功能模块解析癌基因及其功能协同
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摘要
在生物系统中,基因编码的蛋白质通过相互作用以模块化的方式执行功能。基于基因功能模块研究癌基因以及癌机制,使我们能够从一个更全局、系统的角度解析癌基因及其功能协同关系,进而探索癌症发生与发展的复杂机制。
本文主要包括以下三部分内容:
1.基于共进化基因功能模块研究癌基因的保守模式。功能相关的蛋白质通过相互作用以模块化的方式发挥功能。这种发挥功能的方式可能对蛋白质进化的速度和模式产生影响。本论文中,我们首先在人类蛋白互作网络中搜索共进化的蛋白互作子网。在一个给定的共进化水平下,我们选择具有非随机大小的蛋白互作子网,定义为共进化基因功能模块。结果显示,模块内蛋白倾向于保守,起源于更加古老的物种,富集在各种组织中广泛表达的管家基因编码的蛋白。而模块外蛋白质保守性较差,起源于较年轻的物种,并富集在特定组织中表达的基因编码的蛋白。基于共进化基因功能模块进一步研究癌基因的保守模式,我们发现癌基因整体上的保守性主要是由编码富集在模块内的蛋白的癌基因导致的,而编码模块外蛋白的癌基因保守性相对较弱。因此,模块内外的癌基因编码蛋白在癌症发生过程中可能发挥不同的作用,为我们进一步研究癌机制提供了重要线索。
2.在癌基因组中发现共突变基因以及候选癌基因。基因在癌症样本中发生共突变的非随机性为基因突变的功能协同提供了重要信息。在目前的癌基因组体细胞突变扫查研究中,检测的样本量较小而假设检验的规模极大,使得发现共突变基因对的统计效能很低。基于两套癌基因组体细胞突变扫查数据,我们提出基于分层错误发现率控制策略发现共突变基因与候选癌基因的方法。与通过预筛选突变频率较高的基因的方法相比,该方法可以更加有效地发现共突变基因对与候选癌基因。
3.基于细胞定位信息发现癌症相关功能模块及其相互作用。Gene Ontology(GO)的一个生物学过程结点包含的基因可能涉及到发生在不同细胞位置的子生物学过程,即使参与同一个过程中的基因也可能由于位于不同的细胞位置而显示出不同的表达模式。因此,在本论文中,我们应用我们提出的结合生物学过程和细胞定位信息的方法,发现癌相关功能模块。对两套癌症表达数据的应用结果显示,控制相同的错误发现率,结合细胞定位信息可以发现更多并且更加细致的癌症相关生物学过程。基于蛋白互作和基因表达数据构建的癌症相关功能模块网络,为我们进一步理解癌症发生过程中模块化的协同机制提供了线索。
综上,本论文基于基因功能模块从序列保守性、体细胞突变和基因表达水平三个层面解析了癌基因及其功能协同关系。
In biological system, proteins encoded by genes perform functions by interacting with each other in a modular fashion. Studying cancer genes and cancer mechanism based on gene functional modules provides us with the opportunity to dissect the cancer genes and their functional cooperation and explore cancer mechanism from a more global and systematical perspective.
The main contributions are as follows:
1. Studying the evolutionary conservation of cancer genes based on co-evolving gene functional modules. Functionally related proteins tend to perform functions by interacting with each other in a modular fashion, which may affect both the mode and tempo of their evolution. In this dissertation, we firstly searched for co-evolving protein protein interacting (PPI) subnetworks in the human PPI network. Identified at a given co-evolving level, we selected the subnetworks with non-randomly large sizes and defined them as co-evolving gene functional modules. Our results showed that proteins within modules tend to be conserved, evolutionarily old and enriched with proteins encoded by housekeeping genes expressed in all tissues, while proteins outside modules tend to be less-conserved, evolutionarily younger and enriched with proteins encoded by genes expressed in specific tissues. Further studying the evolutionary conservation of cancer genes based on co-evolving gene functional modules, we found that the overall conservation of cancer genes should be mainly attributed to the cancer genes encoding proteins enriched in the conserved modules. The cancer genes encoding proteins outside modules are relatively less conserved. Thus, cancer genes encoding proteins within and outside modules might play different roles in carcinogenesis, providing a new hint for studying the mechanism of cancer.
2. Find co-mutated genes and candidate cancer genes in cancer genomes. The non-randomness of the co-mutation of genes in cancer samples can provide important information on the functional cooperation of gene mutations. In the current high-throughput somatic mutation screening studies, due to the relatively small sample sizes used and the extraordinary large-scale hypothesis tests, the statistical power of finding co-mutated gene pairs is very low. Based on two datasets of somatic mutations in cancer genomes, we proposed a stratified false discovery rate control approach for identifying significantly co-mutated gene pairs and candidate cancer genes. Compared with the approach of pre-selecting genes with higher mutation frequencies, many more co-mutated gene pairs and candidate cancer genes could be found by a stratified false discovery rate control strategy.
3. Identifying cancer related functional modules and their interactions based on cellular location information. A Gene Ontology (GO) biological process category may encompass the genes involved in distinct processes occurring in different cellular compartments. Furthermore, the genes even within a same process may show a clear expression distinction with respect to their cellular localizations. Therefore, in this dissertation, we identified cancer related functional modules by the approach proposed by us that integrating both biological process and cellular component information. Applying this method to two cancer expression datasets, the results showed that controlling the same false discovery rate level, more and specific cancer related processes could be found by using the cellular location information. The modular network constructed by connecting pairs of cancer related signature functional modules based on PPI and gene expression data suggests a clue for further exploring the synergic action of the signature functional modules during tumorigenesis.
In conclusion, based on gene functional modules, we dissected cancer genes and their functional cooperation from the aspects of sequence conservation, somatic mutation and gene expression in this dissertation.
本文主要包括以下三部分内容:
1.基于共进化基因功能模块研究癌基因的保守模式。功能相关的蛋白质通过相互作用以模块化的方式发挥功能。这种发挥功能的方式可能对蛋白质进化的速度和模式产生影响。本论文中,我们首先在人类蛋白互作网络中搜索共进化的蛋白互作子网。在一个给定的共进化水平下,我们选择具有非随机大小的蛋白互作子网,定义为共进化基因功能模块。结果显示,模块内蛋白倾向于保守,起源于更加古老的物种,富集在各种组织中广泛表达的管家基因编码的蛋白。而模块外蛋白质保守性较差,起源于较年轻的物种,并富集在特定组织中表达的基因编码的蛋白。基于共进化基因功能模块进一步研究癌基因的保守模式,我们发现癌基因整体上的保守性主要是由编码富集在模块内的蛋白的癌基因导致的,而编码模块外蛋白的癌基因保守性相对较弱。因此,模块内外的癌基因编码蛋白在癌症发生过程中可能发挥不同的作用,为我们进一步研究癌机制提供了重要线索。
2.在癌基因组中发现共突变基因以及候选癌基因。基因在癌症样本中发生共突变的非随机性为基因突变的功能协同提供了重要信息。在目前的癌基因组体细胞突变扫查研究中,检测的样本量较小而假设检验的规模极大,使得发现共突变基因对的统计效能很低。基于两套癌基因组体细胞突变扫查数据,我们提出基于分层错误发现率控制策略发现共突变基因与候选癌基因的方法。与通过预筛选突变频率较高的基因的方法相比,该方法可以更加有效地发现共突变基因对与候选癌基因。
3.基于细胞定位信息发现癌症相关功能模块及其相互作用。Gene Ontology(GO)的一个生物学过程结点包含的基因可能涉及到发生在不同细胞位置的子生物学过程,即使参与同一个过程中的基因也可能由于位于不同的细胞位置而显示出不同的表达模式。因此,在本论文中,我们应用我们提出的结合生物学过程和细胞定位信息的方法,发现癌相关功能模块。对两套癌症表达数据的应用结果显示,控制相同的错误发现率,结合细胞定位信息可以发现更多并且更加细致的癌症相关生物学过程。基于蛋白互作和基因表达数据构建的癌症相关功能模块网络,为我们进一步理解癌症发生过程中模块化的协同机制提供了线索。
综上,本论文基于基因功能模块从序列保守性、体细胞突变和基因表达水平三个层面解析了癌基因及其功能协同关系。
In biological system, proteins encoded by genes perform functions by interacting with each other in a modular fashion. Studying cancer genes and cancer mechanism based on gene functional modules provides us with the opportunity to dissect the cancer genes and their functional cooperation and explore cancer mechanism from a more global and systematical perspective.
The main contributions are as follows:
1. Studying the evolutionary conservation of cancer genes based on co-evolving gene functional modules. Functionally related proteins tend to perform functions by interacting with each other in a modular fashion, which may affect both the mode and tempo of their evolution. In this dissertation, we firstly searched for co-evolving protein protein interacting (PPI) subnetworks in the human PPI network. Identified at a given co-evolving level, we selected the subnetworks with non-randomly large sizes and defined them as co-evolving gene functional modules. Our results showed that proteins within modules tend to be conserved, evolutionarily old and enriched with proteins encoded by housekeeping genes expressed in all tissues, while proteins outside modules tend to be less-conserved, evolutionarily younger and enriched with proteins encoded by genes expressed in specific tissues. Further studying the evolutionary conservation of cancer genes based on co-evolving gene functional modules, we found that the overall conservation of cancer genes should be mainly attributed to the cancer genes encoding proteins enriched in the conserved modules. The cancer genes encoding proteins outside modules are relatively less conserved. Thus, cancer genes encoding proteins within and outside modules might play different roles in carcinogenesis, providing a new hint for studying the mechanism of cancer.
2. Find co-mutated genes and candidate cancer genes in cancer genomes. The non-randomness of the co-mutation of genes in cancer samples can provide important information on the functional cooperation of gene mutations. In the current high-throughput somatic mutation screening studies, due to the relatively small sample sizes used and the extraordinary large-scale hypothesis tests, the statistical power of finding co-mutated gene pairs is very low. Based on two datasets of somatic mutations in cancer genomes, we proposed a stratified false discovery rate control approach for identifying significantly co-mutated gene pairs and candidate cancer genes. Compared with the approach of pre-selecting genes with higher mutation frequencies, many more co-mutated gene pairs and candidate cancer genes could be found by a stratified false discovery rate control strategy.
3. Identifying cancer related functional modules and their interactions based on cellular location information. A Gene Ontology (GO) biological process category may encompass the genes involved in distinct processes occurring in different cellular compartments. Furthermore, the genes even within a same process may show a clear expression distinction with respect to their cellular localizations. Therefore, in this dissertation, we identified cancer related functional modules by the approach proposed by us that integrating both biological process and cellular component information. Applying this method to two cancer expression datasets, the results showed that controlling the same false discovery rate level, more and specific cancer related processes could be found by using the cellular location information. The modular network constructed by connecting pairs of cancer related signature functional modules based on PPI and gene expression data suggests a clue for further exploring the synergic action of the signature functional modules during tumorigenesis.
In conclusion, based on gene functional modules, we dissected cancer genes and their functional cooperation from the aspects of sequence conservation, somatic mutation and gene expression in this dissertation.
引文
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