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dc.contributor.authorGur-Dedeoglu, B.en_US
dc.contributor.authorKonu, O.en_US
dc.contributor.authorKir, S.en_US
dc.contributor.authorOzturk, A. R.en_US
dc.contributor.authorBozkurt, B.en_US
dc.contributor.authorErgul, G.en_US
dc.contributor.authorYulug, I.G.en_US
dc.date.accessioned2016-02-08T10:06:01Z
dc.date.available2016-02-08T10:06:01Z
dc.date.issued2008en_US
dc.identifier.issn1471-2407
dc.identifier.urihttp://hdl.handle.net/11693/22883
dc.description.abstractBackground: Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic gene-sets at the mRNA expression level have been tested as better predictors of disease state. However, breast cancer is heterogeneous in nature; thus extraction of differentially expressed gene-sets that stably distinguish normal tissue from various pathologies poses challenges. Meta-analysis of high-throughput expression data using a collection of statistical methodologies leads to the identification of robust tumor gene expression signatures. Methods: A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) samples were used for the meta-analysis. Expression of the genes, selected from the gene list for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes were tested on 10 independent primary IDC samples and matched non-tumor controls by real-time qRT-PCR. Other existing breast cancer microarray datasets were used in support of the resampling-based meta-analysis. Results: The two independent microarray studies were found to be comparable, although differing in their experimental methodologies (Pearson correlation coefficient, R = 0.9389 and R = 0.8465 for ductal and lobular samples, respectively). The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. The expression results of the selected genes obtained through real-time qRT-PCR supported the meta-analysis results. Conclusion: The proposed meta-analysis approach has the ability to detect a set of differentially expressed genes with the least amount of within-group variability, thus providing highly stable gene lists for class prediction. Increased statistical power and stringent filtering criteria used in the present study also make identification of novel candidate genes possible and may provide further insight to improve our understanding of breast cancer development.en_US
dc.language.isoEnglishen_US
dc.source.titleBMC Canceren_US
dc.relation.isversionofhttp://dx.doi.org/10.1186/1471-2407-8-396en_US
dc.subjectActivating transcription factor 3en_US
dc.subjectBinding proteinen_US
dc.subjectProtein ADAMTS1en_US
dc.subjectProtein COX6Cen_US
dc.subjectProtein FN1en_US
dc.subjectProtein GSNen_US
dc.subjectprotein GSPT1en_US
dc.subjectProtein ID4en_US
dc.subjectProtein IGFBP6en_US
dc.subjectProtein NME1en_US
dc.subjectProtein PRNPen_US
dc.subjectProtein Rad21en_US
dc.subjectProtein SPTBN1en_US
dc.subjectSecreted frizzled related protein 1en_US
dc.subjectSecurinen_US
dc.subjectTranscription factor Mafen_US
dc.subjectUnclassified drugen_US
dc.subjectVasculotropin receptor 1en_US
dc.subjectArticleen_US
dc.subjectBreast canceren_US
dc.subjectCancer geneticsen_US
dc.subjectClinical articleen_US
dc.subjectControlled studyen_US
dc.subjectGene expression profilingen_US
dc.subjectGene identificationen_US
dc.subjectGenetic algorithmen_US
dc.subjectGenetic variabilityen_US
dc.subjectHumanen_US
dc.subjectHuman tissueen_US
dc.subjectInformation processingen_US
dc.subjectInformation retrievalen_US
dc.subjectIntraductal carcinomaen_US
dc.subjectLung carcinomaen_US
dc.subjectReal time polymerase chain reactionen_US
dc.subjectReverse transcription polymerase chain reactionen_US
dc.subjectRNA extractionen_US
dc.subjectSample sizeen_US
dc.subjectValidation processen_US
dc.subjectBreasten_US
dc.subjectBreast tumoren_US
dc.subjectDNA microarrayen_US
dc.subjectFemaleen_US
dc.subjectGene expression regulationen_US
dc.subjectGenetic databaseen_US
dc.subjectGeneticsen_US
dc.subjectMeta analysisen_US
dc.subjectMetabolismen_US
dc.subjectNonparametric testen_US
dc.subjectPaget nipple diseaseen_US
dc.subjectReproducibilityen_US
dc.subjectBreasten_US
dc.subjectBreast Neoplasmsen_US
dc.subjectCarcinoma, Ductal, Breasten_US
dc.subjectCarcinoma, Lobularen_US
dc.subjectDatabases, Geneticen_US
dc.subjectFemaleen_US
dc.subjectGene expression regulation, Neoplasticen_US
dc.subjectHumansen_US
dc.subjectOligonucleotide array sequence analysisen_US
dc.subjectReproducibility of resultsen_US
dc.subjectReverse transcriptase polymerase chain reactionen_US
dc.subjectStatistics, Nonparametricen_US
dc.titleA resampling-based meta-analysis for detection of differential gene expression in breast canceren_US
dc.typeArticleen_US
dc.departmentDepartment of Molecular Biology and Genetics
dc.citation.spage396-1en_US
dc.citation.epage396-16en_US
dc.citation.volumeNumber8en_US
dc.identifier.doi10.1186/1471-2407-8-396en_US
dc.publisherBioMed Centralen_US


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