Literature DB >> 17499000

Comparative DNA methylation analysis in normal and tumour tissues and in cancer cell lines using differential methylation hybridisation.

Joern Lewin1, Achim Plum, Thomas Hildmann, Tamas Rujan, Florian Eckhardt, Volker Liebenberg, Catherine Lofton-Day, Reinhold Wasserkort.   

Abstract

Immortalized human cancer cell lines are widely used as tools and model systems in cancer research but their authenticity with regard to primary tissues remains a matter of debate. We have used differential methylation hybridisation to obtain comparative methylation profiles from normal and tumour tissues of lung and colon, and permanent cancer cell lines originally derived from these tissues. Average methylation differences only larger than 25% between sample groups were considered for the profiles and with this criterion approximately 1000 probesets, around 2% of the sites represented on the array, indicated differential methylation between normal lung and primary lung cancer tissue, and approximately 700 probesets between normal colon and primary colon cancer tissue. Both hyper- and hypomethylation was found to differentiate normal tissue from cancer tissue. The profiles obtained from these tissue comparisons were found to correspond largely to those from the corresponding cancer cell lines, indicating that the cell lines represent the methylation pattern of the primary tissue rather well. Moreover, the cancer specific profiles were found to be very similar for the two tumour types studied. Tissue specific differential methylation between lung and colon tissues, in contrast, was found to be preserved to a larger extent only in the malignant tissue, but was not preserved well in the cancer cell lines studied. Overall, our data therefore provide further evidence that permanent cell lines are good model systems for cancer specific methylation patterns, but deviate with regard to tissue-specific methylation.

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Year:  2007        PMID: 17499000     DOI: 10.1016/j.biocel.2007.03.006

Source DB:  PubMed          Journal:  Int J Biochem Cell Biol        ISSN: 1357-2725            Impact factor:   5.085


  9 in total

1.  Aberrant DNA methylation occurs in colon neoplasms arising in the azoxymethane colon cancer model.

Authors:  Scott C Borinstein; Melissa Conerly; Slavomir Dzieciatkowski; Swati Biswas; M Kay Washington; Patty Trobridge; Steve Henikoff; William M Grady
Journal:  Mol Carcinog       Date:  2010-01       Impact factor: 4.784

2.  CDO1 promoter methylation is a biomarker for outcome prediction of anthracycline treated, estrogen receptor-positive, lymph node-positive breast cancer patients.

Authors:  Dimo Dietrich; Manuel Krispin; Jörn Dietrich; Anne Fassbender; Jörn Lewin; Nadia Harbeck; Manfred Schmitt; Serenella Eppenberger-Castori; Vincent Vuaroqueaux; Frédérique Spyratos; John A Foekens; Ralf Lesche; John W M Martens
Journal:  BMC Cancer       Date:  2010-06-01       Impact factor: 4.430

3.  SOX11 expression correlates to promoter methylation and regulates tumor growth in hematopoietic malignancies.

Authors:  Elin Gustavsson; Sandra Sernbo; Elin Andersson; Donal J Brennan; Michael Dictor; Mats Jerkeman; Carl Ak Borrebaeck; Sara Ek
Journal:  Mol Cancer       Date:  2010-07-12       Impact factor: 27.401

4.  The presence of methylation quantitative trait loci indicates a direct genetic influence on the level of DNA methylation in adipose tissue.

Authors:  Alexander W Drong; George Nicholson; Asa K Hedman; Eshwar Meduri; Elin Grundberg; Kerrin S Small; So-Youn Shin; Jordana T Bell; Fredrik Karpe; Nicole Soranzo; Tim D Spector; Mark I McCarthy; Panos Deloukas; Mattias Rantalainen; Cecilia M Lindgren
Journal:  PLoS One       Date:  2013-02-19       Impact factor: 3.240

5.  SHOX2 DNA methylation is a biomarker for the diagnosis of lung cancer based on bronchial aspirates.

Authors:  Bernd Schmidt; Volker Liebenberg; Dimo Dietrich; Thomas Schlegel; Christoph Kneip; Anke Seegebarth; Nadja Flemming; Stefanie Seemann; Jürgen Distler; Jörn Lewin; Reimo Tetzner; Sabine Weickmann; Ulrike Wille; Triantafillos Liloglou; Olaide Raji; Martin Walshaw; Michael Fleischhacker; Christian Witt; John K Field
Journal:  BMC Cancer       Date:  2010-11-03       Impact factor: 4.430

6.  Genome-wide screen for differential DNA methylation associated with neural cell differentiation in mouse.

Authors:  Rene Cortese; Jörn Lewin; Liselotte Bäckdahl; Manuel Krispin; Reinhold Wasserkort; Florian Eckhardt; Stephan Beck
Journal:  PLoS One       Date:  2011-10-18       Impact factor: 3.240

7.  De novo identification of differentially methylated regions in the human genome.

Authors:  Timothy J Peters; Michael J Buckley; Aaron L Statham; Ruth Pidsley; Katherine Samaras; Reginald V Lord; Susan J Clark; Peter L Molloy
Journal:  Epigenetics Chromatin       Date:  2015-01-27       Impact factor: 4.954

8.  BIMMER: a novel algorithm for detecting differential DNA methylation regions from MBDCap-seq data.

Authors:  Zijing Mao; Chifeng Ma; Tim H-M Huang; Yidong Chen; Yufei Huang
Journal:  BMC Bioinformatics       Date:  2014-11-06       Impact factor: 3.169

9.  Plasma cell free DNA methylation markers for hepatocellular carcinoma surveillance in patients with cirrhosis: a case control study.

Authors:  Jörn Lewin; Denise Kottwitz; Johanna Aoyama; Theo deVos; Jorge Garces; Oliver Hasinger; Stefanie Kasielke; Florian Knaust; Preeti Rathi; Sebastian Rausch; Gunter Weiss; Alexander Zipprich; Edward Mena; Tse-Ling Fong
Journal:  BMC Gastroenterol       Date:  2021-03-25       Impact factor: 3.067

  9 in total

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