【摘要】 为了系统地研究卵巢癌病变过程的信号转导模式,综合多种信息构建了完整的人类信号转导网络,发现卵巢癌相关基因在网络中的拓扑学性质与其它基因存在显著差异。将正常、早期卵巢癌和晚期卵巢癌三种状态下的基因表达谱数据映射到信号转导网络中,利用基于共表达改变量的方法,筛选出正常与早期卵巢癌、正常与晚期卵巢癌、早期卵巢癌与晚期卵巢癌间的显著差异模体。通过对显著差异模体进行功能和模式的分析,发现卵巢癌的发生发展主要和信号级联模式的异常相关。另外,还发现AR、PAK6和KPNB1三个基因间信号转导模式的异常和卵巢癌的恶化相关。更多还原

【Abstract】 In order to understand the signal transduction mode of the deterioration of ovarian cancer in a systematic level, a comprehensive human signal transduction network was constructed base on several databases. Topological analysis of ovarian cancer genes showed that they had significantly different topological properties compared with others in the network. Then the authors mapped the gene expression data of ovarian cancer in normal, early and advanced stages to the signal transduction network, and detected significantly changed motifs in normal vs. early, normal vs. advanced, and early vs. advanced stages by using the co-expression changed method. After analyzing the functions and modes of the significantly changed motifs, they found that the development of ovarian cancer was mainly related to the dysfunction of the cascading signal transduction mode, and what is more, the abnormal of the signal transduction among AR, PAK6 and KPNB1 associates with the deterioration of ovarian cancer.更多还原