A simple knowledge-based mining method for exploring hidden key molecules in a human biomolecular network

Network-based analysis for identification of candidate genes for colorectal cancer progression

Although high-throughput biological technologies have been producing a vast amount of multi-omics data regarding cancer genomics and several disease susceptible genes have been reported, many of these genes are likely to be irrelevant for the cancer process because only one feature of the tumor pathway could be focused on. By identifying 'CpG core', which was extracted from CpG sites in genomic DNA by our newly developed method, we performed integrated analysis using gene expression and DNA methylation profiles of 116 colorectal cancer samples. First, based on gene expression values, colorectal cancer samples were divided into three clusters (Cluster-1, -2, and -3) by k-means clustering. The 5-year overall survival rates of colorectal cancer patients were 74.8%, 29.2%, and 29.4% in Cluster-1, -2, and -3, respectively, and the prognosis of Cluster-2 was significantly poorer than that of the other two clusters owing to liver metastasis (P < 0.001). Second, each cluster was divided into two subgroups based on methylation status, and the 5-year overall survival rate of Cluster-1H (36.8%) was significantly shorter than that of Cluster-1L (96.1%) due to the accumulation of aberrant DNA methylation (P = 0.014). Third, network-based analysis using expression and methylation profiles demonstrated that nucleoporin family genes were downregulated in Cluster-2 and that the PTX3 gene was highly methylated in Cluster-1H. These combined data indicate that integrated analysis can identify disease characteristics that would be missed using single comprehensive analysis, and that multiple pathways would play pivotal roles in the liver metastasis of colorectal cancer.