Changes in correlation between promoter methylation and gene expression in cancer

BackgroundMethylation of high-density CpG regions known as CpG Islands (CGIs) has been widely described as a mechanism associated with gene expression regulation. Aberrant promoter methylation is considered a hallmark of cancer involved in silencing of tumor suppressor genes and activation of oncogenes. However, recent studies have also challenged the simple model of gene expression control by promoter methylation in cancer, and the precise mechanism of and role played by changes in DNA methylation in carcinogenesis remains elusive.ResultsUsing a large dataset of 672 matched cancerous and healthy methylomes, gene expression, and copy number profiles accross 3 types of tissues from The Cancer Genome Atlas (TCGA), we perform a detailed meta-analysis to clarify the interplay between promoter methylation and gene expression in normal and cancer samples. On the one hand, we recover the existence of a CpG island methylator phenotype (CIMP) with prognostic value in a subset of breast, colon and lung cancer samples, where a common subset of promoter CGIs hypomethylated in normal samples become hypermethylated. However, this hypermethylation is not accompanied by a decrease in expression of the corresponding genes, which are already lowly expressed in the normal genes. On the other hand, we identify tissue-specific sets of genes, different between normal and cancer samples, whose inter-individual variation in expression is significantly correlated with the variation in methylation of the 3’ flanking regions of the promoter CGIs. These subsets of genes are not the same in the different tissues, nor between normal and cancerous samples, but transcription factors are over-represented in all subsets.ConclusionOur results suggest that epigenetic reprogramming in cancer does not contribute to cancer development via direct inhibition of gene expression through promoter hypermethylation. It may instead modify how the expression of a few specific genes, particularly transcription factors, are associated with DNA methylation variations in a tissue-dependent manner.

[1]  Trevor J. Hastie,et al.  Regression Analysis of Multiple Protein Structures , 1998, J. Comput. Biol..

[2]  M. Ringnér,et al.  DNA methylation analyses of urothelial carcinoma reveal distinct epigenetic subtypes and an association between gene copy number and methylation status , 2012, Epigenetics.

[3]  M. Ehrlich,et al.  DNA methylation in cancer: too much, but also too little , 2002, Oncogene.

[4]  A. Reymond,et al.  Copy number variants, diseases and gene expression. , 2009, Human molecular genetics.

[5]  F. Taroni,et al.  Mitochondrial ferritin limits oxidative damage regulating mitochondrial iron availability: hypothesis for a protective role in Friedreich ataxia. , 2008, Human molecular genetics.

[6]  J. Newell-Price,et al.  DNA Methylation and Silencing of Gene Expression , 2000, Trends in Endocrinology & Metabolism.

[7]  Chia-Lin Wei,et al.  Dynamic changes in the human methylome during differentiation. , 2010, Genome research.

[8]  Edith Heard,et al.  Recent advances in X-chromosome inactivation research. , 2012, Current opinion in cell biology.

[9]  K. Gunderson,et al.  High density DNA methylation array with single CpG site resolution. , 2011, Genomics.

[10]  B. Langmead,et al.  BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions , 2012, Genome Biology.

[11]  T. Mikkelsen,et al.  Genome-scale DNA methylation maps of pluripotent and differentiated cells , 2008, Nature.

[12]  Hui Liu,et al.  AnimalTFDB: a comprehensive animal transcription factor database , 2011, Nucleic Acids Res..

[13]  Isabelle Guyon,et al.  A Stability Based Method for Discovering Structure in Clustered Data , 2001, Pacific Symposium on Biocomputing.

[14]  I. Simon,et al.  Evidence for an instructive mechanism of de novo methylation in cancer cells , 2006, Nature Genetics.

[15]  J. Mesirov,et al.  GenePattern 2.0 , 2006, Nature Genetics.

[16]  Li Yu,et al.  [DNA methylation and cancer]. , 2005, Zhonghua nei ke za zhi.

[17]  Rudolf Jaenisch,et al.  Role for DNA methylation in genomic imprinting , 1993, Nature.

[18]  N. Heintz,et al.  The Nuclear DNA Base 5-Hydroxymethylcytosine Is Present in Purkinje Neurons and the Brain , 2009, Science.

[19]  Xia Li,et al.  QDMR: a quantitative method for identification of differentially methylated regions by entropy , 2011, Nucleic acids research.

[20]  J. Martín-Subero,et al.  Intragenic DNA methylation in transcriptional regulation, normal differentiation and cancer. , 2013, Biochimica et biophysica acta.

[21]  Suresh Venkatasubramanian,et al.  Curve Matching, Time Warping, and Light Fields: New Algorithms for Computing Similarity between Curves , 2007, Journal of Mathematical Imaging and Vision.

[22]  D. Cox,et al.  Analysis of Survival Data. , 1986 .

[23]  Dietrich Rebholz-Schuhmann,et al.  UKPMC: a full text article resource for the life sciences , 2011, Nucleic Acids Res..

[24]  B. Williams,et al.  Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.

[25]  Colm E. Nestor,et al.  Tissue of origin determines cancer-associated CpG island promoter hypermethylation patterns , 2012, Genome Biology.

[26]  R. Redon,et al.  Relative Impact of Nucleotide and Copy Number Variation on Gene Expression Phenotypes , 2007, Science.

[27]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[28]  Sören Lehmann,et al.  Differential methylation in CN-AML preferentially targets non-CGI regions and is dictated by DNMT3A mutational status and associated with predominant hypomethylation of HOX genes , 2014, Epigenetics.

[29]  Nita Ahuja,et al.  The CpG island methylator phenotype: what's in a name? , 2013, Cancer research.

[30]  Margaret R. Karagas,et al.  Copy number variation has little impact on bead-array-based measures of DNA methylation , 2009, Bioinform..

[31]  Eamonn J. Keogh,et al.  Scaling up Dynamic Time Warping to Massive Dataset , 1999, PKDD.

[32]  Juan M. Vaquerizas,et al.  A census of human transcription factors: function, expression and evolution , 2009, Nature Reviews Genetics.

[33]  A. E. Hoerl,et al.  Ridge Regression: Applications to Nonorthogonal Problems , 1970 .

[34]  Rafael A. Irizarry,et al.  Genome-wide methylation analysis of human colon cancer reveals similar hypo- and hypermethylation at conserved tissue-specific CpG island shores , 2008, Nature Genetics.

[35]  Kairong Cui,et al.  Intragenic DNA methylation modulates alternative splicing by recruiting MeCP2 to promote exon recognition , 2013, Cell Research.

[36]  C. Iacobuzio-Donahue,et al.  Global 5-hydroxymethylcytosine content is significantly reduced in tissue stem/progenitor cell compartments and in human cancers , 2011, Oncotarget.

[37]  Zachary D. Smith,et al.  DNA methylation: roles in mammalian development , 2013, Nature Reviews Genetics.

[38]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[39]  R. Meehan,et al.  Enzymatic approaches and bisulfite sequencing cannot distinguish between 5-methylcytosine and 5-hydroxymethylcytosine in DNA. , 2010, BioTechniques.

[40]  B. Ramsahoye,et al.  Transcriptionally repressed genes become aberrantly methylated and distinguish tumors of different lineages in breast cancer , 2011, Proceedings of the National Academy of Sciences.

[41]  P. Vertino DNA methylation in cancer , 2011 .

[42]  Winston Timp,et al.  Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors , 2014, Genome Medicine.

[43]  G. Schackert,et al.  5-Hydroxymethylcytosine is strongly depleted in human cancers but its levels do not correlate with IDH1 mutations. , 2011, Cancer research.

[44]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[45]  Lee E. Edsall,et al.  Human DNA methylomes at base resolution show widespread epigenomic differences , 2009, Nature.

[46]  Nathan D. VanderKraats,et al.  Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes , 2013, Nucleic acids research.

[47]  R. Meehan,et al.  DNA methylation reprogramming in cancer: Does it act by re-configuring the binding landscape of Polycomb repressive complexes? , 2013, BioEssays : news and reviews in molecular, cellular and developmental biology.

[48]  S. Advani CpG Island Methylator Phenotype in Colorectal Cancer , 2017 .

[49]  J. Herman,et al.  Analysis of Promoter CpG Island Hypermethylation in Cancer: Location, Location, Location! , 2011, Clinical Cancer Research.

[50]  R. Meehan,et al.  Genomic insights into cancer-associated aberrant CpG island hypermethylation , 2013, Briefings in functional genomics.

[51]  Richard G. F. Visser,et al.  Comparison of Regularized Regression Methods for ~Omics Data , 2012 .

[52]  R. Jaenisch,et al.  DNA methylation and cancer. , 1994, Human molecular genetics.

[53]  Timothy E. Reddy,et al.  Dynamic DNA methylation across diverse human cell lines and tissues , 2013, Genome research.

[54]  Marc Berthod,et al.  Subpixel contour matching using continuous dynamic programming , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[55]  K. Struhl Fundamentally Different Logic of Gene Regulation in Eukaryotes and Prokaryotes , 1999, Cell.

[56]  Guangchuang Yu,et al.  clusterProfiler: an R package for comparing biological themes among gene clusters. , 2012, Omics : a journal of integrative biology.

[57]  M. Esteller CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future , 2002, Oncogene.

[58]  Andrew P. Feinberg,et al.  Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host , 2013, Nature Reviews Cancer.

[59]  A. Feinberg,et al.  Genome-wide methylation analysis of human colon cancer reveals similar hypo- and hypermethylation at conserved tissue-specific CpG island shores , 2008, Nature Genetics.