Liu Wei, Wang Wei, Tian Guohua, Xie Wenming, Lei Li, Liu Jiujin, Huang Wanxun, Xu Liyan, Li Enmin
The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou, 515041, China.
Department of Mathematics, Heilongjiang Institute of Technology, Harbin, 150050, China.
Mol Biosyst. 2017 Feb 28;13(3):537-548. doi: 10.1039/c6mb00757k.
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.
在临床癌症研究中，准确预测患者的生存结果至关重要。在过去十年中，基于基因表达数据构建生存预测模型受到了越来越多的关注。然而，现有方法主要基于单个基因特征，已知其在独立数据集上的预测准确性有限且生物学相关性不明确。在此，我们提出一种名为DRWPSurv的基于通路的新型生存预测方法，以便准确预测生存结果。DRWPSurv整合基因表达谱和先前的基因相互作用信息，以拓扑方式推断与生存相关的通路活性，并将通路活性用作特征来构建套索 - 考克斯模型。它使用通过有向随机游走评估的基因拓扑重要性来增强通路活性的稳健性，从而提高预测性能。我们将DRWPSurv应用于三个独立的乳腺癌数据集，并将预测性能与传统的基于基因的方法和四种基于通路的方法进行比较。结果表明，基于通路的方法比基于基因的方法获得了相当或更好的预测性能，而在基于通路的方法中，DRWPSurv能够以更高的准确性和稳健性预测生存结果。此外，DRWPSurv识别出的风险通路为乳腺癌的预后和治疗提供了具有生物学信息的模型。
