Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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7.3 Tumour Expression Correlation Results Figure 7.4: Core gene expression changes in GNS lines are mirrored in glioblastoma tumours. Gene expression in tumours for the core up-regulated (a, b) and downregulated (c, d) genes. The gene sets were identified by comparison of Tag-seq expression profiles for GNS and NS cell lines. Bars depict average FC between glioblastoma and non-neoplastic brain tissue (a, c) and between glioblastoma and grade III astrocytoma (b, d). Black bars indicate genes with significant differential expression in the microarray data (p < 0.01). The heatmaps show expression in individual samples relative to the average level in non-neoplastic brain (a, c) or grade III astrocytoma (b, d). The side dots point to the genes that belong to the cohort of 29 differentially expressed genes measured via qRT-PCR and have the same colour gradient adopted in figure 6.13. One gene (CHCHD10) not quantified in the TCGA dataset is omitted from panel a. 172
7.3 Tumour Expression Correlation Results The orientation of the bar represents the direction of FC and the height of the bar the magnitude of the FC (−3 < F C > 3). We hypothesised that expression of these genes might also differ between glioblastoma and less severe gliomas and, upon examination of the expression patterns for grade III gliomas and glioblastoma in the dataset HGG, we found the core up-regulated genes to be more highly expressed in glioblastoma than in grade III glioma (Fig 7.4b; p < 10 -6 ), while the core down-regulated genes showed the opposite pattern (Fig 7.4d; p < 10 -5 ). Thus, of the core differentially expressed genes we measured with Tag-seq, a significant portion of the highly expressed ones in GNS cell lines with respect to NS cell lines, were also more highly expressed in grade IV gliomas (GBMs) with respect to grade III gliomas (histogram bars in the direction of positive average log2(FC) identify these genes in panel b of figure 7.4). This trend may be indicative of the fact that the core up-regulated genes in our GNS cell lines that are also up-regulated in GBM primary tumours, belong to a cohort of genes involved in regulatory networks already activated in grade III gliomas. The very fact that the same genes are similarly over expressed in our GNS cell lines and in grade III gliomas may be an indication of a specific prognosis, namely the progression to a higher grade glioma. Of course, at this level of analysis this is only a hypothesis and it would be interesting to test it further and try to establish if it can be used as a pre-diagnostic tool. Similarly to the matching trends observed for the core up-regulated genes, a significant portion of the differentially expressed genes that we found to be down-regulated in GNS cell lines with respect to NS cell lines, were also down-regulated in grade IV gliomas (GBMs) with respect to grade III gliomas (histogram bars in the direction of negative average log2(FC) identify these genes in panel d of figure 7.4). This opposite trend is not surprising consid- ering the hypothesis mentioned before. If the regulatory networks involved in the progression of the disease from a grade III to a grade IV have already been activated in grade III gliomas, then a set of complementary genes and regulatory networks must be silenced and rendered transcriptionally inactive. In this scenario postulating a diagnostic tool that derives from the combination of the two oppositely moving groups of genes may serve a pre-diagnostic tool purpose well. Of course, a specific study would need to be carried to verify the validity of this hypothesis. The dots next to the gene names on the sides of the heatmaps in figure 7.4 are there to highlight whether a gene also belongs to the cohort of the 29 genes found via qRT-PCR to distinguish GNS cell lines from NS cell lines. The colour 173
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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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Results 119
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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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8.3 Workflows at the core of the pr
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7.1 Selected Gene Ontology terms an
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