Harbin Institute of Technology, School of Computer Science and Technology, Harbin, 150001, P.R. China.
Heilongjiang Bayi Agricultural University, College of Science, Daqing, 163319, P.R. China.
Sci Rep. 2019 Aug 14;9(1):11853. doi: 10.1038/s41598-019-48372-1.
Pathway analysis allows us to gain insights into a comprehensive understanding of the molecular mechanisms underlying cancers. Currently, high-throughput multi-omics data and various types of large-scale biological networks enable us to identify cancer-related pathways by comprehensively analyzing these data. Combining information from multidimensional data, pathway databases and interaction networks is a promising strategy to identify cancer-related pathways. Here we present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to extend original pathways. The results show that the extension of original pathways can provide a basis for discovering new components of the original pathway and understanding the crosstalk between pathways in a large-scale biological network. By inputting the gene lists of the extended pathways into the classical gene set analysis (ORA and FCS), we effectively identified the altered pathways which are correlated well with the corresponding cancer. The method is evaluated on three datasets retrieved from TCGA (BRCA, LUAD and COAD). The results show that the integration of DNA methylation and gene expression data through a network of known gene interactions is effective in identifying altered pathways.
通路分析使我们能够深入了解癌症相关的分子机制。目前，高通量多组学数据和各种类型的大规模生物网络使我们能够通过综合分析这些数据来识别与癌症相关的通路。整合多维数据、通路数据库和相互作用网络的信息是识别癌症相关通路的一种很有前途的策略。在这里，我们提出了一种新的基于网络的方法，用于整合分析 DNA 甲基化和基因表达数据，以扩展原始通路。结果表明，原始通路的扩展可以为发现原始通路的新组成部分以及理解大规模生物网络中通路之间的相互作用提供基础。通过将扩展后的通路的基因列表输入到经典的基因集分析（ORA 和 FCS）中，我们有效地识别了与相应癌症相关的改变通路。该方法在从 TCGA 检索到的三个数据集（BRCA、LUAD 和 COAD）上进行了评估。结果表明，通过已知基因相互作用网络整合 DNA 甲基化和基因表达数据，可有效地识别改变的通路。
