Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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1.4 Pathways Involved in Glioblastoma Introduction which regulate cell cycle primarily by governing the G1/S 18 phase transition, are major targets of inactivating mutations in glioblastoma and their absence renders tumours particularly susceptible to inappropriate cell division driven by constitutively active mitogenic signaling effectors, such as PI3K and MAP kinases [154]. The same three pathways were singled out in the TCGA project when mapping the somatic nucleotide substitutions, homozygous deletions and focal amplifications, onto the major pathways implicated in glioblastoma. A statistical tendency was observed in which components within each pathway were altered in a mutually exclusive fashion, hinting at deregulation of one com- ponent relieving the selective pressure for additional ones in the same pathway. Also, 74% of the samples harbored aberrations in all three pathways, suggesting a functional non-redundancy between the three as a key requirement for glioblastoma pathogenesis [326]. The study by Parsons et al in 2008 [383] detected critical genes within important cell signaling pathways in glioma: TP53, Mdm2 p53 binding protein homolog (MDM2) and Mdm4 p53 binding protein homolog (MDM4) in the p53 pathway; RB1, CDK4 and CDKN2A in the Rb pathway and PIK3CA, PIK3R1, PTEN and IRS1 in the RTK/PI3K/PTEN pathway. All but one of the cancers bearing mutations in members of one of these three pathways did not show alterations in any of the other two, suggesting functional redundancy for these mutations in tumourigenesis [383]. RTK/PI3K/PTEN Pathway Tumour cells acquire genomic alterations that greatly reduce their dependence on exogenous growth stimulation via transmembrane receptor contact with dif- fusible growth factors, cell to cell adhesion, or extracellular matrix. Most often these cells enable inappropriate cell division, survival, and motility through the constitutive activation of the PI3K and MAPK pathways. The predominant mechanism of mitogenic signaling activation for gliomas occurs through RTKs, high-affinity cell surface receptors that bind and transduce the signal from cy- tokines, growth factors and hormones, and integrins, membrane-bound receptors that mediate the interaction between the extracellular matrix and the cy- toskeleton [154]. Receptors that belong to the RTK family are EGFR, PDGFR, MET, FGFR and VEGFR [431]. The epidermal growth factor EGF and the platelet-derived growth factor PDGF pathways play important roles in both 18 Major checkpoint in the regulation of cell cycle beyond which the cell is committed to dividing. 22
1.4 Pathways Involved in Glioblastoma Introduction 23 Figure 1.2: KEGG Glioma Pathway [2]. Oncogenes and tumour suppressors are highlighted in red.
Page 1 and 2: Transcriptional Characterization of
Page 3 and 4: This dissertation is my own work an
Page 5 and 6: Acknowledgements I would like to th
Page 7 and 8: 5.9 External Dataset Expression Cor
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
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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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Page 67 and 68: 3.1 The Cancer Stem Cell Hypothesis
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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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