BACKGROUND Integrating the rich information from multi-omics data has been a popular approach to survival prediction and bio-marker identification for several cancer studies. To facilitate the integrative analysis of multiple genomic profiles, several studies have suggested utilizing pathway information rather than using individual genomic profiles. METHODS We have recently proposed an integrative directed random walk-based method utilizing pathway information (iDRW) for more robust and effective genomic feature extraction. In this study, we applied iDRW to multiple genomic profiles for two different cancers, and designed a directed gene-gene graph which reflects the interaction between gene expression and copy number data. In the experiments, the performances of the iDRW method and four state-of-the-art pathway-based methods were compared using a survival prediction model which classifies samples into two survival groups. RESULTS The results show that the integrative analysis guided by pathway information not only improves prediction performance, but also provides better biological insights into the top pathways and genes prioritized by the model in both the neuroblastoma and the breast cancer datasets. The pathways and genes selected by the iDRW method were shown to be related to the corresponding cancers. CONCLUSIONS In this study, we demonstrated the effectiveness of a directed random walk-based multi-omics data integration method applied to gene expression and copy number data for both breast cancer and neuroblastoma datasets. We revamped a directed gene-gene graph considering the impact of copy number variation on gene expression and redefined the weight initialization and gene-scoring method. The benchmark result for iDRW with four pathway-based methods demonstrated that the iDRW method improved survival prediction performance and jointly identified cancer-related pathways and genes for two different cancer datasets. REVIEWERS This article was reviewed by Helena Molina-Abril and Marta Hidalgo.

中文翻译：

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基于稳健的基于通路的多组学数据集成，使用定向随机游走进行多项癌症研究中的生存预测。

背景技术从多组学数据中整合丰富的信息已经成为用于几种癌症研究的生存预测和生物标志物鉴定的流行方法。为了促进对多个基因组图谱的综合分析，一些研究建议利用途径信息而不是使用单个基因组图谱。方法我们最近提出了一种利用路径信息（iDRW）的综合定向随机游动法，以进行更健壮和有效的基因组特征提取。在这项研究中，我们将iDRW应用于两种不同癌症的多个基因组图谱，并设计了定向基因-基因图，该图反映了基因表达与拷贝数数据之间的相互作用。在实验中 使用生存预测模型（将样本分为两个生存组）比较了iDRW方法和四种基于最新途径的方法的性能。结果结果表明，以通路信息为指导的综合分析不仅改善了预测性能，而且还为神经母细胞瘤和乳腺癌数据集中的模型确定的主要通路和基因提供了更好的生物学见解。通过iDRW方法选择的途径和基因显示与相应的癌症相关。结论在这项研究中，我们证明了有针对性的基于定向行走的多组学数据整合方法的有效性，该方法适用于乳腺癌和神经母细胞瘤数据集的基因表达和拷贝数数据。考虑到拷贝数变异对基因表达的影响，我们修订了定向基因-基因图，并重新定义了权重初始化和基因评分方法。使用四种基于途径的方法对iDRW进行的基准测试结果表明，iDRW方法改善了生存预测性能，并共同确定了两个不同癌症数据集的癌症相关途径和基因。审阅者本文由Helena Molina-Abril和Marta Hidalgo审阅。