Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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3.1 The Cancer Stem Cell Hypothesis Introduction Figure 3.1: Stem cell differentiation hierarchy, in which a possible increase in plasticity is highlighted that may be present within cancer populations. Such plasticity would be the enabler for the bidirectional interconvertibility between cancer stem cells and non-cancer stem cells. Adapted from Gupta et al 2009 [182]. tissues. This type of cell division is referred to as "asymmetrical division" or "asymmetrical self-renewal" and is specifically characterised by adult stem cell divisions that produce a new adult stem cell together with a non-stem cell sis- ter that becomes the progenitor for short-lived, differentiating, functional cells that in most cases mature to a terminal division arrest [420]. For the vast ma- jority of adult tissues, it remains unclear how stem cells succeed in maintaining a precise balance between proliferation and differentiation in steady-state. To explain their long-term viability, it has been argued that tissue stem cells are maintained in a long-lived quiescent state, with most divisions supported by differentiating progenitor cells that ultimately exit the cell cycle and are re- placed by stem cell progeny, which provides a mechanism for protecting stem cells from damage and loss throughout adult life (Fig 3.2). Clear evidence for asymmetric stem cell divisions is found in invertebrates C. elegans and D. melanogaster, although in recent lineage-tracing studies in mammals it was shown that stem cells behave as an equipotent population, in which the balance between proliferation and differentiation is achieved through frequent and stochastic stem cell loss and replacement [240]. While the asymmetrical self- renewing properties of adult stem cells are particularly important for homeo- static control in adult tissues that undergo continuous cellular turnover such as epithelium and blood [457,486], the non-stem cells of most adult tissues go through a rapid cycle in which they are born, mature, expire, and are removed from the tissue by apoptosis. The fact that the rate of adult cellular turnover is much faster than that of tumour development is the basis for the hypothesis 58
3.1 The Cancer Stem Cell Hypothesis Introduction Figure 3.2: (a) Asymmetric cell division, in which each stem cell (orange) generates one daughter stem cell and one daughter destined to differentiate (green). (b-c) Population strategies that provide dynamic control over the balance between stem cells and differentiated cells, a capacity that is necessary for repair after injury or disease. In this scheme, stem cells are defined by their "potential" to generate both stem cells and differentiated daughters, rather than their actual production of a stem cell and a differentiated cell at each division. (b) Symmetric cell division: each stem cell can divide symmetrically to generate either two daughter stem cells or two differentiated cells. (c) Combination of cell divisions: each stem cell can divide either symmetrically or asymmetrically. Adapted from Morrison et al 2006 [345]. that non-stem cells cannot effectively initiate cancers, a concept that contrasts with the commonly held idea that cancers may arise from any tissue cell with equal likelihood [420]. Although adult stem cells are attractive candidates to fulfill the role of tumour-initiating cells, two of their properties are also considered limitations to that end: firstly, asymmetric division potentially limits the number of stem cells and therefore the incidence with which they could drive tumourigenesis, and secondly, the immortal DNA strand co-segregation 35 process reduces the rate at which they can accumulate mutations. Through the DNA strand co-segregation molecular manoeuvre, adult stem cells reduce their mutation rate by more than 1000-fold, avoiding all mutations that arise from replication errors that are not properly repaired [420]. Since all stem cells alike must self-renew and regulate the relative balance between self-renewal and differentiation, and cancer can be considered a disease of unregulated self-renewal, understanding the regulation of normal stem cell self-renewal is fundamental to understanding the regulation of cancer cell proliferation [458]. By maintaining at least some of the properties of their tissue 35 Non random segregation of the set of chromosomes with the oldest template of the DNA strands operated at each cell division. 59
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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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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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