Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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9.3. Concluding Remarks Discussion in context with other genomic information in order to elucidate regulatory function. In an attempt to elucidate the answer to the research question of interest, the hypothesis put forward by Alexiou et al [16] that combinations of algorithms predict more accurately than the single algorithm alone was chal- lenged. Through the analysis described in this thesis it is clear that the target prediction algorithm ElMMo [155]is at the head of the list sorted by accuracy score out of the 258 combinations tested for. Although this analysis shows that ElMMo in particular has the most accurate target prediction algorithm within the pool of eight tested prediction algorithms, it must be noted that there is a substantial discrepancy between the score achieved by ElMMo and the next best score for a single target prediction algorithm. Before Diana- microT [237,316], the second highest solo score, many combinations of two to three target prediction algorithms appear earlier in the list, demonstrating the potential for better accuracy than another solo prediction algorithm. Although it is refreshing to see that one target prediction algorithm can fare better than the rest, the question remains as to whether the variables and factors that are taken into consideration, and the ones that are not, are fairly judged in the algorithm so as to simulate the intricate regulations happening within the cell. In fact, during this analysis the accuracy score of each prediction algorithm decreased greatly when a background gene list was not used to filter out all the genes that were predicted but were not tissue specific. While the results of this analysis indicate that some algorithms alone and some group of algorithms are better than others, it highlights at the same time the importance of re-assessing the factors that are taken into consideration to make the microRNA target predictions. If we have reached the limit of what sequence-based algorithms can achieve, we ought to start thinking of adding another dimension to the field of microRNA target prediction algorithms that takes factors in components of the tissue-specific regulatory actions of microR- NAs. 9.3 Concluding Remarks In this thesis I aimed to characterise the transcriptional landscape of Glioma Neural Stem Cells (GNS) in the most comprehensive way possible. Several approaches were taken to examine the expression profiling data that gave insights into the stem cell component of the biology of these cells, which had 232
9.3. Concluding Remarks Appendix previously been overlooked due to the suboptimal culturing systems for the maintenance of pluripotency. To this end I have approached the analysis from the coding transcriptome and the non-coding transcriptome point of view, trying to draw as many parallels as possible and as many comparisons as possible with other external datasets that could give insights in the characteristics the are unique to the GNS sys- tem. Pairing the available expression data with new DNA methylation data would allow us to gain insights into the epigenetic mechanisms at work. It is, in fact, important that the future works in this field and with these cell lines are directed towards a deeper understanding of the mechanisms that elect and maintain them as tumour-initiating agents of the glioblastoma primary tumour. 233
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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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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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