Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

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7.4 Survival Analysis Results (Fig 7.4). Overall, we can confirm that the set of core differentially expressed genes identified by Tag-seq defines an expression signature characteristic of glioblastoma and related to glioma histological grade. 7.4 Survival Analysis To explore the relevance in glioma of the patterns observed for the GNS vs. NS cell line comparison, we decided to integrate clinical information with tumour expression data. Although this analysis was not performed by the candidate it is included in this thesis because it is very relevant to the rest of the material and analyses performed by the candidate and will therefore help paint for the reader a more complete picture of the relevance of GNS cell lines in glioblastoma cancer research. We first tested for associations between gene expression and survival time using the TCGA dataset, consisting of 397 glioblastoma cases (see Methods section 5.9 for table 5.4). For each gene, we fitted a Cox proportional hazards model with gene expression as a continuous explanatory variable and computed a p-value by the score test (Table 7.5). The set of 29 genes found to distinguish GNS cells from NS cells across the 22 cell lines assayed by qRT-PCR was enriched for low p-values compared to the complete set of 18,632 genes quantified in the TCGA dataset (p = 0.02, one-sided Kolmogorov-Smirnov test), demon- strating that expression analysis of GNS and NS lines had enriched for genes associated with patient survival. Seven of the 29 genes had a p-value
7.4 Survival Analysis Results Table 7.5: Survival tests for the 29 genes found via qRT-PCR to distinguish GNS cell lines from NS cell lines. Gene Category TCGA dataset Gravendeel dataset (GBM cases) Coefficient* p-value Probeset** Coefficient* p-value ADD2 Up -0.13 0.2858 237336_at -0.17 0.1420 CD9 Up 0.18 0.0731 201005_at 0.17 0.0689 CEBPB Up 0.19 0.1028 212501_at 0.17 0.0651 DDIT3 Up 0.17 0.0128 209383_at 0.09 0.2777 FOXG1 Up 0.13 0.0861 206018_at 0.11 0.0380 HMGA2 Down 0.13 0.1456 1561633_at -0.84 0.2459 HOXD10 Up 0.12 0.0108 229400_at 0.15 0.0021 IRX2 Down -0.19 0.2346 228462_at -0.20 4.4x10 -4 LMO4 Up 0.24 0.1046 209205_s_at 0.20 0.1435 LYST Up 0.05 0.5590 203518_at 0.10 0.4151 MAF Down 0.10 0.5873 209348_s_at 0.38 0.0074 MAP6 Down 0.16 0.3063 235672_at -0.30 0.0087 MT2A Up 0.16 0.1554 212185_x_at 0.27 0.0127 MYL9 Down 0.08 0.3764 201058_s_at 0.15 0.0252 NDN Down -0.04 0.4874 209550_at -0.22 6.0x10 -5 NELL2 Down 0.08 0.1021 203413_at 0.14 0.0215 PDE1C Up 0.20 0.0105 236344_at 0.21 0.0134 PLA2G4A Up -0.06 0.3198 210145_at 0.30 2.9x10 -4 PLCH1 Down 0.10 0.3165 214745_at 0.45 0.0094 PLS3 Up 0.13 0.0381 201215_at 0.30 0.0069 PRSS12 -0.11 0.1865 213802_at 0.20 0.0296 PTEN Down -0.53 0.0047 228006_at -0.40 0.0062 SDC2 Down 0.22 0.0044 212158_at 0.28 5.8x10 -4 ST6GALNAC5 Down 0.01 0.9116 220979_s_at 0.08 0.2416 SULF2 -0.11 0.1525 233555_s_at -l0.15 0.0930 SYNM Down -0.06 0.5620 212730_at 0.08 0.2613 TAGLN Down 0.03 0.5947 205547_s_at 0.17 0.0030 TES Down -0.05 0.5759 202720_at 0.07 0.5499 TUSC3 Down -0.14 0.0079 209227_at -0.18 0.0060 *Fitted coefficient from Cox model; a positive coefficient indicates that higher expression is associated with poor survival and a negative coefficient indicates the opposite. ** For the Gravendeel dataset, the result for the most significant probeset interrogating the gene is shown; Up=Up-regulated; Down=Down-regulated. To test whether these findings generalise to independent clinical sample groups, we examined the glioblastoma datasets described by Gravendeel et al [176] and Murat et al [353], consisting of 141 and 70 cases, respectively (see Methods section 5.9 for table 5.4). The GNS signature score was correlated with patient survival in both of these datasets (p = 3x10 5 and p = 0.006, respectively; Fig 7.5a). At the level of individual GNS signature genes, five genes were significantly associated with survival (p < 0.05) in both of the two largest 183
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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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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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