Wu Jun, Zhao Xiaodong, Lin Zongli, Shao Zhifeng
Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai, China.
Mol Biosyst. 2015 Jul;11(7):1925-32. doi: 10.1039/c5mb00105f.
Gastric cancer is the third leading cause of cancer-related death in the world. Over the past few decades, with the development of high-throughput technologies and the application of various statistical tools, cancer research has witnessed remarkable advancements. However, no system level analysis has taken into account the cancer stages, which are known to be extremely important in prognosis and therapy. In this study, we aimed to carry out a system level analysis of the dynamics of the network structure across the normal phenotype and the four tumor stage phenotypes. We analyzed 276 samples of primary tumor tissues including normal and four tumor stage phenotypes to reveal the dynamics of the five phenotype-specific co-expression networks. Our analysis reveals that the structure of the normal network is dramatically different from that of a tumor network. The analysis of connectivity dynamics shows that hub genes present in the normal network but not in the tumor networks play important roles in tumorigenesis and hub genes unique to a tumor network are enriched in specific biological terms. Moreover, we found three interesting clusters of genes which possess specific dynamic features across the five phenotypes and are enriched in stage-specific biological terms. Integrating the results from the expression analysis and the connectivity analysis shows that the stages of tumor should be taken into consideration and a system level analysis serves as a complement to and a refinement of the traditional expression analysis.
胃癌是全球癌症相关死亡的第三大主要原因。在过去几十年里，随着高通量技术的发展以及各种统计工具的应用，癌症研究取得了显著进展。然而，尚无系统层面的分析考虑到癌症分期，而癌症分期在预后和治疗中极为重要。在本研究中，我们旨在对正常表型和四种肿瘤分期表型的网络结构动态进行系统层面的分析。我们分析了276个原发性肿瘤组织样本，包括正常样本和四种肿瘤分期表型样本，以揭示五个表型特异性共表达网络的动态变化。我们的分析表明，正常网络的结构与肿瘤网络的结构显著不同。连通性动态分析表明，正常网络中存在但肿瘤网络中不存在的枢纽基因在肿瘤发生中起重要作用，而肿瘤网络特有的枢纽基因在特定生物学术语中富集。此外，我们发现了三个有趣的基因簇，它们在五种表型中具有特定的动态特征，并在分期特异性生物学术语中富集。综合表达分析和连通性分析的结果表明，应考虑肿瘤分期，系统层面的分析是对传统表达分析的补充和完善。
