Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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6.6 Literature Mining for Differentially Expressed Genes Results Figure 6.14: Expression levels of the 29 genes distinguishing GNS from NS lines, measured by qRT-PCR and presented as percent of NS geometric mean. Two histograms reveal the data for the two probes used to measure the expression levels of MYL9. 6.6 Literature Mining for Differentially Expressed Genes An important assessment that adds value to our pathway analysis, is the classification of every differentially expressed gene (FDR< 10%) as a gene known 142
6.6 Literature Mining for Differentially Expressed Genes Results to be associated with glioblastoma, cancer or yet unlinked to any of these. I extracted the information held in several literature navigating resources, such as BioGraph [280], GBMbase [162] and iHOP [197] using a script described in detail in the Methods section and reported in Appendix B, and classified each gene in one of the following categories: · Extensive amount of literature implicates the gene in glioblastoma; · Limited amount of literature implicates the gene in glioblastoma; · Unknown to be implicated in glioblastoma, but known in other cancers; · Unknown to be implicated in any type of cancer. The information retrieved by the script for each of the differentially expressed genes was assigned to one of the four categories described above (Appendix A.2). The data outputted from this query is summarised in table 6.5. Table 6.5: Differentially expressed genes (F DR < 10%) assigned to a four-tier classification system. The unique total refers to the unique list of all the genes in each category. Category Number Extensive amount of literature implicating the gene in GBM 238 Limited amount of literature implicating the gene in GBM 94 Unknown to be implicated in GBM, but known in other cancers 199 Unknown to be implicated in any type of cancer 232 Unique Total 748 of genes A stripped down version of the complete table available in Appendix A.2, highlights only the genes with limited evidence or no evidence of implication in glioblastoma that appear in our integrated glioblastoma pathway (Table 6.6). Many of these genes had not been directly implicated in glioma, but they participate in glioma-related pathways and they differ in expression between GNS and NS lines. Thus, by considering expression changes in a pathway context, we identified additional candidate glioblastoma genes, such as the putative cell adhesion gene ITGBL1 [50], the orphan nuclear receptor NR0B1, which is strongly up-regulated in G179 and is known to be up-regulated and mediate tumour growth in Ewing’s sarcoma [158], and the genes PARP3 and PARP12 which belong to the PARP family of ADP-ribosyl transferase genes involved in DNA repair. The up-regulation of these PARP genes in GNS cells 143
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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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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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Bibliography [1] Cell signaling tec
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[38] G. Bain, D. Kitchens, M. Yao,
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[78] S. Bustin, V. Benes, J. Garson
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[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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