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      Genome-wide methylation analysis of human colon cancer reveals similar hypo- and hypermethylation at conserved tissue-specific CpG island shores

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          Alterations in DNA methylation (DNAm) in cancer have been known for 25 years, including hypomethylation of oncogenes and hypermethylation of tumor suppressor genes 1. 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 2, 3. 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 surprising overlap (45-65%) of the location 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 non-colon normal tissues. Thus, methylation changes in cancer are at sites that vary normally in tissue differentiation, and they are consistent with the epigenetic progenitor model of cancer 4, that epigenetic alterations affecting tissue-specific differentiation are the predominant mechanism by which epigenetic changes cause cancer.

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          Most cited references 27

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

           K Livak,  T Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

            In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.
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              Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome.

              To gain insight into the function of DNA methylation at cis-regulatory regions and its impact on gene expression, we measured methylation, RNA polymerase occupancy and histone modifications at 16,000 promoters in primary human somatic and germline cells. We find CpG-poor promoters hypermethylated in somatic cells, which does not preclude their activity. This methylation is present in male gametes and results in evolutionary loss of CpG dinucleotides, as measured by divergence between humans and primates. In contrast, strong CpG island promoters are mostly unmethylated, even when inactive. Weak CpG island promoters are distinct, as they are preferential targets for de novo methylation in somatic cells. Notably, most germline-specific genes are methylated in somatic cells, suggesting additional functional selection. These results show that promoter sequence and gene function are major predictors of promoter methylation states. Moreover, we observe that inactive unmethylated CpG island promoters show elevated levels of dimethylation of Lys4 of histone H3, suggesting that this chromatin mark may protect DNA from methylation.

                Author and article information

                Nat Genet
                Nature genetics
                17 November 2008
                18 January 2009
                February 2009
                19 August 2009
                : 41
                : 2
                : 178-186
                [1 ]Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
                [2 ]Center for Epigenetics, Institute for Basic Biomedical Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
                [3 ]Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
                [4 ]Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
                [5 ]Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
                [6 ]Center for Statistical Sciences, Brown University, Providence, RI
                [7 ]Stanley Laboratory of Brain Research, Uniform Services University of Health Sciences, Bethesda, MD 20892, USA
                Author notes

                Equal contribution from these authors

                Author contributions R.I. and A.P.F. designed the study and interpreted the results; R.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.P.; P.O. and H.C. performed functional assays; A.P.F. supervised the laboratory experiments, and wrote the paper with R.I. and C.L.-A.

                †To whom correspondence should be addressed. Email: rafa@ and afeinberg@
                Author Information Reprints and permission information is available at These authors declare no competing financial interests. Correspondence and requests for materials should be addressed to R.I ( rafa@ ) and A.P.F. ( afeinberg@ ).
                Funded by: National Cancer Institute : NCI
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R37 CA054358-19 ||CA
                Funded by: National Cancer Institute : NCI
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 RR021967-02 ||RR
                Funded by: National Cancer Institute : NCI
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 GM083084-02 ||GM



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