Jeong Hyun-hwan, Kim So, Wee Kyubum, Sohn Kyung-Ah
BMC Syst Biol. 2015;9 Suppl 1(Suppl 1):S8. doi: 10.1186/1752-0509-9-S1-S8. Epub 2015 Jan 21.
Network-based approaches have recently gained considerable popularity in high- dimensional regression settings. For example, the Cox regression model is widely used in expression analysis to predict the survival of patients. However, as the number of genes becomes substantially larger than the number of samples, the traditional Cox or L2-regularized Cox models are still prone to noise and produce unreliable estimations of regression coefficients. A recent approach called the network-based Cox (Net-Cox) model attempts to resolve this issue by incorporating prior gene network information into the Cox regression. The Net-Cox model has shown to outperform the models that do not use this network information.
In this study, we demonstrate an alternative network construction method for the outcome-guided gene interaction network, and we investigate its utility in survival analysis using Net-Cox regression as compared with conventional networks, such as co-expression or static networks obtained from the existing knowledgebase. Our network edges consist of gene pairs that are significantly associated with the clinical outcome. We measure the strength of this association using mutual information between the gene pair and the clinical outcome. We applied this approach to ovarian cancer patients' data in The Cancer Genome Atlas (TCGA) and compared the predictive performance of the proposed approach with those that use other types of networks.
We found that the alternative outcome-guided mutual information network further improved the prediction power of the network-based Cox regression. We expect that a modification of the network regularization term in the Net-Cox model could further improve its prediction power because the properties of our network edges are not optimally reflected in its current form.
基于网络的方法最近在高维回归设置中变得相当流行。例如，Cox回归模型在表达分析中被广泛用于预测患者的生存情况。然而，当基因数量远大于样本数量时，传统的Cox或L2正则化Cox模型仍然容易受到噪声影响，并产生不可靠的回归系数估计。一种最近被称为基于网络的Cox（Net-Cox）模型的方法试图通过将先验基因网络信息纳入Cox回归来解决这个问题。Net-Cox模型已被证明优于不使用此网络信息的模型。
在本研究中，我们展示了一种用于结果引导的基因相互作用网络的替代网络构建方法，并使用Net-Cox回归研究了其在生存分析中的效用，与传统网络（如从现有知识库获得的共表达或静态网络）进行比较。我们的网络边由与临床结果显著相关的基因对组成。我们使用基因对与临床结果之间的互信息来衡量这种关联的强度。我们将此方法应用于癌症基因组图谱（TCGA）中的卵巢癌患者数据，并将所提出方法的预测性能与使用其他类型网络的方法进行比较。
我们发现替代的结果引导互信息网络进一步提高了基于网络的Cox回归的预测能力。我们预计，对Net-Cox模型中的网络正则化项进行修改可以进一步提高其预测能力，因为我们网络边的属性在其当前形式中没有得到最佳反映。
