Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer

Journal of Ovarian Research

Kyung-Ah Sohn¹  Kyubum Wee¹  Sangseob Leem¹  Hyun-hwan Jeong¹ 

[1] Department of Information and Computer Engineering, Ajou University, Suwon 443-749, Republic of Korea

关键词: TCGA;    Survival analysis;    Integrative network analysis;    Outcome-associated gene interaction network;    Mutual information;   

Others  :  1218018
DOI  :  10.1186/s13048-015-0171-1

 received in 2014-12-23, accepted in 2015-06-24,  发布年份 2015

【 摘 要 】

Background

Recent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, the integration of information from multiple molecular profiles adds another challenge to this problem. Recently, network-based approaches have gained huge popularity. However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome.

Methods

We propose a mutual information-based integrative network analysis framework (MINA) that identifies gene pairs associated with clinical outcome and systematically analyses the resulting networks over multiple genomic profiles. We implement an efficient non-parametric testing scheme that ensures the significance of detected gene interactions. We develop a tool named MINA that automates the proposed analysis scheme of identifying outcome-associated gene interactions and generating various networks from those interacting pairs for downstream analysis.

Results

We demonstrate the proposed framework using real data from ovarian cancer patients in The Cancer Genome Atlas (TCGA). Statistically significant gene pairs associated with survival were identified from multiple genomic profiles, which include many individual genes that have weak or no effect on survival. Moreover, we also show that integrated networks, constructed by merging networks from multiple genomic profiles, demonstrate better topological properties and biological significance than individual networks.

Conclusions

We have developed a simple but powerful analysis tool that is able to detect gene-gene interactions associated with clinical outcome on multiple genomic profiles. By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

【 授权许可】

   
2015 Jeong et al.

【 图 表 】

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