Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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7.5 Glioblastoma Pathway Analysis Results Figure 7.7: The integrated glioblastoma pathway is subdivided into rough sections contained within the orange and blue boxes identifying the gene networks that participate in the pathway. In the orange boxes the "classic" glioblastoma pathways are highlighted: TP53, RB1 and PTEN signaling; in the blue boxes the cancer and glioblastoma-specific pathways are represented. 188
7.5 Glioblastoma Pathway Analysis Results Figure 7.7 shows the integrated pathway in its default colours, without any overlaid expression data that would colour the gene nodes. Sections pertaining to different pathways are coloured in blue if the pathways are glioblastoma- specific or cancer-specific and orange if they reflect the cross-talk between the three well-known pathways commonly affected in glioblastoma. The colour, line and endpoints of the edges represent the type of interactions between the nodes: activation (solid, green, arrow point), transcriptional activation (solid, green, diamond point), tentative activation (dashed, green, arrow point), inhibition (solid, red, T point), transcriptional inhibition (solid, red, delta point), includes (solid, black, circle point), becomes (solid, black, top half arrow point), simple interaction (solid, grey, no endpoint), leading to (dashed, grey, arrow), lets in (dashed, grey, no endpoint). The shape of the node represents the type: gene (round), complex (hexagon), family (hexagon), molecule (fee), process (round rectangle). Finally, the beige colour distinguishes genes (grey if not overlaid with colour-coded expression data) from non genes (beige). All the interactions described by the pathway can be found in Appendix D.1. In order to ensure that every single interaction described in the pathway had been experimentally validated I checked every one of them in the interactions databases described in detail in the Methods section, such as BioGrid [62] and Intact [205]. Thus, this glioblastoma pathway is a representation of physically interacting proteins at work in the specific disease context. When the interaction needs to occur with chromatin to describe the regulation properly, such as in the case of a transcription factor, either a node named "DNA" is described as one of the two interactors, or a special edge named "activates transcription of" or "inhibits transcription of" - that can be identified by the diamond or delta endpoint, respectively - connects the two interactors. The entire pathway can be recreated from the list of interactions and interactors described in Ap- pendix D.1, but an editable version - with extension CYS - can be downloaded from www.ebi.ac.uk/~diva/GBM-pathway.cys, where the latest and previous versions of the pathway are available. The CYS extension allows the pathway to be viewed and/or edited with any network editing software, such as Cy- toscape [451]. The availability of the pathway for download and use by other researchers, as well as its constant curation, attempts to address one of the four main shortcomings of already existing GBM pathways: the unavailability of formats other than images. Hopefully this resource will now be used by all the researchers who want to overlay expression data on a new comprehensive integrated GBM pathway to observe changes in the wider disease context. 189
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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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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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