Meta-analysis of microarray data identifies GAS6 expression as an independent predictor of poor survival in ovarian cancer

Buehler, Michelle, Tse, Brian, Leboucq, Alix, Jacob, Francis, Caduff, Rosmarie, Fink, Daniel, Goldstein, Darlene R., & Heinzelmann-Schwarz, Viola (2013) Meta-analysis of microarray data identifies GAS6 expression as an independent predictor of poor survival in ovarian cancer. BioMed Research International, 2013, pp. 238284-1.

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Abstract

Seeking new biomarkers for epithelial ovarian cancer, the fifth most common cause of death from all cancers in women and the leading cause of death from gynaecological malignancies, we performed a meta-analysis of three independent studies and compared the results in regard to clinicopathological parameters. This analysis revealed that GAS6 was highly expressed in ovarian cancer and therefore was selected as our candidate of choice. GAS6 encodes a secreted protein involved in physiological processes including cell proliferation, chemotaxis, and cell survival. We performed immunohistochemistry on various ovarian cancer tissues and found that GAS6 expression was elevated in tumour tissue samples compared to healthy control samples (P < 0.0001). In addition, GAS6 expression was also higher in tumours from patients with residual disease compared to those without. Our data propose GAS6 as an independent predictor of poor survival, suggesting GAS6, both on the mRNA and on the protein level, as a potential biomarker for ovarian cancer. In clinical practice, the staining of a tumour biopsy for GAS6 may be useful to assess cancer prognosis and/or to monitor disease progression.

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9 citations in Scopus
6 citations in Web of Science®
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ID Code: 77700
Item Type: Journal Article
Refereed: Yes
DOI: 10.1155/2013/238284
ISSN: 2314-6141
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2013 Michelle Buehler et al.
Copyright Statement: This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Deposited On: 16 Oct 2014 01:18
Last Modified: 23 Jun 2017 00:01

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