Integrating Multiple Datasets to Discover Stage-Specific Cancer Related Genes and Stage-Specific Pathways

Investigating the evolution of complex diseases through different disease stages is critical for understanding the root cause of these diseases, which is fundamental for their accurate prognosis and effective treatment. There have been numerous studies that have identified many single genes, static modules and individual pathways related cancer progression, but few attempt has been developed to identify specific genes and pathways interactions related individual disease stages via data integration. To address these issues, we have proposed a general working flow, to reveal disease stages dynamics by joint analysis of multi-level datasets. Our contribution is two-fold. Firstly, we present a classical regression method to identify stage-specific cancer genes, where the gene expression and DNA methylation datasets are integrated. Secondly, we construct a pathway evolution network, which considered interactions among specific mapped pathways and their overlapped genes. Interestingly, the potential discovered biological functions from this network together with the common bridges and genes, not only help us to understand the functional evolution and dynamics of complex diseases in a more deep fashion, but also useful for clinical management to design customized drugs with more effective therapy.