Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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1.3 Primary and Secondary Glioblastomas Introduction triphosphate hydrolases (GTPase) that activate the mitogen activated protein kinase (MAPK) signaling cascade amongst other pathways [496]. Overall, at least 47 of the 206 samples harboured somatic NF1 inactivating mutations or deletions, confirming the relevance of this gene in sporadic human glioblastoma [326]. Furthermore, within the TCGA dataset it was observed that mutation rates between untreated and treated glioblastomas were markedly different, aver- aging at 1.4 and 5.8 somatic silent mutations per sample, respectively. The higher average in the treated samples was mostly due to the contributions from a cohort of seven hypermutated tumours treated with temozolomide or lomustine. The hypermutator phenotypes previously described in glioblastoma [79,204] were known to carry mutations in MutS homolog 6 (MSH6), a component of the post-replicative DNA mismatch repair system (MMR). MSH6 heterodimerizes with MSH2 to form MutSα, which binds to DNA mis- matches thereby initiating DNA repair [6]. An analysis of the genes involved in mismatch repair within the TCGA dataset uncovered that six out of the seven hypermutated samples harbored mutations in at least one of the mismatch repair genes MLH1, MSH2, MSH6 or PMS2 [326]. Recurrent focal alterations found in the TCGA samples that have already been described and are common in glioblastoma are the amplification of Epi- dermal growth factor receptor (EGFR), cyclin-dependent-kinase (CDK) CDK4 and CDK6, PDGFRA, MDM2, MDM4, MET, MYCN, CCND2 and PIK2CA [104,255,262,292,374,423,429]. Interestingly, uncommon focal alterations were also found, such as the amplification of the serine/threonine protein kinase AKT3 and the homozygous deletions of NF1 and PARK2. V-akt murine thy- moma viral oncogene homolog 3 (AKT3) belongs to the AKT family of serine/threonine kinases together with AKT1 and AKT2. The three AKT kinases are now known to represent central nodes in a variety of signaling cascades that regulate normal cellular process such as cell size and growth, proliferation, survival, glucose metabolism, genome stability, and neo-vascularization. It is currently less clear, however, whether AKT1, AKT2, and AKT3 are function- ally redundant or whether each carries out a specific functional role [45,46]. Parkinson protein 2 (PARK2) encodes for a component of an E3 ubiquitin ligase complex that mediates the targeting of substrate proteins for protea- somal degradation. The functions carried by PARK2 are currently unknown, 14
1.3 Primary and Secondary Glioblastomas Introduction but mutations in this gene are known to cause a familial form of Parkinson’s disease known as autosomal recessive juvenile Parkinson disease [229]. The most significant loss-of-heterozygosity (LOH 15 ) event identified in the TCGA dataset was observed on the long arm of chromosome 17 where the TP53 gene resides [326]. Cytosine-phosphate-Guanine (CpG 16 ) islands are regions of DNA in which a cytosine is linked to a guanine via a phopsphodiester bond to form the dinu- cleotide CpG that is repeated many times along a linear sequence. In formal definitions, a CpG island must occupy more than 50% of a 200bp sequence and have an expected/observed CpG ratio of 0.6 [102,159]. Methylation of the 5’ carbon of cytosine is a form of epigenetic modification that affects regulation of gene expression via non-sequence based interactions. Such methylated cytosines are present in the coding regions of mammalian genes and, over evo- lutionary time, spontaneously deaminate to become thymines [102,287]. In mammals, 70% to 80% of CpG cytosines are methylated [208]. Oppositely, CpG islands in promoters tend to be unmethylated when genes are expressed, suggesting that methylation is an inhibiting event for gene expression [142,214]. Methylation of CpG sites within promoters has been observed as a mechanism of tumour suppressor gene silencing in a number of human cancers. In contrast, the hypomethylation of CpG sites has been associated with the over-expression of oncogenes within cancer cells [214]. Evaluation of promoter methylation was conducted in the TCGA project across 91 glioblastoma samples. A pattern emerged between the methylation of the O-6-methylguanine-DNA methyltransferase (MGMT) promoter and the sub- stitution spectrum of treated samples. MGMT is a DNA repair enzyme that repairs damaged alkylated guanine residues and its promoter methylation sta- tus has already been associated to sensitivity to the temozolomide alkylating agent, which is the current standard of care for glioblastoma patients [79,204]. Amongst the 13 samples treated with alkylating agent that did not show MGMT methylation, the validated somatic mutations from GC to AT, caused by the spontaneous deamination of methylated cytosines to thymines, occurred in comparable amounts between CpG and non-CpG dinucleotides. However, in the six treated samples with MGMT methylation, the GC to AT transitions 15 The loss of normal function of one allele of a gene in which the other allele was already inactivated. In the context of oncogenesis it refers to when the remaining functional allele in a somatic cell of the offspring becomes inactivated by mutation. 16 The "CpG" notation is used to distinguish CG base-pairing of cytosine and guanine. 15
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 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
Page 65 and 66: Chapter 3 Brain Cancer Stem Cells C
Page 67 and 68: 3.1 The Cancer Stem Cell Hypothesis
Page 69 and 70: 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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