Functional genomics in translational cancer research: focus on breast cancer

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dc.citation.epage7en_US
dc.citation.issueNumber1en_US
dc.citation.spage1en_US
dc.citation.volumeNumber7en_US
dc.contributor.authorYulug, I. G.en_US
dc.contributor.authorGur-Dedeoglu, B.en_US
dc.date.accessioned2016-02-08T10:10:37Z
dc.date.available2016-02-08T10:10:37Z
dc.date.issued2008en_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.description.abstractConventional molecular and genetic methods for studying cancer are limited to the analysis of one locus at a time. A cluster of genes that are regulated together can be identified by DNA microarray, and the functional relationships can uncover new aspects of cancer biology. Breast cancer can be used to provide a model to demonstrate the current approaches to the molecular analysis of cancer. Meta-analysis is an important tool for the identification and validation of differentially expressed genes to increase power in clinical and biological studies across different sets of data. Recently, meta-analysis approaches have been applied to large collections of microarray datasets to investigate molecular commonalities of multiple cancer types not only to find the common molecular pathways in tumour development but also to compare the individual datasets to other cancer datasets to identify new sets of genes. Several investigators agree that microarray results should be validated. One commonly used method is quantitative reverse transcription PCR (qRT-PCR) to validate the expression profiles of the target genes obtained through microarray experiments. qRT-PCR is attractive for clinical use, since it can be automated and performed on fresh or archived formalin-fixed, paraffin-embedded tissue samples. The outcome of these analyses might accelerate the application of basic research findings into daily clinical practice through translational research and may have an impact on foreseeing the clinical outcome, predicting tumour response to specific therapy, identification of new prognostic biomarkers, discovering targets for the development of novel therapies and providing further insights into tumour biology. © The Author 2008. Published by Oxford University Press.en_US
dc.identifier.doi10.1093/bfgp/eln009en_US
dc.identifier.issn1473-9550
dc.identifier.urihttp://hdl.handle.net/11693/23233
dc.language.isoEnglishen_US
dc.publisherOxford University Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1093/bfgp/eln009en_US
dc.source.titleBriefings in Functional Genomics and Proteomicsen_US
dc.subjectBreast canceren_US
dc.subjectFunctional genomicsen_US
dc.subjectGene expressionen_US
dc.subjectMeta-analysisen_US
dc.subjectMicroarraysen_US
dc.subjectqRT-PCRen_US
dc.subjectBiological markeren_US
dc.subjectDNAen_US
dc.titleFunctional genomics in translational cancer research: focus on breast canceren_US
dc.typeArticleen_US

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