Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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9.1. Digital Profiling of GNS Cell Lines Discussion ciated with the poorest prognosis. G144, instead, with its high oligodendrocyte component known to positively correlate with patient survival rates in glioblastoma, was assigned to the proneural signature, linked with neural markers and therefore the best prognosis of all. In comparing the GNS line expression profiles to the subtype signatures, we found that both G166 and G179 correlated strongly with the mesenchymal signature with worst prognosis. Mesenchymal subtype markers with elevated expression in these two lines included MET, CD44, CD68 and CASP1 [511]. G144 did not correlate significantly with any of the four signatures, but showed a slight positive correlation with the proneural one. Supporting such classification of G144 were several of the hallmarks of the proneural subtype emphasised by Verhaak et al 2010 [511]: high expression of oligodendrocytic development genes PDGFRA, NKX2-2 and OLIG2, as well as ERBB3, DCX and TCF4 genes and low levels of tumour suppressor CDKN1A. These expression signature profile studies should be performed on an always greater number of glioblastoma samples in order for them to be able to capture even finer differences than the ones proposed by the study by Ver- haak et al, which have already proven the existence of different glioblastoma classes and different prognoses associated with each class. If each class were to be targeted by a tailored molecular therapy, this class would find it most beneficial as a treatment and patients would obtain better results. A Gene Ontology term analysis confirmed that the sets of differentially expressed genes were enriched for genes involved in processes related to brain development and cancer biology. We also observed enrichment of genes encoding regulatory and inflammatory proteins, such as signal transduction components, cytokines, growth factors and DNA binding proteins. In line with these find- ings, affected pathways from the KEGG database included Cytokine-cytokine receptor interaction, Neuroactive ligand-receptor interaction, MAPK signaling and, expectedly, Glioma, a collection of genes involved in glioma formation. GSEA analysis revealed a consistent up-regulation of inflammatory genes in the GNS lines belonging especially to the MHC class II family, suggesting an immune-evasion phenotype that has already been called upon by a small number of early glioma studies [468,484]. The up-regulation in the GNS lines was seen in several MHC class II genes, as well as related genes involved in antigen presentation on MHC class I complexes. Several works have already shown that MHC class I and II molecules are involved in aspects of human cancer pathology such as invasion and migration [327,421,546]. We find an overall 228
9.1. Digital Profiling of GNS Cell Lines Discussion transcriptional up-regulation of MHC class II molecules and a smaller subset of MHC class I molecules, suggestive of the absence of transcriptionally active compensatory mechanisms. Follow-up proteomic studies could be carried out by us to further explore the dynamics of this immunoevasion mechanism in ac- tion. The identification of specific protein expression levels, in fact, would help us understand the level at which this regulation, which is bound to exist given the up-regulation observed especially in MHC class II molecules, takes place. It would help us answer questions such as whether an excess of MHC class II molecules is also present on the surface of GNS cells, and if there are any correlations with the expression signature profiles and the diagnosis associated with them. In fact, it is possible that in the proneural signature would fall those glioblastoma samples that carry higher protein expression levels of MHC class II molecules, which by exposing aberrant extracellular antigens, would be responsible for a more efficient immunological activation against those cells, in line with the kinder prognosis associated with this signature. On the other hand, it is also possible that little or no sample-dependent variation exists in the up-regulation of MHC class molecules and therefore no correlation between prognosis and MHC member protein levels. In the attempt to observe whether non-coding RNA regulation plays an impor- tant role in the levels of gene expression observed for GNS cell lines, differential microRNA-targeted isoform expression and long non-coding RNA expression were evaluated. Assuming that the differential isoform expression is due to microRNA regulation exerted on a specific isoform, after having identified approximately 2,000 isoform candidates for differential expression detected by multiple tag mappings, we verified which predicted microRNA sequences (from five of the leading algorithms) aligned on their 3'UTRs. An interesting candidate for isoform microRNA regulation is gene NTRK2, which encodes a receptor for brain-derived neurotrophic factor, promoting differentiation, prolifera- tion and survival [118]. In several tumour types, NTRK2 expression correlates with poor prognosis and metastasis [118] and the protein has been detected in a subset of cells in astrocytomas [29]. The Tag-seq data demonstrates that GNS lines express a short NTRK2 isoform, potentially the one that lacks the kinase domain (Fig 6.11), which has been implicated in regulation of astrocyte morphology [366]. Other interesting candidate genes for microRNA regulation of isoform expression, are the tumour suppressor gene BRCA1, involved in p53 signaling [370], the genes AKT2 and AKT3, involved in glioblastoma tumour evasion phenotypes [245], BMP7, a member of the TGF-β superfamily of bone 229
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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.1 Pathway Interactions Appendix F
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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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[38] G. Bain, D. Kitchens, M. Yao,
Page 327 and 328:
[78] S. Bustin, V. Benes, J. Garson
Page 329 and 330:
[112] M. Czystowska, J. Han, M. Szc
Page 331 and 332:
[153] T. Fujiwara, M. Bandi, M. Nit
Page 333 and 334:
[192] M. Hernandez, M. Nieto, and M
Page 335 and 336:
[231] T.-M. Kim, W. Huang, R. Park,
Page 337 and 338:
[268] H. Lemjabbar-Alaoui, A. van Z
Page 339 and 340:
[305] K. MAEDA, S. MATSUHASHI, K. T
Page 341 and 342:
[342] H. Moon, M. Ahn, J. Park, K.
Page 343 and 344:
[379] D. Park and J. Rich. Biology
Page 345 and 346:
[417] P. Rakic. Guidance of neurons
Page 347 and 348:
[456] T. Shima, N. Okumura, T. Taka
Page 349 and 350:
[490] P. A. C. t’Hoen, Y. Ariyure
Page 351 and 352:
[523] T. Watanabe, A. Takeda, T. Ts
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