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Journal article
FXYD5 is a Marker for Poor Prognosis and a Potential Driver for Metastasis in Ovarian Carcinomas
Cancer informatics, v 14(2015), pp 113-119
12 Oct 2015
PMID: 26494976
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Ovarian cancer (OC) is a leading cause of cancer mortality, but aside from a few well-studied mutations, very little is known about its underlying causes. As such, we performed survival analysis on ovarian copy number amplifications and gene expression datasets presented by The Cancer Genome Atlas in order to identify potential drivers and markers of aggressive OC. Additionally, two independent datasets from the Gene Expression Omnibus web platform were used to validate the identified markers. Based on our analysis, we identified
FXYD5
, a glycoprotein known to reduce cell adhesion, as a potential driver of metastasis and a significant predictor of mortality in OC. As a marker of poor outcome, the protein has effective antibodies against it for use in tissue arrays.
FXYD5
bridges together a wide variety of cancers, including ovarian, breast cancer stage II, thyroid, colorectal, pancreatic, and head and neck cancers for metastasis studies.
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Details
- Title
- FXYD5 is a Marker for Poor Prognosis and a Potential Driver for Metastasis in Ovarian Carcinomas
- Creators
- Pichai Raman - School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA ; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, PA, USA ; Fara Diagnostics, Philadelphia, PA, USA.Timothy Purwin - Drexel UniversityRichard Pestell - Thomas Jefferson UniversityAydin Tozeren - Drexel University
- Publication Details
- Cancer informatics, v 14(2015), pp 113-119
- Publisher
- Libertas Academica
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:000383958600002
- Scopus ID
- 2-s2.0-84949951649
- Other Identifier
- 991019168557904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Oncology