Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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Contents Page Introduction 1 1 Glioblastoma 2 1.1 Glial Cells in the Central Nervous System . . . . . . . . . . . . 2 1.2 Glioblastoma Multiforme . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Primary and Secondary Glioblastomas . . . . . . . . . . . . . . 10 1.4 Pathways Involved in Glioblastoma . . . . . . . . . . . . . . . . 21 1.5 Pathway Crosstalk . . . . . . . . . . . . . . . . . . . . . . . . . 30 2 Neurogenesis 33 2.1 Radial Glia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Neural Stem Cells . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3 Brain Cancer Stem Cells 57 3.1 The Cancer Stem Cell Hypothesis . . . . . . . . . . . . . . . . . 57 3.2 Brain Cancer Stem Cells . . . . . . . . . . . . . . . . . . . . . . 63 3.3 Glioma Culture Systems . . . . . . . . . . . . . . . . . . . . . . 72 4 The Non-Coding RNA World 85 4.1 MicroRNA regulation . . . . . . . . . . . . . . . . . . . . . . . . 86 4.2 Target Prediction and Validation . . . . . . . . . . . . . . . . . 90 Methods 93 5 Methods 94 5.1 Tag-sequencing Data Processing . . . . . . . . . . . . . . . . . . 94 5.2 Array Comparative Genomic Hybridization . . . . . . . . . . . . 98 5.3 Differential Gene Expression . . . . . . . . . . . . . . . . . . . . 100 5.4 Quantitative Real Time-PCR Validation . . . . . . . . . . . . . 101 5.5 Literature Mining . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.6 Differential Isoform Expression . . . . . . . . . . . . . . . . . . . 110 5.7 Differential Long ncRNA Expression . . . . . . . . . . . . . . . 112 5.8 Glioma Expression Signatures . . . . . . . . . . . . . . . . . . . 112 i
5.9 External Dataset Expression Correlation . . . . . . . . . . . . . 112 5.10 Glioblastoma Pathway Construction . . . . . . . . . . . . . . . . 114 5.11 MicroRNA Target Prediction Analysis . . . . . . . . . . . . . . 117 Results 119 6 Digital Transcriptome Profiling 120 6.1 Clinical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6.2 Tag mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.3 Copy Number Aberrations . . . . . . . . . . . . . . . . . . . . . 125 6.4 Core Differentially Expressed Genes . . . . . . . . . . . . . . . . 129 6.5 Large-scale qRT-PCR Validation . . . . . . . . . . . . . . . . . 136 6.6 Literature Mining for Differentially Expressed Genes . . . . . . 142 6.7 Isoform Differential Expression . . . . . . . . . . . . . . . . . . . 144 6.8 Long ncRNA Differential Expression . . . . . . . . . . . . . . . 157 7 Dataset Correlation Analyses 161 7.1 Enrichment Analysis . . . . . . . . . . . . . . . . . . . . . . . . 161 7.2 Glioblastoma Expression Signatures . . . . . . . . . . . . . . . . 168 7.3 Tumour Expression Correlation . . . . . . . . . . . . . . . . . . 170 7.4 Survival Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 182 7.5 Glioblastoma Pathway Analysis . . . . . . . . . . . . . . . . . . 187 8 MicroRNA Target Prediction Ensemble Software 202 8.1 Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 8.2 Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 8.3 Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 8.4 Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 8.5 Target Prediction Ensemble Analysis . . . . . . . . . . . . . . . 211 Conclusions 219 9 Discussion 220 9.1 Digital Profiling of GNS Cell Lines . . . . . . . . . . . . . . . . 220 9.2 MicroRNA Target Prediction Analysis . . . . . . . . . . . . . . 231 9.3 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . 232 Appendix A Differentially Expressed Genes 234 A.1 Differential Expression . . . . . . . . . . . . . . . . . . . . . . . 234 A.2 Classified Differential Expression . . . . . . . . . . . . . . . . . 250 A.3 Quantitative RT-PCR . . . . . . . . . . . . . . . . . . . . . . . 256 A.4 Tag-seq vs qRT-PCR Correlation . . . . . . . . . . . . . . . . . 265 Appendix B Literature Mining Script 266 ii
Page 1 and 2: Transcriptional Characterization of
Page 3 and 4: This dissertation is my own work an
Page 5: Acknowledgements I would like to th
Page 9 and 10: Introduction 1
Page 11 and 12: 1.1 Glial Cells in the Central Nerv
Page 13 and 14: 1.2 Glioblastoma Multiforme Introdu
Page 19 and 20: 1.3 Primary and Secondary Glioblast
Page 29 and 30: 1.4 Pathways Involved in Glioblasto
Page 39 and 40: 1.5 Pathway Crosstalk Introduction
Page 41 and 42: Chapter 2 Neurogenesis Contents 2.1
Page 43 and 44: 2.1 Radial Glia Introduction VZ adj
Page 45 and 46: 2.2 Neural Stem Cells Introduction
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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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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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Bibliography [1] Cell signaling tec
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[38] G. Bain, D. Kitchens, M. Yao,
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[268] H. Lemjabbar-Alaoui, A. van Z
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