Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores


For the past 25 years, it has been known that alterations in DNA methylation (DNAm) occur in cancer, including hypomethylation of oncogenes and hypermethylation of tumor suppressor genes. However, most studies of cancer methylation have assumed that functionally important DNAm will occur in promoters, and that most DNAm changes in cancer occur in CpG islands. Here we show that most methylation alterations in colon cancer occur not in promoters, and also not in CpG islands, but in sequences up to 2 kb distant, which we term 'CpG island shores'. CpG island shore methylation was strongly related to gene expression, and it was highly conserved in mouse, discriminating tissue types regardless of species of origin. There was a notable overlap (45–65%) of the locations of colon cancer–related methylation changes with those that distinguished normal tissues, with hypermethylation enriched closer to the associated CpG islands, and hypomethylation enriched further from the associated CpG island and resembling that of noncolon normal tissues. Thus, methylation changes in cancer are at sites that vary normally in tissue differentiation, consistent with the epigenetic progenitor model of cancer, which proposes that epigenetic alterations affecting tissue-specific differentiation are the predominant mechanism by which epigenetic changes cause cancer.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Most tissue-specific differential DNA methylation is located at CpG island shores.
Figure 2: Distribution of distance of T-DMRs and C-DMRs from CpG islands.
Figure 3: Similar numbers of sites of hypomethylation and hypermethylation in colon cancer.
Figure 4: Gene expression is strongly correlated with T-DMRs at CpG island shores.
Figure 5: Genes downregulated in association with T-DMR shore hypermethylation are activated by 5-aza-2′-deoxycytidine treatment of colon cancer cell line HCT116 and knockout of DNA methyltrasferase 1 and 3b in HCT116.
Figure 6: Clustering of human tissue samples using mouse T-DMRs results in perfect discrimination of tissues.
Figure 7: Clustering of normal tissue samples using C-DMRs results in perfect discrimination of tissues.
Figure 8: Magnitude of differential methylation and variation in C-DMRs and T-DMRs.

Accession codes


Gene Expression Omnibus


  1. Feinberg, A.P. & Vogelstein, B. Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature 301, 89–92 (1983).

    Article  CAS  PubMed  Google Scholar 

  2. Feinberg, A.P. & Tycko, B. The history of cancer epigenetics. Nat. Rev. Cancer 4, 143–153 (2004).

    Article  CAS  PubMed  Google Scholar 

  3. Baylin, S.B. & Ohm, J.E. Epigenetic gene silencing in cancer - a mechanism for early oncogenic pathway addiction? Nat. Rev. Cancer 6, 107–116 (2006).

    Article  CAS  PubMed  Google Scholar 

  4. Irizarry, R.A. et al. Comprehensive high-throughput arrays for relative methylation (CHARM). Genome Res. 18, 771–779 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Gardiner-Garden, M. & Frommer, M. CpG islands in vertebrate genomes. J. Mol. Biol. 196, 261–282 (1987).

    Article  CAS  PubMed  Google Scholar 

  6. Eckhardt, F. et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat. Genet. 38, 1378–1385 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Weber, M. et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat. Genet. 39, 457–466 (2007).

    Article  CAS  PubMed  Google Scholar 

  8. Illingworth, R. et al. A novel CpG island set identifies tissue-specific methylation at developmental gene loci. PLoS Biol. 6, e22 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Shen, L. et al. Genome-wide profiling of DNA methylation reveals a class of normally methylated CpG island promoters. PLoS Genet. 3, 2023–2036 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Antequera, F. & Bird, A. CpG islands as genomic footprints of promoters that are associated with replication origins. Curr. Biol. 9, R661–R667 (1999).

    Article  CAS  PubMed  Google Scholar 

  11. Ehrlich, M. DNA methylation in cancer: too much, but also too little. Oncogene 21, 5400–5413 (2002).

    Article  CAS  PubMed  Google Scholar 

  12. Hong, C. et al. Epigenome scans and cancer genome sequencing converge on WNK2, a kinase-independent suppressor of cell growth. Proc. Natl. Acad. Sci. USA 104, 10974–10979 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Strathdee, G. et al. Inactivation of HOXA genes by hypermethylation in myeloid and lymphoid malignancy is frequent and associated with poor prognosis. Clin. Cancer Res. 13, 5048–5055 (2007).

    Article  CAS  PubMed  Google Scholar 

  14. Cantor, A.B. et al. Antagonism of FOG-1 and GATA factors in fate choice for the mast cell lineage. J. Exp. Med. 205, 611–624 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Segditsas, S. et al. Putative direct and indirect Wnt targets identified through consistent gene expression changes in APC-mutant intestinal adenomas from humans and mice. Hum. Mol. Genet. 17, 3864–3875 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Jaeger, J. et al. Gene expression signatures for tumor progression, tumor subtype, and tumor thickness in laser-microdissected melanoma tissues. Clin. Cancer Res. 13, 806–815 (2007).

    Article  CAS  PubMed  Google Scholar 

  17. Seal, S. et al. Truncating mutations in the Fanconi anemia J gene BRIP1 are low-penetrance breast cancer susceptibility alleles. Nat. Genet. 38, 1239–1241 (2006).

    Article  CAS  PubMed  Google Scholar 

  18. Pedersen, I.S. et al. Frequent loss of imprinting of PEG1/MEST in invasive breast cancer. Cancer Res. 59, 5449–5451 (1999).

    CAS  PubMed  Google Scholar 

  19. Gius, D. et al. Distinct effects on gene expression of chemical and genetic manipulation of the cancer epigenome revealed by a multimodality approach. Cancer Cell 6, 361–371 (2004).

    Article  CAS  PubMed  Google Scholar 

  20. Shiraki, T. et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage. Proc. Natl. Acad. Sci. USA 100, 15776–15781 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wakaguri, H., Yamashita, R., Suzuki, Y., Sugano, S. & Nakai, K. DBTSS: database of transcription start sites, progress report 2008. Nucleic Acids Res. 36, D97–D101 (2008).

    Article  CAS  PubMed  Google Scholar 

  22. Glass, J.L. et al. CG dinucleotide clustering is a species-specific property of the genome. Nucleic Acids Res. 35, 6798–6807 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Feinberg, A.P., Ohlsson, R. & Henikoff, S. The epigenetic progenitor origin of human cancer. Nat. Rev. Genet. 7, 21–33 (2006).

    Article  CAS  PubMed  Google Scholar 

  24. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Statist Soc B (Methodol.) 57, 289–300 (1995).

    Google Scholar 

  25. Storey, J.D. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann. Statist. 31, 2013–2035 (2003).

    Article  Google Scholar 

  26. Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  PubMed  Google Scholar 

  27. Bullmore, E.T. et al. Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Trans. Med. Imaging 18, 32–42 (1999).

    Article  CAS  PubMed  Google Scholar 

  28. Efron, B., Tibshirani, R., Storey, J.D. & Tusher, V. Empirical Bayes analysis of a microarray experiment. J. Am. Stat. Assoc. 96, 1151–1160 (2001).

    Article  Google Scholar 

  29. Tost, J. & Gut, I.G. DNA methylation analysis by pyrosequencing. Nat. Protocols 2, 2265–2275 (2007).

    Article  CAS  PubMed  Google Scholar 

  30. Higgs, B.W. et al. An online database for brain disease research. BMC Genomics 7, 70 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  PubMed  Google Scholar 

  32. Livak, K.J. & Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)). Methods 25, 402–408 (2001).

    Article  CAS  PubMed  Google Scholar 

  33. Gentleman, R.C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).

    Article  CAS  PubMed  Google Scholar 

Download references


We thank B. Volgelstein (Johns Hopkins University School of Medicine) for providing colon tumors and matched normal mucosa samples. Postmortem brain, liver and spleen tissue was donated by The Stanley Medical Research Institute collection courtesy of M.B. Knable, E.F. Torrey and R.H. Yolken, whom we also thank for making available gene expression data for the brain and the liver tissue. We thank B. Carvalho for help with statistical software and C. Crainiceanu for advice with statistical methods. This work was supported by US National Institutes of Health grants P50HG003233 (A.P.F.), R37CA54358 (A.P.F.) and 5R01RR021967 (R.A.I.).

Author information

Authors and Affiliations



R.A.I. and A.P.F. designed the study and interpreted the results; R.A.I. designed new CHARM arrays and statistical methods with Z.W.; C.L.-A. performed bisulfite pyrosequencing, real-time quantitative PCR and sample preparation with C.M., K.G., M.R. and H.J.; B.W. and S.S. performed CHARM assays with sample preparation from M.W. and advice from J.B.P.; P.O. and H.C. performed functional assays; A.P.F. supervised the laboratory experiments and wrote the paper with R.A.I. and C.L.-A.

Corresponding authors

Correspondence to Rafael A Irizarry or Andrew P Feinberg.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Tables 1–7 and Supplementary Methods (PDF 541 kb)

Supplementary Data 1

Excel file of T-DMRs (XLS 3629 kb)

Supplementary Data 2

Excel file of C-DMRs (XLS 696 kb)

Supplementary Data 3

Gene expression data from 5-aza-2′ deoxycytidine/DKO experiments and gene expression data for genes associated with T-DMRs (XLS 40 kb)

Supplementary Data 4

Excel file of C-DMRs that are also T-DMRs; and C-DMRs that distinguish all the tumors from normal (XLS 284 kb)

Supplementary Data 5

Mus musculus T-DMRs (XLS 3273 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Irizarry, R., Ladd-Acosta, C., Wen, B. et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet 41, 178–186 (2009).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing