Abstract
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.
Highlights
Cancer etiology and progression is currently understood to be driven primarily by molecular and genetic mechanisms[1,2]
One hypothesis of the proposed method is that the genetic interactions are variables between controls and cases which is responsible for different phenotypes varying in cancer
Genes of two extended pathways under different phenotypes are eventually united as a final extended pathway gene set
Summary
Cancer etiology and progression is currently understood to be driven primarily by molecular and genetic mechanisms[1,2]. Glaab et al.[13] present a methodology for extending original pathways by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners These methods only consider network topologies and ignore edge weights of large-scale networks when extending pathways. It has a limitation to extract different types of biological entities in the context of biological knowledge This method only employs the pathway topology itself. We present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to calculate edge weights of the large-scale network for each phenotype. By inputting the gene lists of extended pathways into the classical gene set analysis (ORA and FCS), we identify altered pathways which are correlated well with the corresponding cancer.
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