Yao Chen, Li Hongdong, Zhou Chenggui, Zhang Lin, Zou Jinfeng, Guo Zheng
Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu, 610054, China.
BMC Syst Biol. 2010 Nov 9;4:151. doi: 10.1186/1752-0509-4-151.
It has been suggested that, in the human protein-protein interaction network, changes of co-expression between highly connected proteins ("hub") and their interaction neighbours might have important roles in cancer metastasis and be predictive disease signatures for patient outcome. However, for a cancer, such disease signatures identified from different studies have little overlap.
Here, we propose a systemic approach to evaluate the reproducibility of disease signatures at multiple levels, on the basis of some statistically testable biological models. Using two datasets for breast cancer metastasis, we showed that different signature hubs identified from different studies were highly consistent in terms of significantly sharing interaction neighbours and displaying consistent co-expression changes with their overlapping neighbours, whereas the shared interaction neighbours were significantly over-represented with known cancer genes and enriched in pathways deregulated in breast cancer pathogenesis. Then, we showed that the signature hubs identified from the two datasets were highly reproducible at the protein interaction and pathway levels in three other independent datasets.
Our results provide a possible biological model that different signature hubs altered in different patient cohorts could disturb the same pathways associated with cancer metastasis through their interaction neighbours.
有人提出，在人类蛋白质-蛋白质相互作用网络中，高连接性蛋白质（“枢纽”）与其相互作用邻居之间的共表达变化可能在癌症转移中起重要作用，并且是预测患者预后的疾病特征。然而，对于一种癌症，从不同研究中确定的此类疾病特征几乎没有重叠。
在此，我们基于一些可进行统计检验的生物学模型，提出了一种系统方法来在多个层面评估疾病特征的可重复性。使用两个乳腺癌转移数据集，我们表明，从不同研究中确定的不同特征枢纽在显著共享相互作用邻居以及与其重叠邻居显示一致的共表达变化方面高度一致，而共享的相互作用邻居中已知癌症基因显著富集，并且在乳腺癌发病机制中失调的通路中也有富集。然后，我们表明，从这两个数据集中确定的特征枢纽在其他三个独立数据集中的蛋白质相互作用和通路层面具有高度可重复性。
我们的结果提供了一种可能的生物学模型，即不同患者队列中改变的不同特征枢纽可能通过其相互作用邻居干扰与癌症转移相关的相同通路。
