Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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List of Tables 1.1 Histological Types and Prognosis of Gliomas (y, years). Taken from Doyle et al 2005 [128]. . . . . . . . . . . . . . . . . . . . . 6 2.1 Summary of commonalities and differences between ES cells and NS cells. Adapted from Pollard et al 2006 [399] . . . . . . . . . 53 3.1 Summary of characteristics of NBE and serum-cultured glioblastoma cells. Adapted from Lee et al 2006. . . . . . . . . . . . . . 77 5.1 Summary of cell lines investigated with Tag-seq. . . . . . . . . . 95 5.2 Classification of sequenced tags in each cell line. . . . . . . . . . 99 5.3 Summary of statistics using the χ 2 and logarithmic tests. . . . . 111 5.4 Public gene expression datasets used in thesis. . . . . . . . . . . 113 6.1 Summary of the available clinical data for our GNS cell lines. . . 121 6.2 Summary of reads per cell line library. . . . . . . . . . . . . . . 121 6.3 Significance of the correlation found between CNAs and expression levels measured with Fisher’s exact test (p-value). . . . . . 128 6.4 Table of genes with large expression changes common to the GNS cell lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 6.5 Differentially expressed genes assigned to a four-tier classification system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 6.6 Genes with limited or no evidence of implication in glioblastoma that appear in our pathway. . . . . . . . . . . . . . . . . . . . . 145 6.7 Summary of predicted microRNAs targeting differentially expressed isoforms. . . . . . . . . . . . . . . . . . . . . . . . . . . 151 6.8 MicroRNA array results for GNS cell lines with respect to NS cell lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 6.9 Differentially expressed ncRNAs. . . . . . . . . . . . . . . . . . 157 312
7.1 Selected Gene Ontology terms and InterPro domains enriched among differentially expressed genes. . . . . . . . . . . . . . . . 163 7.2 Representative KEGG pathways from signaling pathway impact analysis of gene expression differences between GNS and NS lines.164 7.3 Summary of all MHC class I and II genes. . . . . . . . . . . . . 167 7.4 Literature survey for the 29 genes found to distinguish GNS from NS lines across a panel of 21 cell lines . . . . . . . . . . . . 175 7.5 Survival tests for the 29 genes found via qRT-PCR to distinguish GNS cell lines from NS cell lines. . . . . . . . . . . . . . . . . . 183 7.6 Significance of survival association for GNS signature and IDH1 status. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 7.7 Node assignment in the glioblastoma pathway. . . . . . . . . . . 190 8.1 microRNA target prediction algorithms used by GenemiR with number of microRNA:3'UTR interactions predicted. The origi- nal target identifiers refer to the identifiers used by a prediction algorithm to identify the targeted genes. The final target identifiers refer to the identifiers that are returned by any query of any prediction algorithm database. . . . . . . . . . . . . . . . . 209 8.2 Single prediction algorithm ensemble analysis results. Displayed in descending order of E-score. . . . . . . . . . . . . . . . . . . . 215 8.3 All combinations of prediction algorithms in descending order of E-score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 A.1 Classification of differentially expressed genes at 10% FDR. . . . 235 A.2 Classification of differentially expressed genes based on literature mining analysis. . . . . . . . . . . . . . . . . . . . . . . . . 251 A.3 Raw Ct values. Abbreviations: "down" for down-regulated, "up" for up-regulated, and "Norm" for Normalisation control. . 257 A.4 Normalised Ct values. Abbreviations: "down" for down-regulated, "up" for up-regulated, and "Norm" for Normalisation control. . 261 A.5 Pearson correlation values between the normalised Ct values measured through qRT-PCR and the tag counts measured across the five GNS and NS cell lines assayed via Tag-seq. . . . . . . . 265 C.1 Differentially expressed non-coding RNAs. . . . . . . . . . . . . 274 D.1 GBM pathway interaction data. . . . . . . . . . . . . . . . . . . 276
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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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6.2 Tag mapping Results Table 6.1:
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6.2 Tag mapping Results 123 Figure
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6.3 Copy Number Aberrations Results
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6.3 Copy Number Aberrations Results
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6.4 Core Differentially Expressed G
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6.5 Large-scale qRT-PCR Validation
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6.6 Literature Mining for Different
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6.7 Isoform Differential Expression
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6.8 Long ncRNA Differential Express
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6.8 Long ncRNA Differential Express
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Chapter 7 Dataset Correlation Analy
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7.1 Enrichment Analysis Results Tab
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7.1 Enrichment Analysis Results Fig
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7.1 Enrichment Analysis Results Tab
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7.2 Glioblastoma Expression Signatu
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7.3 Tumour Expression Correlation R
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7.4 Survival Analysis Results Table
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7.4 Survival Analysis Results 867 g
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7.5 Glioblastoma Pathway Analysis R
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8.1 Principles Results say, this is
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8.3 Databases Results Figure 8.1: O
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8.3 Databases Results Figure 8.3: W
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8.4 Filters Results Bayesian method
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8.5 Target Prediction Ensemble Anal
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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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Page 295 and 296: E. Exon Array Data Appendix Average
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