Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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7.3 Tumour Expression Correlation Results located on the long arm of chromosome 7, which we know is gained in our GNS cell lines. Thus, very effective transcriptional silencing mechanisms must be actively suppressing the TES gene. Finally, PLS3 encodes for an actin-binding protein that has yet to become associated with any form of neoplasia and that we find to be up-regulated in our GNS cell lines with respect to NS cell lines as measured by qRT-PCR. Figure 7.3: Correlation with glioblastoma subtype expression signatures for tissue samples and cell lines interrogated by Tag-seq. (a) Shows the heatmap from the Verhaak et al [511] paper and in (b) colours indicate the correlation (Pearson R) between subtype-specific centroid values determined by Verhaak et al [511] and gene expression in our indicated Tag-seq measured samples. Cells showing positive correlation are labeled with p-values indicating the significance of the correlation. 7.3 Tumour Expression Correlation To investigate whether the identified core set of differentially expressed genes that included 32 up-regulated and 60 down-regulated genes, showed similar expression patterns in primary tumours as in GNS lines, we made use of a cohort of public microarray data (see Methods, table 5.4). The core set of differentially expressed genes included genes with established roles in glioblastoma, such as PTEN [326] and CEBPB [85], as well as others not previously implicated in the disease. It was our interest to investigate 170
7.3 Tumour Expression Correlation Results how each of these genes behaved in different settings: stem cell component of the tumour (GNS cells), primary tumour and lower grade gliomas, given they differ significantly from one another under a biological perspective. In fact, the tissues in the primary tumours comprise a heterogeneous mixture of cell types, whilst our GNS culture system has been designed to maintain the very specific stem cell component of the tumour in culture. Thus, we did not expect a perfect agreement between tissue and cell based results. Fur- thermore, assessing the behaviour of these same genes in lower grade gliomas could give us insights into the key players that determine the severity of the disease. Considering that our GNS cell lines have all been classified as primary GBMs, the differences observed from the lower grade glioma datasets will be especially meaningful in telling us how the same genes behave in the two different categories (if we approximate that a significant percentage of the lower grade gliomas transform into higher grade gliomas such as GBMs). Panels a and c of figure 7.4 compare the GBM data in the TCGA dataset to the non-neoplastic brain tissue data in the TCGA dataset (altogether referred to as dataset GBM in Methods), for the core up-regulated (a) and core down- regulated (c) genes. Panels b and d, on the other hand, compare the GBM (grade IV glioma) data to the grade III glioma data in the combined Phillips and Freije datasets (altogether referred to as dataset HGG in Methods), for the core up-regulated (b) and core down-regulated (d) genes. From this figure we observe that there is a clear trend for the core up-regulated genes to be more highly expressed in glioblastoma tumours than in non-neoplastic brain tissue (Fig 7.4a; p = 0.02, randomisation test) and an opposite trend for the core down-regulated genes (Fig 7.4c; p = 3x10 -5 ). This means that, out of all the core differentially expressed genes that we measured with Tag-seq, a significant part of the ones we found to be more highly expressed in GNS cell lines with respect to NS cell lines are also more highly expressed in primary GBM with respect to non-neoplastic brain tissue (histogram bars in the direction of positive average log2(FC) identify these genes in panel a of figure 7.4). Similarly, a significant part of the core differentially expressed genes that we found to be down-regulated in our GNS cell lines with respect to our NS cell lines, are also down-regulated in primary GBM with respect to non-neoplastic brain tissue (histogram bars in the direction of negative average log2(FC) identify these genes in panel c of figure 7.4). The colour of the histogram bars represents the level of significance of the comparison with black indicating a p-value0.01. 171
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Transcriptional Characterization of
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This dissertation is my own work an
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Acknowledgements I would like to th
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5.9 External Dataset Expression Cor
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Introduction 1
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1.1 Glial Cells in the Central Nerv
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1.2 Glioblastoma Multiforme Introdu
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1.3 Primary and Secondary Glioblast
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1.4 Pathways Involved in Glioblasto
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1.5 Pathway Crosstalk Introduction
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Chapter 2 Neurogenesis Contents 2.1
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2.1 Radial Glia Introduction VZ adj
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2.2 Neural Stem Cells Introduction
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Chapter 3 Brain Cancer Stem Cells C
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3.1 The Cancer Stem Cell Hypothesis
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3.1 The Cancer Stem Cell Hypothesis
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3.2 Brain Cancer Stem Cells Introdu
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3.3 Glioma Culture Systems Introduc
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Chapter 4 The Non-Coding RNA World
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4.1 MicroRNA regulation Introductio
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4.1 MicroRNA regulation Introductio
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4.2 Target Prediction and Validatio
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Methods 93
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5.1 Tag-sequencing Data Processing
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5.1 Tag-sequencing Data Processing
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5.2 Array Comparative Genomic Hybri
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5.4 Quantitative Real Time-PCR Vali
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5.5 Literature Mining Methods Figur
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5.5 Literature Mining Methods How m
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5.6 Differential Isoform Expression
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5.9 External Dataset Expression Cor
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5.10 Glioblastoma Pathway Construct
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5.11 MicroRNA Target Prediction Ana
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9.1. Digital Profiling of GNS Cell
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9.2. MicroRNA Target Prediction Ana
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9.3. Concluding Remarks Appendix pr
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A.1 Differential Expression Appendi
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A.2 Classified Differential Express
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A.3 Quantitative RT-PCR Appendix Ta
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A.3 Quantitative RT-PCR Appendix We
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A.3 Quantitative RT-PCR Appendix Ta
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A.3 Quantitative RT-PCR Appendix We
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A.4 Tag-seq vs qRT-PCR Correlation
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Appendix "BEX5", "DIAPH2", "GBP3",
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End Select End If End Sub Appendix
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If NameStr.ToLower().Equals("query"
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Appendix C Long ncRNAs We detected
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C. Long ncRNAs Appendix Tag Accessi
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D.1 Pathway Interactions Appendix F
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D.2 Pathway Images Appendix Figure
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D.2 Pathway Images Appendix Figure
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E. Exon Array Data Appendix Average
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F. MicroRNA Array Data Appendix Tab
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F. MicroRNA Array Data Appendix GNS
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F. MicroRNA Array Data Appendix GNS
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Ln Natural logarithm LOH Loss of He
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3.3 Schematisation of cell cycle ph
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7.1 Selected Gene Ontology terms an
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