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dc.contributor.authorWarner, Giles C
dc.description.abstractThe purpose of this study was to classify oral squamous cell carcinomas (OSCCs) based on their gene expression profiles, to identify differentially expressed genes in these cancers, and to correlate genetic deregulation with clinical-histopathological data and patient outcome. After conducting proof of principle experiments utilizing six head and neck squamous cell carcinomas (HNSCCs) cell lines, the gene expression profiles of 20 OSCCs and subsequently an additional 8 OSCCs were determined using cDNA microarrays containing 19,200 sequences and the Binary Tree-Structured Vector Quantization (BTSVQ) method of data analysis. Two sample clusters were identified in the group of 20 tumors that correlated with T3-T4 category of disease (P=0.035) and nodal metastasis( p=0.035). Samplec lustering of 28 OSCCsa nd the 6 cell lines revealed a correlation with disease free survival. BTSVQ analysis identified a subset of 23 differentially expressed genes with the lowest quantization error scores in the cluster containing more advanceds taget umors from the 20 OSCC dataset.T he expressiono f six of these differentially expressedg enesw as validated by quantitative real-time RT-PCR. Statistical analysis of quantitative real-time RT-PCR data was performed and, after Bonferroni correction, CLDNI (p = 0.007) over-expressionw as significantly correlated with the cluster containing more advanced stage tumors. Despite the clinical heterogeneity of OSCC, molecular subtyping by cDNA microarray analysis was able to identify distinct patternso f genee xpressiona ssociatedw ith relevant clinical parameters. The application of this methodology represents an advance in the classification of oral cavity tumors, and may ultimately aid in the development of more tailored therapies for oral carcinoma.en_US
dc.publisherQueen Mary University of London
dc.titleGene expression profiling of head and neck canceren_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author

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  • Theses [3822]
    Theses Awarded by Queen Mary University of London

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