Khunlertgit Navadon, Yoon Byung-Jun
Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843-3128 TX USA.
College of Science, Engineering, and Technology, Hamad Bin Khalifa University (HBKU), Doha, P.O. Box 5825 Qatar.
EURASIP J Bioinform Syst Biol. 2014 Nov 6;2014:19. doi: 10.1186/s13637-014-0019-9. eCollection 2014 Dec.
Accurate prediction of cancer prognosis based on gene expression data is generally difficult, and identifying robust prognostic markers for cancer remains a challenging problem. Recent studies have shown that modular markers, such as pathway markers and subnetwork markers, can provide better snapshots of the underlying biological mechanisms by incorporating additional biological information, thereby leading to more accurate cancer classification.
In this paper, we propose a novel method for simultaneously identifying robust synergistic subnetwork markers that can accurately predict cancer prognosis. The proposed method utilizes an efficient message-passing algorithm called affinity propagation, based on which we identify groups - or subnetworks - of discriminative and synergistic genes, whose protein products are closely located in the protein-protein interaction (PPI) network. Unlike other existing subnetwork marker identification methods, our proposed method can simultaneously identify multiple nonoverlapping subnetwork markers that can synergistically predict cancer prognosis.
Evaluation results based on multiple breast cancer datasets demonstrate that the proposed message-passing approach can identify robust subnetwork markers in the human PPI network, which have higher discriminative power and better reproducibility compared to those identified by previous methods. The identified subnetwork makers can lead to better cancer classifiers with improved overall performance and consistency across independent cancer datasets.
基于基因表达数据准确预测癌症预后通常很困难，识别可靠的癌症预后标志物仍然是一个具有挑战性的问题。最近的研究表明，模块化标志物，如通路标志物和子网标志物，通过纳入额外的生物学信息，可以更好地反映潜在的生物学机制，从而实现更准确的癌症分类。
在本文中，我们提出了一种同时识别能够准确预测癌症预后的强大协同子网标志物的新方法。该方法利用一种名为亲和传播的高效消息传递算法，在此基础上识别出具有判别力和协同作用的基因群（即子网），这些基因的蛋白质产物在蛋白质-蛋白质相互作用（PPI）网络中紧密相邻。与其他现有的子网标志物识别方法不同，我们提出的方法能够同时识别多个可协同预测癌症预后的非重叠子网标志物。
基于多个乳腺癌数据集的评估结果表明，所提出的消息传递方法能够在人类PPI网络中识别出强大的子网标志物，与先前方法识别出的标志物相比，具有更高的判别力和更好的可重复性。所识别出的子网标志物能够带来性能更优的癌症分类器，在独立癌症数据集上具有更好的整体性能和一致性。
