Identifying CpG sites with different differential methylation frequencies in colorectal cancer tissues based on individualized differential methylation analysis
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Haidan Yan1,*, Jun He1,*, Qingzhou Guan1, Hao Cai1, Lin Zhang4, Weicheng Zheng1, Lishuang Qi2, Suyun Zhang3, Huaping Liu1, Hongdong Li1, Wenyuan Zhao2, Sheng Yang3 and Zheng Guo1,2
1Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China
2Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
3Department of Medical Oncology, Fujian Medical University Union Hospital, Fuzhou, China
4Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
*These authors contributed equally to this work
Zheng Guo, email: email@example.com
Sheng Yang, email: firstname.lastname@example.org
Keywords: colorectal cancer, DNA methylation, relative methylation level orderings, differentially methylated CpG sites, biomarkers
Received: September 23, 2016 Accepted: April 21, 2017 Published: May 07, 2017
A big challenge to clinical diagnosis and therapy of colorectal cancer (CRC) is its extreme heterogeneity, and thus it would be of special importance if we could find common biomarkers besides subtype-specific biomarkers for CRC. Here, with DNA methylation data produced by different laboratories, we firstly revealed that the relative methylation-level orderings (RMOs) of CpG sites within colorectal normal tissues are highly stable but widely disrupted in the CRC tissues. This finding provides the basis for using the RankComp algorithm to identify differentially methylated (DM) CpG sites in every individual CRC sample through comparing the RMOs within the individual sample with the stable RMOs predetermined in normal tissues. For 75 CRC samples, RankComp detected averagely 4,062 DM CpG sites per sample and reached an average precision of 91.34% in terms that the hypermethylation or hypomethylation states of the DM CpG sites detected for each cancer sample were consistent with the observed differences between this cancer sample and its paired adjacent normal sample. Finally, we applied RankComp to identify DM CpG sites for each of the 268 CRC samples from The Cancer Genome Atlas and found 26 and 143 genes whose promoter regions included CpG sites that were hypermethylated and hypomethylated, respectively, in more than 95% of the 268 CRC samples. Individualized pathway analysis identified six pathways that were significantly enriched with DM genes in more than 90% of the CRC tissues. These universal DNA methylation biomarkers could be important diagnostic makers and therapy targets for CRC.
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