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Open Access Research article

Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis

Chen Yao1, Hongdong Li1, Chenggui Zhou1, Lin Zhang1, Jinfeng Zou1 and Zheng Guo12*

Author Affiliations

1 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

2 Colleges of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China

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BMC Systems Biology 2010, 4:151  doi:10.1186/1752-0509-4-151

Published: 9 November 2010

Abstract

Background

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.

Results

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.

Conclusions

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.