Ovarian Cancer Genome and Molecular Experimental Sciences

2017 ◽  
pp. 143-154
Author(s):  
Noriomi Matsumura ◽  
Ikuo Konishi
Keyword(s):  
Ovarian Cancer ◽  
Cancer Genome

Clinical translation of the cancer genome atlas signature for ovarian cancer survival

2012 ◽  
Vol 125 ◽  
pp. S42
Author(s):  
G. Sfakianos ◽  
E. Iversen ◽  
W. Lowery ◽  
R. Whitaker ◽  
L. Akushevich ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Survival ◽  
Cancer Genome ◽  
Clinical Translation ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Ovarian Cancer Survival ◽  
Genome Atlas

Ovarian Cancer Genome

2013 ◽  
pp. 3-7 ◽  
Author(s):  
Evgeny N. Imyanitov
Keyword(s):  
Ovarian Cancer ◽  
Cancer Genome

scholarly journals High level of chromosomal aberration in ovarian cancer genome correlates with poor clinical outcome

2013 ◽  
Vol 128 (3) ◽  
pp. 500-505 ◽  
Author(s):  
Leslie Cope ◽  
Ren-Chin Wu ◽  
Ie-Ming Shih ◽  
Tian-Li Wang
Keyword(s):  
Ovarian Cancer ◽  
Clinical Outcome ◽  
Chromosomal Aberration ◽  
Cancer Genome ◽  
Poor Clinical Outcome ◽  
High Level

scholarly journals TP53 hot spot mutations in ovarian cancer: Selective resistance to microtubule stabilizers in vitro and differential survival outcomes from The Cancer Genome Atlas

2015 ◽  
Vol 138 (1) ◽  
pp. 159-164 ◽  
Author(s):  
Brandon-Luke L. Seagle ◽  
Chia-Ping Huang Yang ◽  
Kevin H. Eng ◽  
Monica Dandapani ◽  
Oluwatosin Odunsi-Akanji ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Hot Spot ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Survival Outcomes ◽  
Differential Survival ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Association between invasive ovarian cancer susceptibility and 11 best candidate SNPs from breast cancer genome-wide association study

2009 ◽  
Vol 18 (12) ◽  
pp. 2297-2304 ◽  
Author(s):  
Honglin Song ◽  
Susan J. Ramus ◽  
Susanne Krüger Kjaer ◽  
Richard A. DiCioccio ◽  
Georgia Chenevix-Trench ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Cancer Susceptibility ◽  
Cancer Genome ◽  
Genome Wide Association ◽  
Candidate Snps ◽  
Genome Wide

scholarly journals Prognostic values of GMPS, PR, CD40, and p21 in ovarian cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6301 ◽  
Author(s):  
Ping Wang ◽  
Zengli Zhang ◽  
Yujie Ma ◽  
Jun Lu ◽  
Hu Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cancer Patients ◽  
Differential Expression ◽  
Cancer Progression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Progression Model ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Early detection and prediction of prognosis and treatment responses are all the keys in improving survival of ovarian cancer patients. This study profiled an ovarian cancer progression model to identify prognostic biomarkers for ovarian cancer patients. Mouse ovarian surface epithelial cells (MOSECs) can undergo spontaneous malignant transformation in vitro cell culture. These were used as a model of ovarian cancer progression for alterations in gene expression and signaling detected using the Illumina HiSeq2000 Next-Generation Sequencing platform and bioinformatical analyses. The differential expression of four selected genes was identified using the gene expression profiling interaction analysis (http://gepia.cancer-pku.cn/) and then associated with survival in ovarian cancer patients using the Cancer Genome Atlas dataset and the online Kaplan–Meier Plotter (http://www.kmplot.com) data. The data showed 263 aberrantly expressed genes, including 182 up-regulated and 81 down-regulated genes between the early and late stages of tumor progression in MOSECs. The bioinformatic data revealed four genes (i.e., guanosine 5′-monophosphate synthase (GMPS), progesterone receptor (PR), CD40, and p21 (cyclin-dependent kinase inhibitor 1A)) to play an important role in ovarian cancer progression. Furthermore, the Cancer Genome Atlas dataset validated the differential expression of these four genes, which were associated with prognosis in ovarian cancer patients. In conclusion, this study profiled differentially expressed genes using the ovarian cancer progression model and identified four (i.e., GMPS, PR, CD40, and p21) as prognostic markers for ovarian cancer patients. Future studies of prospective patients could further verify the clinical usefulness of this four-gene signature.

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