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Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer

机译：利用基因表达和DNA甲基化之间相互作用的乳腺癌综合通路生存预测

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Integrative analysis on multi-omics data has gained much attention recently. To investigate the interactive effect of gene expression and DNA methylation on cancer, we propose a directed random walk-based approach on an integrated gene-gene graph that is guided by pathway information. Our approach first extracts a single pathway profile matrix out of the gene expression and DNA methylation data by performing the random walk over the integrated graph. We then apply a denoising autoencoder to the pathway profile to further identify important pathway features and genes. The extracted features are validated in the survival prediction task for breast cancer patients. The results show that the proposed method substantially improves the survival prediction performance compared to that of other pathway-based prediction methods, revealing that the combined effect of gene expression and methylation data is well reflected in the integrated gene-gene graph combined with pathway information. Furthermore, we show that our joint analysis on the methylation features and gene expression profile identifies cancer-specific pathways with genes related to breast cancer. In this study, we proposed a DRW-based method on an integrated gene-gene graph with expression and methylation profiles in order to utilize the interactions between them. The results showed that the constructed integrated gene-gene graph can successfully reflect the combined effect of methylation features on gene expression profiles. We also found that the selected features by DA can effectively extract topologically important pathways and genes specifically related to breast cancer.
机译：最近，对多组学数据的综合分析备受关注。为了研究基因表达和DNA甲基化对癌症的相互作用，我们在通路信息指导的综合基因-基因图上提出了一种基于定向随机游动的方法。我们的方法首先通过在集成图上执行随机游走，从基因表达和DNA甲基化数据中提取单个途径谱矩阵。然后，我们将去噪自动编码器应用于路径配置文件，以进一步识别重要的路径特征和基因。提取的特征在乳腺癌患者的生存预测任务中得到验证。结果表明，与其他基于途径的预测方法相比，该方法显着提高了生存预测性能，表明基因表达和甲基化数据的结合效果很好地反映在结合了途径信息的综合基因-基因图中。此外，我们表明我们对甲基化特征和基因表达谱的联合分析确定了与乳腺癌相关的基因的癌症特异性途径。在这项研究中，我们提出了一种基于DRW的方法，在具有表达和甲基化特征的综合基因-基因图中，以利用它们之间的相互作用。结果表明，构建的整合基因基因图谱可以成功反映甲基化特征对基因表达谱的综合影响。我们还发现DA选择的功能可以有效地提取与乳腺癌特别相关的拓扑重要途径和基因。

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