Systematic tracking of coordinated differential network motifs identifies novel disease-related genes by integrating multiple data

• A new network approach is proposed to identify disease genes.
• The network motifs with significant characteristics are as the skeleton for analysis.
• The change patterns of genes of coordinated differential motifs are identified.

Recently, one of the most hotspots in system biology is exploring the disease pathogenesis by integrating different omics data. A lot of methods are developed to identify disease genes for an indepth understanding of a given disease or a biological process. However, most of them do not sufficiently consider the relationship between epigenetic and expressional changes in deregulated genes. Here, we propose a network based approach to identify disease related genes by properly combining the network topological characteristic and the biological characteristic. Our approach identifies network motifs with coordinated changed pattern, differential-methylation and differential-expression, in the context of a human signaling network by integrating DNA methylation and gene expression data. For validation, we do experiments by using colorectal cancer data sets, the results show that the classification performance of our approach outperforms the existing method. The screened network motifs and predicted genes are almost epigenetically deregulated, which are highly associated with colorectal cancer development. Furthermore, functional enrichment analysis reveals that the functions they enriched in are hallmarks of cancer. We not only provide a method for identification of disease related genes but also add a new perspective to integrate heterogeneous data and mine subgraph with significant biological characteristics pattern.