Topologically inferring pathway activity for precise survival outcome prediction: breast cancer as a case

Accurately predicting the survival outcome of patients is of great importance in clinical cancer research. In the past decade, building survival prediction models based on gene expression data has received increasing interest. However, the existing methods are mainly based on individual gene signatu...

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Bibliographic Details
Published in:Molecular bioSystems 2017-02, Vol.13 (3), p.537-548
Main Authors: Liu, Wei, Wang, Wei, Tian, Guohua, Xie, Wenming, Lei, Li, Liu, Jiujin, Huang, Wanxun, Xu, Liyan, Li, Enmin
Format: Article
Language:English
Subjects:
Algorithms
Biomarkers, Tumor
Breast Neoplasms - genetics
Breast Neoplasms - metabolism
Breast Neoplasms - mortality
Cluster Analysis
Computational Biology - methods
Databases, Genetic
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Kaplan-Meier Estimate
Models, Biological
Prognosis
Protein Interaction Mapping - methods
Reproducibility of Results
Signal Transduction
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Summary:Accurately predicting the survival outcome of patients is of great importance in clinical cancer research. In the past decade, building survival prediction models based on gene expression data has received increasing interest. However, the existing methods are mainly based on individual gene signatures, which are known to have limited prediction accuracy on independent datasets and unclear biological relevance. Here, we propose a novel pathway-based survival prediction method called DRWPSurv in order to accurately predict survival outcome. DRWPSurv integrates gene expression profiles and prior gene interaction information to topologically infer survival associated pathway activities, and uses the pathway activities as features to construct Lasso-Cox model. It uses topological importance of genes evaluated by directed random walk to enhance the robustness of pathway activities and thereby improve the predictive performance. We applied DRWPSurv on three independent breast cancer datasets and compared the predictive performance with a traditional gene-based method and four pathway-based methods. Results showed that pathway-based methods obtained comparable or better predictive performance than the gene-based method, whereas DRWPSurv could predict survival outcome with better accuracy and robustness among the pathway-based methods. In addition, the risk pathways identified by DRWPSurv provide biologically informative models for breast cancer prognosis and treatment.
ISSN:1742-206X
1742-2051
DOI:10.1039/c6mb00757k