Literature DB >> 33278864

DNA methylation subtypes for ovarian cancer prognosis.

Lili Yin1, Ningning Zhang1, Qing Yang1.   

Abstract

Ovarian cancer is one of three major malignancies of the female reproductive system. DNA methylation (MET) is closely related to ovarian cancer occurrence and development, and as such, elucidation of effective MET subtype markers may guide individualized treatment and improve ovarian cancer prognosis. To identify potential markers, we downloaded a total of 571 ovarian cancer MET samples from The Cancer Genome Atlas (TCGA), and established a Cox proportional hazards model using the MET spectrum and clinical pathological parameters. A total of 250 prognosis-related MET loci were obtained by Cox regression, and six molecular subtypes were screened by consensus clustering of CpG loci with a significant difference in both univariate and multivariate analyses. There was a remarkable MET difference between most subtypes. Cluster 2 had the highest MET level and demonstrated the best prognosis, while Clusters 4 and 5 had MET levels significantly lower than those of the other subtypes and demonstrated very poor prognosis. All Cluster 5 samples were at a high grade, while the percentage of stage IV samples in Cluster 4 was greater than in the other subtypes. We obtained five CpG loci using a coexpression network: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. Our cluster analysis showed that prognosis in patients with hypomethylation was significantly worse than in patients with hypermethylation. These MET molecular subtypes can be used not only to evaluate ovarian cancer prognosis, but also to fully distinguish the tumor stage and histological grade in patients with ovarian cancer.
© 2020 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

Entities:  

Keywords:  TCGA; methylation; molecular subtype; ovarian cancer; prognosis

Mesh:

Substances:

Year:  2021        PMID: 33278864      PMCID: PMC7931230          DOI: 10.1002/2211-5463.13056

Source DB:  PubMed          Journal:  FEBS Open Bio        ISSN: 2211-5463            Impact factor:   2.693


  38 in total

1.  Overall survival and the response to radiotherapy among molecular subtypes of breast cancer brain metastases treated with targeted therapies.

Authors:  Jacob A Miller; Rupesh Kotecha; Manmeet S Ahluwalia; Alireza M Mohammadi; Samuel T Chao; Gene H Barnett; Erin S Murphy; Michael A Vogelbaum; Lilyana Angelov; David M Peereboom; John H Suh
Journal:  Cancer       Date:  2017-02-13       Impact factor: 6.860

2.  Bevacizumab May Differentially Improve Ovarian Cancer Outcome in Patients with Proliferative and Mesenchymal Molecular Subtypes.

Authors:  Stefan Kommoss; Boris Winterhoff; Ann L Oberg; Gottfried E Konecny; Chen Wang; Shaun M Riska; Jian-Bing Fan; Matthew J Maurer; Craig April; Viji Shridhar; Friedrich Kommoss; Andreas du Bois; Felix Hilpert; Sven Mahner; Klaus Baumann; Willibald Schroeder; Alexander Burges; Ulrich Canzler; Jeremy Chien; Andrew C Embleton; Mahesh Parmar; Richard Kaplan; Timothy Perren; Lynn C Hartmann; Ellen L Goode; Sean C Dowdy; Jacobus Pfisterer
Journal:  Clin Cancer Res       Date:  2017-02-03       Impact factor: 12.531

3.  Genome-wide assessment of recurrent genomic imbalances in canine leukemia identifies evolutionarily conserved regions for subtype differentiation.

Authors:  Sarah C Roode; Daniel Rotroff; Anne C Avery; Steven E Suter; Dorothee Bienzle; Joshua D Schiffman; Alison Motsinger-Reif; Matthew Breen
Journal:  Chromosome Res       Date:  2015-06-03       Impact factor: 5.239

4.  Biology and clinical relevance of the micropthalmia family of transcription factors in human cancer.

Authors:  Rizwan Haq; David E Fisher
Journal:  J Clin Oncol       Date:  2011-06-13       Impact factor: 44.544

5.  Promoter hypermethylation and BRCA1 inactivation in sporadic breast and ovarian tumors.

Authors:  M Esteller; J M Silva; G Dominguez; F Bonilla; X Matias-Guiu; E Lerma; E Bussaglia; J Prat; I C Harkes; E A Repasky; E Gabrielson; M Schutte; S B Baylin; J G Herman
Journal:  J Natl Cancer Inst       Date:  2000-04-05       Impact factor: 13.506

6.  Histological grading in a large series of advanced stage ovarian carcinomas by three widely used grading systems: consistent lack of prognostic significance. A translational research subprotocol of a prospective randomized phase III study (AGO-OVAR 3 protocol).

Authors:  Stefan Kommoss; Dietmar Schmidt; Friedrich Kommoss; Juergen Hedderich; Philipp Harter; Jacobus Pfisterer; Andreas du Bois
Journal:  Virchows Arch       Date:  2009-01-27       Impact factor: 4.064

7.  Alterations of the tumor suppressor gene ARLTS1 in ovarian cancer.

Authors:  Fabio Petrocca; Dimitrios Iliopoulos; Haiyan R Qin; Milena S Nicoloso; Say Yendamuri; Sylwia E Wojcik; Masayoshi Shimizu; Gianpiero Di Leva; Andrea Vecchione; Francesco Trapasso; Andrew K Godwin; Massimo Negrini; George A Calin; Carlo M Croce
Journal:  Cancer Res       Date:  2006-11-01       Impact factor: 12.701

8.  Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome.

Authors:  Richard W Tothill; Anna V Tinker; Joshy George; Robert Brown; Stephen B Fox; Stephen Lade; Daryl S Johnson; Melanie K Trivett; Dariush Etemadmoghadam; Bianca Locandro; Nadia Traficante; Sian Fereday; Jillian A Hung; Yoke-Eng Chiew; Izhak Haviv; Dorota Gertig; Anna DeFazio; David D L Bowtell
Journal:  Clin Cancer Res       Date:  2008-08-15       Impact factor: 12.531

9.  DNA Methylation-Based Classifier for Accurate Molecular Diagnosis of Bone Sarcomas.

Authors:  S Peter Wu; Benjamin T Cooper; Fang Bu; Christopher J Bowman; J Keith Killian; Jonathan Serrano; Shiyang Wang; Twana M Jackson; Daniel Gorovets; Neerav Shukla; Paul A Meyers; David J Pisapia; Richard Gorlick; Marc Ladanyi; Kristen Thomas; Matija Snuderl; Matthias A Karajannis
Journal:  JCO Precis Oncol       Date:  2017-10-06

10.  g:Profiler-a web server for functional interpretation of gene lists (2016 update).

Authors:  Jüri Reimand; Tambet Arak; Priit Adler; Liis Kolberg; Sulev Reisberg; Hedi Peterson; Jaak Vilo
Journal:  Nucleic Acids Res       Date:  2016-04-20       Impact factor: 16.971

View more
  4 in total

1.  Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.

Authors:  Chen Zhao; Kewei Xiong; Fangrui Zhao; Abdalla Adam; Xiangpan Li
Journal:  Oxid Med Cell Longev       Date:  2022-03-04       Impact factor: 6.543

Review 2.  Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis.

Authors:  Ran Hu; Xianghong Jasmine Zhou; Wenyuan Li
Journal:  J Comput Biol       Date:  2022-06-06       Impact factor: 1.549

3.  Integration of Transcriptome and Epigenome to Identify and Develop Prognostic Markers for Ovarian Cancer.

Authors:  Can Xu; Wei Cao
Journal:  J Oncol       Date:  2022-08-30       Impact factor: 4.501

4.  Integrated analysis of the M2 macrophage-related signature associated with prognosis in ovarian cancer.

Authors:  Caijiao Peng; Licheng Li; Guangxia Luo; Shanmei Tan; Ruming Xia; Lanjuan Zeng
Journal:  Front Oncol       Date:  2022-08-26       Impact factor: 5.738

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.