Changes in correlation between promoter methylation and gene expression in cancer

BMC Genomics

Volumn 16, Issue 1, 2015, Pages

  Moarii, Matahi ^a,b,c   Boeva, Valentina ^a,b,c   Vert, Jean Philippe ^a,b,c   Reyal, Fabien ^b  

^a PSL RESEARCH UNIVERSITY   (France)

^b INSTITUT CURIE   (France)

^c INSERM   (France)

Author keywords

Cancer; CpG Island methylator phenotype; Epigenetic regulation

Indexed keywords

ARTICLE; CANCER PROGNOSIS; CANCER SURVIVAL; CARCINOGENESIS; CONTROLLED STUDY; COPY NUMBER VARIATION; CPG ISLAND; DNA FLANKING REGION; DNA METHYLATION; EPIGENETICS; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENETIC ASSOCIATION; HUMAN; HUMAN TISSUE; MAJOR CLINICAL STUDY; NEOPLASM; NUCLEAR REPROGRAMMING; ONCOGENE; OVERALL SURVIVAL; PREDICTIVE VALUE; PROMOTER REGION; SURVIVAL PREDICTION; TRANSCRIPTION REGULATION; TUMOR SUPPRESSOR GENE; GENETICS;

DNA METHYLATION; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; NEOPLASMS; PROMOTER REGIONS, GENETIC;

EID: 84945558568     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-015-1994-2     Document Type: Article

References (56)

1. Newell-Price J, Clark AJL, King P. DNA Methylation and Silencing of Gene Expression. Trends Endocrinol Metab. 2000; 11(4):142-8.
2. Laurent L, Wong E, Li G, Huynh T, Tsirigos A, Ong CT, et al. Dynamic changes in the human methylome during differentiation. Genome Res. 2010; 20(3):320-1.
3. Smith ZD, Meissner A. DNA methylation: roles in mammalian development. Nat Rev Genet. 2013; 14(3):204-0.
4. Pollex T, Heard E. Recent advances in X-chromosome inactivation research. Curr Opin Cell Biol. 2012; 24(6):825-32.
5. Li E, Beard C, Jaenisch R. Role for DNA methylation in genomic imprinting. Nature. 1993; 366:362-5.
6. Esteller M. CpG island hypermethylation and tumor suppressor genes: a booming present, a brighter future. Oncogene. 2002; 21(35):5427-40.
7. Ehrlich M. DNA methylation in cancer: too much, but also too little. Oncogene. 2002; 21:5400-413.
8. Laird PW, Jaenisch R. DNA methylation and cancer. Hum Mol Genet. 1994; 3:1487-95.
9. Das PM, Singal R. DNA methylation and cancer. J Clin Oncol. 2004; 22(22):4632-2.
10. Kulis M, Esteller M. DNA methylation and cancer. Adv Genet. 2010; 70(10):27-56.
11. Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, et al. Genome-wide methylation analysis of human colon cancer reveals similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet. 2009; 41(2):178-86.
12. Qu Y, Lennartsson A, Gaidzik VI, Deneberg S, Karimi M, 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. Epigenetics. 2014; 9(8):1108-19.
13. Meissner A, Mikkelsen TS, Gu H, Wernig M, Sivachenko A, Zhang X, et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature. 2008; 454(7205):766-70.
14. Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009; 462(7271):315-22.
15. Zhang Y, Liu H, Lv J, Xiao X, Zhu J, Liu X, et al. QDMR: a quantitative method for identification of differentially methylated regions by entropy. Nucleic Acids Res. 2011; 39(9):58.
16. Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 2012; 13(10):83.
17. Varley KE, Gertz J, Bowling KM, Parker SL, Reddy TE, Pauli-Behn F, et al. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res. 2013; 23(3):555-67.
18. Keshet I, Schlesinger Y, Farkash S, Rand E, Hecht M, Segal E, et al. Evidence for an instructive mechanism of de novo methylation in cancer cells. Nat Genet. 2006; 38(2):149-53.
19. Sproul D, Nestor C, Culley J, Dickson JH, Dixon JM, Harrison DJ, et al. Transcriptionally repressed genes become aberrantly methylated and distinguish tumors of different lineages in breast cancer. Proc Nat Acad Sci USA. 2011; 108(11):4364-9.
20. Sproul D, Kitchen RR, Nestor CE, Dixon JM, Sims AH, Harrison DJ, et al. Tissue of origin determines cancer-associated CpG island promoter hypermethylation patterns. Genome Biol. 2012; 13(10):R84. doi: 10.1186/gb-2012-13-10-r84.
21. Sproul D, Meehan RR. Genomic insights into cancer-associated aberrant CpG island hypermethylation. Brief Funct Genomics. 2013; 12(3):174-90.
22. Timp W, Feinberg AP. Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Epigenet Genet. 2013;13(July):497-510.
23. Timp W, Bravo HC, McDonald OG, Goggins M, Umbricht C, Zeiger M., et al. Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med. 2014; 6(61):61.
24. Reddington JP, Sproul D, Meehan RR. DNA methylation reprogramming in cancer: does it act by re-configuring the binding landscape of Polycomb repressive complexes?Bioessays. 2014; 36(2):134-40.
25. Toyota M, Ahuja N, Ohe-Toyota M, Herman JG, Baylin SB, Issa J-PJ. CpG island methylator phenotype in colorectal cancer. Proc Natl Acad Sci. 1999; 96(July):8681-6.
26. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics: J Integrative Biol. 2012; 16(5):284-7.
27. Hughes LAE, Melotte V, de Schrijver J, de Maat M, Smit VTHBM, Bovée JVMG, et al. The CpG island methylator phenotype: what's in a name?Cancer Res. 2013; 73(19):5858-68.
28. Struhl K. Fundamentally Different Logic of Gene Regulation in Eukaryotes and Prokaryotes. Cell. 1999; 98:1-4.
29. Zhang HM, Chen H, Liu W, Liu H, Gong J, Wang H, et al. AnimalTFDB: a comprehensive animal transcription factor database. Nucleic Acids Res. 2012; 40(Database issue):144-9.
30. Vaquerizas JM, Kummerfeld SK, Teichmann Sa, Luscombe NM. A census of human transcription factors: function, expression and evolution. Nat Rev Genet. 2009; 10(4):252-63.
31. Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Sci (New York, N.Y.) 2007; 315(5813):848-53.
32. Vanderkraats ND, Hiken JF, Decker KF, Edwards JR. Discovering high-resolution patterns of differential DNA methylation that correlate with gene expression changes. Nucleic Acids Res. 2013; 41(14):6816-27.
33. van Vlodrop IJH, Niessen HEC, Derks S, Baldewijns MMLL, van Criekinge W, Herman JG, et al. Analysis of promoter CpG island hypermethylation in cancer: location, location, location!. Clin Cancer Res. 2011; 17(13):4225-31.
34. Maunakea AK, Chepelev I, Cui K, Zhao K. Intragenic DNA methylation modulates alternative splicing by recruiting MeCP2 to promote exon recognition. Cell Res. 2013; 23(11):1256-69.
35. Kulis M, Queirós AC, Beekman R, Martín-Subero JI. Intragenic DNA methylation in transcriptional regulation, normal differentiation and cancer. Biochimica et Biophysica Acta. 2013; 1829(11):1161-74.
36. Kriaucionis S, Heintz N. The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science. 2009; 324:929-30.
37. Nestor C, Ruzov A, Meehan R, Dunican D. Enzymatic approaches and bisulfite sequencing cannot distinguish between 5-methylcytosine and 5-hydroxymethylcytosine in DNA. Biotechniques. 2010; 48:317-9.
38. Haffner M, Chaux A, Meeker A, Esopi D, Gerber J, Pellakuru L, et al. Global 5-hydroxymethylcytosine content is significantly reduced in tissue stem/progenitor cell compartments and in human cancers. Oncotarget. 2011; 2:627-37.
39. Jin S, Jiang Y, Qiu R, Rauch T, Wang Y, Schackert G, et al. 5-hydroxymethylcytosine is strongly depleted in human cancers, but its levels do not correlate with IDH1 mutations. Cancer Res. 2011; 71:7360-365.
40. Henrichsen CN, Chaignat E, Reymond A. Copy number variants, diseases and gene expression. Hum Mol Genet. 2009; 18(R1):1-8.
41. Houseman EA, Christensen BC, Karagas MR, Wrensch MR, Nelson HH, Wiemels JL, et al. Copy number variation has little impact on bead-array-based measures of DNA methylation. Bioinformatics (Oxford, England). 2009; 25(16):1999-2005.
42. Lauss M, Aine M, Sjödahl G, Veerla S, Patschan O, Gudjonsson S, et al. DNA methylation analyses of urothelial carcinoma reveal distinct epigenetic subtypes and an association between gene copy number and methylation status. Epigenetics. 2012; 7(8):858-67.
43. Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011; 98(4):288-95.
44. Mortazavi A, Williams BA, Mccue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008; 5(7):1-8.
45. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP. GenePattern 2.0. Nat Genet. 2006; 38(5):500-1.
46. Rabiner L, Juang BH. Fundamentals of Speech Recognition. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.; 1993.
47. Efrat A, Fan Q, Venkatasubramanian S. Curve Matching, Time Warping, and Light Fields: New Algorithms for Computing Similarity between Curves. J Math Imaging Vis. 2006; 27(3):203-16.
48. Serra B, Berthod M. Subpixel contour matching using continuous dynamic programming. In: Computer Vision and Pattern Recognition 1994. Proceedings CVPR '94, 1994 IEEE Computer Society Conference: 1994. p. 202-207.
49. Wu TD, Schmidler SC, Hastie T, Brutlag DL. Regression analysis of multiple protein structures. J Comput Biol. 1998; 5(3):585-95.
50. Keogh EJ, Pazzani MJ. Scaling up Dynamic Time Warping to Massive Datasets. Proc 3rd Eur Conf Principles Prac Knowl Discov Databases (KDD). 1999; 1704:1-11.
51. Ben-Hur A, Elisseeff A, Guyon I. A stability based method for discovering structure in clustered data. Pac Symp Biocomput. 2002; 17:6-17.
52. Kaplan EL, Meier D. Nonparametric estimation from incomplete observation. J Am Statist. 1958; 58:457-81.
53. Cox DR, Oakes D. Analysis of Survival Data. London: Chapman and Hall; 1984.
54. Hoerl AE, Kennard RW, Kennard W. Ridge Regression: Applications to Nonorthogonal Problems. Technometrics. 1970; 12(1):69-82.
55. Tibshirani R. Regression Shrinkage and Selection via the Lasso. J R Stat Soc. 1996; 58(1):267-88.
56. Acharjee A, Finkers R, Visser RGF, Maliepaard C. Comparison of Regularized Regression Methods for Omics Data. Metabolomics. 2013; 3:126.