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A novel algorithm for network-based prediction of cancer recurrence

机译：一种新型癌症复发预测的新算法

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To develop accurate prognostic models is one of the biggest challenges in “omics”-based cancer research. Here, we propose a novel computational method for identifying dysregulated gene subnetworks as biomarkers to predict cancer recurrence. Applying our method to the DNA methylome of endometrial cancer patients, we identified a subnetwork consisting of differentially methylated (DM) genes, and non-differentially methylated genes, termed Epigenetic Connectors (EC), that are topologically important for connecting the DM genes in a protein-protein interaction network. The ECs are statistically significantly enriched in well-known tumorgenesis and metastasis pathways, and include known epigenetic regulators. Importantly, combining the DMs and ECs as features using a novel random walk procedure, we constructed a support vector machine classifier that significantly improved the prediction accuracy of cancer recurrence and outperformed several alternative methods, demonstrating the effectiveness of our network-based approach.
机译：发展准确的预后模型是基于“OMICS”的癌症研究中最大的挑战之一。在这里，我们提出了一种新的计算方法，用于鉴定鉴定失调的基因子网作为生物标志物，以预测癌症复发。将方法应用于子宫内膜癌症患者的DNA甲基杂物，我们鉴定了由差异甲基化（DM）基因的子网和非差异甲基化基因称为脑膜遗传连接器（EC），其对于连接DM基因在A中是拓扑上重要的重要性蛋白质蛋白质相互作用网络。 ECS在众所周知的肿瘤间致肿瘤内和转移途径中具有统计学上显着富集，并包括已知的表观遗传调节剂。重要的是，将DMS和ECS与使用新型随机步行程序相结合的特征，我们构建了一种支持向量机分类器，可显着提高癌症复发和优于几种替代方法的预测准确性，展示了基于网络的方法的有效性。

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