基于系统生物学整合技术挖掘结直肠癌形成中的核心通路和驱动基因

目的 探索结直肠癌(Colorectal carcinoma, CRC)形成中的核心通路和关键基因；方法 利用Meta分析技术从以往5项CRC发生相关转录组学研究中筛选癌及癌旁差异表达基因；ComBat方法合并5项研究的癌组织基因表达谱数据，针对上述差异表达基因用PCIT软件进行共表达网络构建；CFinder软件揭示该共表达网络中存在的核心亚网络，并用Gather软件确定主要核心亚网络所富集的生物学功能；以重要核心亚网络（或通路）为重点，联合节点基因在CRC中的表达变化方向和相应染色体区域扩增或缺失的信息，发现亚网络功能形成中的候选驱动基因。结果 Meta分析转录组学研究共发现差异表达基因2073个，其中在5项研究中癌组织一致上调1174个，一致下调899个，这些基因在CRC样本中形成的共表达网络包括798个基因节点和1462条边，存在22个核心亚网络；最大的核心亚网络由77个基因和436条边组成，功能涉及细胞周期和增殖信号调控，UBE2C、MYBL2、FAM83D、AURKA、TPX2等11个基因被预测为该信号功能的驱动基因。结论 细胞周期和增殖信号通路是结直肠癌发生中的核心通路，UBE2C和AURKA等11个基因是该核心通路的驱动基因。

Integrated genomic analysis-based identification of core pathways and driver genes associated with colorectal carcinoma

Objective To investigate the core pathways and driver genes associated with development of colorectal carcinoma (CRC). Methods Meta-analysis was employed to screen differently expressed genes between CRC and the adjacent normal mucosa in 5 studies. ComBat was used to combine the gene data of the 5 studies and then the differently expressed genes were used to construct a stable co-expression network in CRC using PCIT software. CFinder software was used to extract the core sub-networks of the constructed coexpression network, and Gather software was used for functional enrichment analysis of the core subnetworks. Finally, those genes with up-regulation and DNA gain in CRC or down-regulation and DNA loss were identified as driver genes. Results Our meta-analysis found 2 073 differently expressed genes between CRC and the adjacent normal mucosa, including 1 174 up-regulated and 899 down-regulated in 5 the CRC studies. A coexpression network was constructed with those differently expressed genes; it had 798 nodes, 1 462 edges, and 22 core subnetworks. The largest sub-network consisted 77 genes and 436 edges, and the function mainly involved cell cycle and proliferation regulation, with the driver genes including UBE2C, MYBL2, FAM83D, AURKA and TPX2. Conclusion Cell cycle and proliferation pathways are the core ones in the development of CRC, and 11 genes including UBE2C and AURKA are the driver genes of the pathways.