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Nucleic Acids Res. 2016 Dec 15;44(22):e164. Epub 2016 Sep 4.

Personalized characterization of diseases using sample-specific networks.

Liu X1,2, Wang Y1,3, Ji H4,5, Aihara K6, Chen L7,2,5.

Author information

1
Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
2
Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan.
3
University of Chinese Academy of Sciences, Beijing 100049, China.
4
Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China hbji@sibcb.ac.cn.
5
School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China.
6
Institute of Industrial Science, University of Tokyo, Tokyo 153-8505, Japan aihara@sat.t.u-tokyo.ac.jp.
7
Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China lnchen@sibs.ac.cn.

Abstract

A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

PMID:
27596597
PMCID:
PMC5159538
DOI:
10.1093/nar/gkw772
[Indexed for MEDLINE]
Free PMC Article

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