Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

7.1 Enrichment Analysis Results
specific pathway distribute across the ranked list, whether they are randomly
distributed throughout the ranked list or primarily found at the top or bottom.
Three key elements define the GSEA method [474]:
1. Calculating an enrichment score that measures the degree to which a set
of genes belonging to a pathway is overrepresented at the top or bottom
of the entire ranked list of differentially expressed genes, for example
(and corresponds to a weighted Kolmogorov-Smirnov like statistic).
2. Estimating the significance of the enrichment score by permuting the
phenotype labels and recomputing the enrichment score of the genes in
the pathway each time. This generates a null distribution that the p-
value of the observed original enrichment score is calculated against.
3. Adjusting the enrichment score to account for multiple hypothesis testing
when entire databases of pathways are evaluated at once, like in our case
with the KEGG and Gene Ontology (GO) databases. In this case the
enrichment score is first normalised to account for the size of the path-
way, and then the proportion of false positives, or FDR, is calculated
for each normalised enrichment score. The FDR associated to each nor-
malised enrichment score corresponds to the estimated probability that
a pathway with a given normalised enrichment score represents a false
positive finding.
The enrichment analysis using GO [106] and the KEGG pathway database [2]
confirmed the set of 739 differentially expressed genes to be enriched for path-
ways related to brain development, glioma and cancer (Table 7.2 and 7.1). We
also observed enrichment of regulatory and inflammatory genes, such as signal
transduction components, cytokines, growth factors and DNA-binding factors.
Several genes related to antigen presentation on MHC class I and II molecules
were up-regulated in GNS cells, consistent with the documented expression of
their corresponding proteins in glioma tumours and cell lines [120,174]. In line
with these findings, affected pathways from the KEGG database included Anti-
gen Processing and Presentation, Diabetes Mellitus Type I, Cytokine-cytokine
receptor interaction, Neuroactive ligand-receptor interaction, MAPK signaling
and, expectedly, Glioma, a collection of genes involved in glioma formation
(Table 7.2). The first two plots from the GSEA run that identified these path-
ways as being significantly altered in our dataset, are shown in figure 7.1. In
the top panels of the figure the green distribution represents the trend of the
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