Literature DB >> 26827964

Gene-expression signatures in ovarian cancer: Promise and challenges for patient stratification.

Gottfried E Konecny1, Boris Winterhoff2, Chen Wang3.   

Abstract

Microarray-based gene expression studies demonstrate that ovarian cancer is both a clinically diverse and molecularly heterogeneous disease compromising subtypes with distinct gene expression patterns that are each associated with statistically significant different clinical outcomes. The information provided by gene expression based assays is promising and deserves incorporation into clinical decision-making. Further studies are needed to determine which subtype signatures are most appropriate to select patients for a given therapy. This process will require the development of standardized molecular diagnostic assays that can be used for retrospective correlative studies and prospective validations of their clinical utility. Recent advances in assay development for FFPE tissues will facilitate accurate and cost-effective classification of ovarian cancer and help move the evolving molecular classification to clinic. The current review will summarize the development of gene expression based assays in ovarian cancer and will describe how the results of studies to date have expanded our appreciation of the heterogeneity of ovarian cancer. We discuss difficulties in the development and validation of molecular classifications in ovarian cancer and we provide future directions how we may be able to soon classify the disease in a manner that might have greater clinical utility.
Copyright © 2016 Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 26827964     DOI: 10.1016/j.ygyno.2016.01.026

Source DB:  PubMed          Journal:  Gynecol Oncol        ISSN: 0090-8258            Impact factor:   5.482


  15 in total

1.  A method for extracting and characterizing RNA from urine: For downstream PCR and RNAseq analysis.

Authors:  Kun Zhou; Monique A Spillman; Kian Behbakht; Julia M Komatsu; Juan E Abrahante; Douglas Hicks; Brent Schotl; Evan Odean; Kenneth L Jones; Michael W Graner; Lynne T Bemis
Journal:  Anal Biochem       Date:  2017-08-10       Impact factor: 3.365

Review 2.  Radiomics: an Introductory Guide to What It May Foretell.

Authors:  Stephanie Nougaret; Hichem Tibermacine; Marion Tardieu; Evis Sala
Journal:  Curr Oncol Rep       Date:  2019-06-25       Impact factor: 5.075

3.  Sorafenib inhibits ovarian cancer cell proliferation and mobility and induces radiosensitivity by targeting the tumor cell epithelial-mesenchymal transition.

Authors:  Chuntao Tian; Ying Liu; Lingfei Xue; Dong Zhang; Xiaotong Zhang; Jing Su; Jiaohong Chen; Xiangke Li; Liuxing Wang; Shunchang Jiao
Journal:  Open Life Sci       Date:  2022-06-15       Impact factor: 1.311

Review 4.  Personalized models of heterogeneous 3D epithelial tumor microenvironments: Ovarian cancer as a model.

Authors:  Eric N Horst; Michael E Bregenzer; Pooja Mehta; Catherine S Snyder; Taylor Repetto; Yang Yang-Hartwich; Geeta Mehta
Journal:  Acta Biomater       Date:  2021-04-30       Impact factor: 10.633

5.  Identification of prognostic signature in cancer based on DNA methylation interaction network.

Authors:  Wei-Lin Hu; Xiong-Hui Zhou
Journal:  BMC Med Genomics       Date:  2017-12-21       Impact factor: 3.063

6.  A tRNA fragment, tRF5-Glu, regulates BCAR3 expression and proliferation in ovarian cancer cells.

Authors:  Kun Zhou; Kevin W Diebel; Jon Holy; Andrew Skildum; Evan Odean; Douglas A Hicks; Brent Schotl; Juan E Abrahante; Monique A Spillman; Lynne T Bemis
Journal:  Oncotarget       Date:  2017-09-08

7.  Identifying prognostic signature in ovarian cancer using DirGenerank.

Authors:  Jian-Yong Wang; Ling-Ling Chen; Xiong-Hui Zhou
Journal:  Oncotarget       Date:  2017-07-11

8.  Clinical and genetic analysis of recurrent adult-type granulosa cell tumor of the ovary: Persistent preservation of heterozygous c.402C>G FOXL2 mutation.

Authors:  Satoshi Yanagida; Michael S Anglesio; Tayyebeh M Nazeran; Amy Lum; Momoko Inoue; Yasushi Iida; Hirokuni Takano; Takashi Nikaido; Aikou Okamoto; David G Huntsman
Journal:  PLoS One       Date:  2017-06-08       Impact factor: 3.240

9.  Identification of Genes and Pathways Involved in Ovarian Epithelial Cancer by Bioinformatics Analysis.

Authors:  Yun Zhou; Olivia Layton; Li Hong
Journal:  J Cancer       Date:  2018-07-30       Impact factor: 4.207

10.  An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules.

Authors:  Yi-Cheng Gao; Xiong-Hui Zhou; Wen Zhang
Journal:  Front Genet       Date:  2019-04-24       Impact factor: 4.599

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