Show simple item record

dc.contributor.authorWarrenfeltz, Susanneen_US
dc.contributor.authorPavlik, Stephenen_US
dc.contributor.authorDatta, Susmitaen_US
dc.contributor.authorKraemer, Eileen T.en_US
dc.contributor.authorBenigno, Benedict B.en_US
dc.contributor.authorMcDonald, John F.en_US
dc.date.accessioned2010-03-23T15:42:10Z
dc.date.available2010-03-23T15:42:10Z
dc.date.issued2004-10-07
dc.identifier.citationSusanne Warrenfeltz, Stephen Pavlik, Susmita Datta, Eileen T. Kraemer, Benedict Benigno and John F. McDonald, "Gene expression profiling of epithelial ovarian tumours correlated with malignant potential," Molecular Cancer 2004, 3:27en
dc.identifier.issn1476-4598
dc.identifier.urihttp://hdl.handle.net/1853/32466
dc.description© 2004 Warrenfeltz et al; licensee BioMed Central Ltd.The electronic version of this article is the complete one and can be found online at: http://www.molecular-cancer.com/content/3/1/27en
dc.descriptionDOI: 10.1186/1476-4598-3-27
dc.description.abstractBackground Epithelial ovarian tumours exhibit a range of malignant potential, presenting distinct clinical phenotypes. Improved knowledge of gene expression changes and functional pathways associated with these clinical phenotypes may lead to new treatment targets, markers for early detection and a better understanding of disease progression. Results Gene expression profiling (Affymetrix, U95Av2) was carried out on 18 ovarian tumours including benign adenomas, borderline adenocarcinomas of low malignant potential and malignant adenocarcinomas. Clustering the expression profiles of samples from patients not treated with chemotherapy prior to surgery effectively classified 92% of samples into their proper histopathological group. Some cancer samples from patients treated with chemotherapy prior to surgery clustered with the benign adenomas. Chemotherapy patients whose tumours exhibited benign-like expression patterns remained disease free for the duration of this study as indicated by continued normal serum CA-125 levels. Statistical analysis identified 163 differentially expressed genes: 61 genes under-expressed in cancer and 102 genes over-expressed in cancer. Profiling the functional categories of co-ordinately expressed genes within this list revealed significant correlation between increased malignant potential and loss of both IGF binding proteins and cell adhesion molecules. Interestingly, in several instances co-ordinately expressed genes sharing biological function also shared chromosomal location. Conclusion Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. Expression levels in borderline tumours were intermediate between benign adenomas and malignant adenocarcinomas for a significant portion of the differentially expressed genes, suggesting that borderline tumours are a transitional state between benign and malignant tumours. Finally, genes displaying coordinated changes in gene expression were often genetically linked, suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic events.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectOvarian tumorsen
dc.subjectGene expression profilingen
dc.subjectHistopathological groupsen
dc.subjectRecurrenceen
dc.subjectMalignant potentialen
dc.titleGene expression profiling of epithelial ovarian tumours correlated with malignant potentialen
dc.typeArticleen
dc.contributor.corporatenameUniversity of Georgia. Dept. of Geneticsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Ovarian Cancer Instituteen_US
dc.contributor.corporatenameUniversity of Georgia. Dept. of Computer Scienceen_US
dc.contributor.corporatenameGeorgia State University. Dept. of Mathematics and Statisticsen_US
dc.publisher.originalBioMed Central
dc.identifier.doi10.1186/1476-4598-3-27


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record