Literature DB >> 30847169

Methylation and ovarian cancer: Can DNA methylation be of diagnostic use?

Julie L Hentze1, Claus K Høgdall2, Estrid V Høgdall1.   

Abstract

Ovarian cancer is a silent killer and, due to late diagnosis and frequent chemo resistance in patients, the primary cause of fatality amongst the various types of gynecological cancer. The discovery of a specific and sensitive biomarker for ovarian cancer could improve early diagnosis, thereby saving lives. Biomarkers could also improve treatment, by predicting which patients will benefit from specific treatment strategies. DNA methylation is an epigenetic mechanism, and 'methylation imbalance' is characteristic of cancer. Previous research suggests that changes in DNA methylation can be used diagnostically, and that they may predict resistance to treatment. This paper gives an up-to-date overview of research investigating the potential of DNA methylation-based markers for diagnostics, prognostics, screening and prediction of drug resistance for ovarian cancer patients. DNA methylation cancer-biomarkers may be useful for cancer treatment, particularly since they are chemically stable and since cancer-associated changes in methylation typically precedes tumor growth. DNA methylation markers could improve diagnosis and treatment and might even be used for screening in the future. Furthermore, DNA methylation biomarkers could facilitate the development of precision medicine. However, at this point no biomarkers for ovarian cancer have a sufficient combination of sensitivity and specificity in a clinical setting. A reason for this is that most studies have focused on a single or a few methylation sites. More large screenings and genome-wide studies must be performed to increase the chance of identifying a DNA methylation marker which can identify ovarian cancer.

Entities:  

Keywords:  DNA methylation; diagnostic/prognostic biomarkers; ovarian cancer; precision medicine; prediction of chemo-resistance

Year:  2019        PMID: 30847169      PMCID: PMC6388465          DOI: 10.3892/mco.2019.1800

Source DB:  PubMed          Journal:  Mol Clin Oncol        ISSN: 2049-9450


  111 in total

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  9 in total

Review 1.  Platinum-resistant ovarian cancer: From drug resistance mechanisms to liquid biopsy-based biomarkers for disease management.

Authors:  Mohammad Aslam Khan; Kunwar Somesh Vikramdeo; Sarabjeet Kour Sudan; Seema Singh; Annelise Wilhite; Santanu Dasgupta; Rodney Paul Rocconi; Ajay Pratap Singh
Journal:  Semin Cancer Biol       Date:  2021-08-18       Impact factor: 15.707

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Authors:  Li-Yuan Feng; Yong-Zhi Huang; Wei Zhang; Li Li
Journal:  J Ovarian Res       Date:  2021-05-15       Impact factor: 4.234

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Authors:  Zachary R Visco; Gregory Sfakianos; Carole Grenier; Marie-Helene Boudreau; Sabrina Simpson; Isabel Rodriguez; Regina Whitaker; Derek Y Yao; Andrew Berchuck; Susan K Murphy; Zhiqing Huang
Journal:  Front Oncol       Date:  2021-03-22       Impact factor: 6.244

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Authors:  Quan Cheng; Jing Wang; He Li; Nayiyuan Wu; Zhao-Yi Liu; Yong-Chang Chen
Journal:  Sci Rep       Date:  2021-03-30       Impact factor: 4.379

Review 5.  Comprehensive analysis of regulation of DNA methyltransferase isoforms in human breast tumors.

Authors:  Mangala Hegde; Manjunath B Joshi
Journal:  J Cancer Res Clin Oncol       Date:  2021-02-18       Impact factor: 4.553

6.  Prognostic Significance of SLFN11 Methylation in Plasma Cell-Free DNA in Advanced High-Grade Serous Ovarian Cancer.

Authors:  Victoria Tserpeli; Dimitra Stergiopoulou; Dora Londra; Lydia Giannopoulou; Paul Buderath; Ioanna Balgkouranidou; Nikolaos Xenidis; Christina Grech; Eva Obermayr; Robert Zeillinger; Kitty Pavlakis; Theodoros Rampias; Stylianos Kakolyris; Sabine Kasimir-Bauer; Evi S Lianidou
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Authors:  Guang-Jie Nie; Jian Liu; Ai-Mei Zou; Shao-Feng Zhan; Jia-Kang Liang; Yi Sui; Yu-Ning Chen; Wei-Shen Yao
Journal:  J Clin Lab Anal       Date:  2022-03-03       Impact factor: 2.352

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Authors:  Yinbing Zhang; Sahar Qazi; Khalid Raza
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9.  Long reads capture simultaneous enhancer-promoter methylation status for cell-type deconvolution.

Authors:  Sapir Margalit; Yotam Abramson; Hila Sharim; Zohar Manber; Surajit Bhattacharya; Yi-Wen Chen; Eric Vilain; Hayk Barseghyan; Ran Elkon; Roded Sharan; Yuval Ebenstein
Journal:  Bioinformatics       Date:  2021-07-12       Impact factor: 6.937

  9 in total

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