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Open Access Highly Accessed Research article

Identifying dysregulated pathways in cancers from pathway interaction networks

Ke-Qin Liu124, Zhi-Ping Liu3, Jin-Kao Hao4*, Luonan Chen135* and Xing-Ming Zhao1*

Author Affiliations

1 Institute of Systems Biology, Shanghai University, Shanghai, 200444, China

2 School of Communication and Information Engineering, Shanghai University, Shanghai, 200072, China

3 Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

4 LERIA, University of Angers, 2 Boulevard Lavoisier, 49045, Angers Cedex 01, France

5 Institute of Industrial Science, University of Tokyo, Tokyo, 153-8505, Japan

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BMC Bioinformatics 2012, 13:126  doi:10.1186/1471-2105-13-126

Published: 7 June 2012

Abstract

Background

Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of pathways. Identifying biomarkers that can characterize cancers would help to understand and diagnose cancers. Traditional computational methods that detect genes differentially expressed between cancer and normal samples fail to work due to small sample size and independent assumption among genes. On the other hand, genes work in concert to perform their functions. Therefore, it is expected that dysregulated pathways will serve as better biomarkers compared with single genes.

Results

In this paper, we propose a novel approach to identify dysregulated pathways in cancer based on a pathway interaction network. Our contribution is three-fold. Firstly, we present a new method to construct pathway interaction network based on gene expression, protein-protein interactions and cellular pathways. Secondly, the identification of dysregulated pathways in cancer is treated as a feature selection problem, which is biologically reasonable and easy to interpret. Thirdly, the dysregulated pathways are identified as subnetworks from the pathway interaction networks, where the subnetworks characterize very well the functional dependency or crosstalk between pathways. The benchmarking results on several distinct cancer datasets demonstrate that our method can obtain more reliable and accurate results compared with existing state of the art methods. Further functional analysis and independent literature evidence also confirm that our identified potential pathogenic pathways are biologically reasonable, indicating the effectiveness of our method.

Conclusions

Dysregulated pathways can serve as better biomarkers compared with single genes. In this work, by utilizing pathway interaction networks and gene expression data, we propose a novel approach that effectively identifies dysregulated pathways, which can not only be used as biomarkers to diagnose cancers but also serve as potential drug targets in the future.