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.

scholarly journals Japanese version of The Cancer Genome Atlas, JCGA, established using fresh frozen tumors obtained from 5143 cancer patients

2020 ◽  
Vol 111 (2) ◽  
pp. 687-699 ◽  
Author(s):  
Takeshi Nagashima ◽  
Ken Yamaguchi ◽  
Kenichi Urakami ◽  
Yuji Shimoda ◽  
Sumiko Ohnami ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Japanese Version ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Fresh Frozen ◽  
Cancer Genome Atlas ◽  
Genome Atlas

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

scholarly journals Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas

2018 ◽  
Author(s):  
SR Rosario ◽  
MD Long ◽  
HC Affronti ◽  
AM Rowsam ◽  
KH Eng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Flux ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Targeted Therapeutics ◽  
Bioinformatics Pipeline ◽  
Metabolic Dysregulation ◽  
Pathway Gene ◽  
Cancer Genome Atlas ◽  
Genome Atlas

AbstractUnderstanding the levels of metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Using transcriptomic data from 10,704 tumor and normal samples from The Cancer Genome Atlas, across 26 disease sites, we developed a novel bioinformatics pipeline that distinguishes tumor from normal tissues, based on differential gene expression for 114 metabolic pathways. This pathway dysregulation was confirmed in separate patient populations, further demonstrating the robustness of this approach. A bootstrapping simulation was then applied to assess whether these alterations were biologically meaningful, rather than expected by chance. We provide distinct examples of the types of analysis that can be accomplished with this tool to understand cancer specific metabolic dysregulation, highlighting novel pathways of interest in both common and rare disease sites. Utilizing a pathway mapping approach to understand patterns of metabolic flux, differential drug sensitivity, can accurately be predicted. Further, the identification of Master Metabolic Transcriptional Regulators, whose expression was highly correlated with pathway gene expression, explains why metabolic differences exist in different disease sites. We demonstrate these also have the ability to segregate patient populations and predict responders to different metabolism-targeted therapeutics.

Sign in / Sign up

Export Citation Format

Share Document