Literature DB >> 24713546

The clinical and diagnostic role of microRNAs in ovarian carcinoma.

Ben Davidson1, Claes G Tropé2, Reuven Reich3.   

Abstract

OBJECTIVE: MicroRNAs (miRNAs, miRs) are non-coding RNAs which post-transcriptionally regulate mRNA synthesis. Data regarding the expression and clinical relevance of miRNAs and the miRNA-regulating machinery in ovarian carcinoma has been rapidly expanding in recent years. This review presents current knowledge in this area.
METHODS: PubMed search was undertaken using the terms 'ovarian' and 'microRNA'.
RESULTS: A total of 492 papers were identified, of which approximately 100 were publications in English focusing exclusively or partly on clinical ovarian carcinoma specimens. These studies have identified multiple miRNAs with a potential role in the diagnosis, biology and progression of ovarian carcinoma, as well as on predicting chemoresponse and determining prognosis.
CONCLUSIONS: The presented data support a clinical role for miRNAs in ovarian carcinoma and suggest that miRNA-regulated pathways may be of relevance for novel therapeutics. Novel technologies offer new possibilities for wide-scale miRNA-based classification of OC. Further genomic research focusing on larger series of tumors of similar histological type in combination with experimental approaches is likely to expand our understanding of the role of miRNAs in this cancer.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Chemotherapy; Diagnosis; MicroRNA; Ovarian carcinoma; Prognosis

Mesh:

Substances:

Year:  2014        PMID: 24713546     DOI: 10.1016/j.ygyno.2014.03.575

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


  24 in total

1.  MicroRNA-9 promotes tumorigenesis and mediates sensitivity to cisplatin in primary epithelial ovarian cancer cells.

Authors:  Hong-Min Zhao; Wei Wei; Yu-Hui Sun; Jian-Hua Gao; Qi Wang; Jian-Hua Zheng
Journal:  Tumour Biol       Date:  2015-04-07

Review 2.  Potential microRNA-related Targets for Therapeutic Intervention with Ovarian Cancer Metastasis.

Authors:  Ulrich H Weidle; Fabian Birzele; Gwen Kollmorgen; Adam Nopora
Journal:  Cancer Genomics Proteomics       Date:  2018 Jan-Feb       Impact factor: 4.069

3.  A prognostic risk model for ovarian cancer based on gene expression profiles from gene expression omnibus database.

Authors:  Wei Fan; Xiaoyun Chen; Ruiping Li; Rongfang Zheng; Yunyun Wang; Yuzhen Guo
Journal:  Biochem Genet       Date:  2022-06-27       Impact factor: 1.890

4.  miR-320 inhibited ovarian cancer oncogenicity via targeting TWIST1 expression.

Authors:  Chunyang Li; Ping Duan; Jianguang Wang; Xiaosheng Lu; Jing Cheng
Journal:  Am J Transl Res       Date:  2017-08-15       Impact factor: 4.060

5.  MicroRNA-613 inhibited ovarian cancer cell proliferation and invasion by regulating KRAS.

Authors:  Xin Fu; Yanfen Cui; Shaobin Yang; Yue Xu; Zicheng Zhang
Journal:  Tumour Biol       Date:  2015-12-02

Review 6.  The role of microRNAs in ovarian cancer.

Authors:  Yasuto Kinose; Kenjiro Sawada; Koji Nakamura; Tadashi Kimura
Journal:  Biomed Res Int       Date:  2014-09-10       Impact factor: 3.411

7.  Diagnostic and prognostic potential of serum miR-7, miR-16, miR-25, miR-93, miR-182, miR-376a and miR-429 in ovarian cancer patients.

Authors:  Xiaodan Meng; Simon A Joosse; Volkmar Müller; Fabian Trillsch; Karin Milde-Langosch; Sven Mahner; Maria Geffken; Klaus Pantel; Heidi Schwarzenbach
Journal:  Br J Cancer       Date:  2015-09-22       Impact factor: 7.640

8.  miR-148a and miR-375 may serve as predictive biomarkers for early diagnosis of laryngeal carcinoma.

Authors:  Ying Wu; Jia Yu; Yanni Ma; Fang Wang; Honggang Liu
Journal:  Oncol Lett       Date:  2016-06-13       Impact factor: 2.967

9.  MicroRNA Gene Expression Signature Driven by miR-9 Overexpression in Ovarian Clear Cell Carcinoma.

Authors:  Nozomu Yanaihara; Yukiko Noguchi; Misato Saito; Masataka Takenaka; Satoshi Takakura; Kyosuke Yamada; Aikou Okamoto
Journal:  PLoS One       Date:  2016-09-09       Impact factor: 3.240

10.  Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics.

Authors:  Meiying Zhang; Guanglei Zhuang; Xiangjun Sun; Yanying Shen; Aimin Zhao; Wen Di
Journal:  J Ovarian Res       Date:  2015-10-21       Impact factor: 4.234

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