Abstract
Abstract In colorectal cancer (CRC) care, treatment decisions depend on efforts to estimate the metastatic potential of tumors. The liver is one of the most common metastatic sites of CRC and prognosis of CRC patients often reflects metastases to distant sites. However, the molecular mechanisms for further development of CRC with metastasis to the liver, lung, brain, bone and lymph nodes are unknown. The aim of this study was to stratify patients based on DNA methylation status and identify not one biomarker gene, but functional motif for metastatic potential of CRC. Using Illumina HumanMethylation 27 harboring approximately 27,000 CpG loci, we performed genome-wide methylation analysis for 74 CRC samples including 36 patients with liver metastasis and 38 without any distant metastasis. As a control, their corresponding colorectal mucosa was used. Consistent with our previous data, using non-negative matrix factorization and consensus clustering analysis, samples were clustered into three groups; high-, intermediate-, and low-methylation epigenotypes. Next, tumor samples were classified based on gene expression profile in each methylation cluster. Gene Set Enrichment Analysis was applied for expression data, which could identify that Rhodopsin kinase was activated in CRC samples with liver metastasis. Then genes included in this kinase were analyzed by Network Hidden Key molecule miner (NetHike) algorithm. NetHike, which we recently developed as network analysis, demonstrated that CXCR4 was one of key molecule for liver metastasis of CRC. In NetHike algorithm, several information such as methylation status and gene expression was inputted which would be integrated on known signaling pathways. In CXCR4 and its neighbors as a functional motif of CRC metastasis, CXCL12 was methylated without gene expression aberration. On the other hand, CXCR4, CCR5, and ARRB2 were not methylated, of which expression was increased, suggesting that integration and mapping of some gene information effectively identify activated gene pathways in some clusters, and may be missed only by conventional analyses. Taken together, network-based analysis could find the potential functional motif and integration of various gene expression aberrations and methylation status also could stratify CRC patients with liver metastasis and without metastasis. We plan to make an exhaustive exploration to discover the some novel functional topologies that have both expression and methylation aberrations and might be related to the mechanism of CRC metastasis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4930. doi:1538-7445.AM2012-4930
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