Khunlertgit Navadon, Yoon Byung-Jun
Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843-3128, TX, USA.
BMC Bioinformatics. 2016 Oct 6;17(Suppl 13):351. doi: 10.1186/s12859-016-1224-1.
Discovering robust markers for cancer prognosis based on gene expression data is an important yet challenging problem in translational bioinformatics. By integrating additional information in biological pathways or a protein-protein interaction (PPI) network, we can find better biomarkers that lead to more accurate and reproducible prognostic predictions. In fact, recent studies have shown that, "modular markers," that integrate multiple genes with potential interactions can improve disease classification and also provide better understanding of the disease mechanisms.
In this work, we propose a novel algorithm for finding robust and effective subnetwork markers that can accurately predict cancer prognosis. To simultaneously discover multiple synergistic subnetwork markers in a human PPI network, we build on our previous work that uses affinity propagation, an efficient clustering algorithm based on a message-passing scheme. Using affinity propagation, we identify potential subnetwork markers that consist of discriminative genes that display coherent expression patterns and whose protein products are closely located on the PPI network. Furthermore, we incorporate the topological information from the PPI network to evaluate the potential of a given set of proteins to be involved in a functional module. Primarily, we adopt widely made assumptions that densely connected subnetworks may likely be potential functional modules and that proteins that are not directly connected but interact with similar sets of other proteins may share similar functionalities.
Incorporating topological attributes based on these assumptions can enhance the prediction of potential subnetwork markers. We evaluate the performance of the proposed subnetwork marker identification method by performing classification experiments using multiple independent breast cancer gene expression datasets and PPI networks. We show that our method leads to the discovery of robust subnetwork markers that can improve cancer classification.
基于基因表达数据发现用于癌症预后的可靠标志物是转化生物信息学中一个重要但具有挑战性的问题。通过整合生物通路或蛋白质 - 蛋白质相互作用（PPI）网络中的其他信息，我们可以找到更好的生物标志物，从而实现更准确和可重复的预后预测。事实上，最近的研究表明，整合多个具有潜在相互作用基因的“模块化标志物”可以改善疾病分类，并能更好地理解疾病机制。
在这项工作中，我们提出了一种新颖的算法来寻找能够准确预测癌症预后的稳健且有效的子网标志物。为了在人类PPI网络中同时发现多个协同的子网标志物，我们基于之前使用亲和传播的工作进行拓展，亲和传播是一种基于消息传递方案的高效聚类算法。利用亲和传播，我们识别出由具有连贯表达模式的判别性基因组成的潜在子网标志物，这些基因的蛋白质产物在PPI网络上紧密相邻。此外，我们纳入PPI网络的拓扑信息来评估给定蛋白质集参与功能模块的潜力。主要地，我们采用广泛认可的假设，即紧密连接的子网可能是潜在的功能模块，并且那些不直接相连但与相似的其他蛋白质集相互作用的蛋白质可能具有相似的功能。
基于这些假设纳入拓扑属性可以增强对潜在子网标志物的预测。我们通过使用多个独立的乳腺癌基因表达数据集和PPI网络进行分类实验，评估了所提出的子网标志物识别方法的性能。我们表明，我们的方法能够发现可改善癌症分类的稳健子网标志物。
