00001 RAC1, TGFβ1, TGFα, VEGFA, ERBB2, STAT3, RAD51 NOTCH signalling 2.40E-6 JAG1, HES1, CTBP1, CTBP2, ADAM10 0.00012 DVL1, HES1, CTBP1, ADAM10 MAPK signalling 0.00015 FGFR2, TGFβ1, MAP2K5, MAP2K2, MAP2K3, MAP2K7, RAC1, DUSP10, DUSP3 Hedgehog signalling 0.00836 CSNK1E, click here BMP2, GSK3B, CSNK1A1 aIPA was performed on respectively 2.806 (good) and 1.692 (bad) differentially expressed probe sets (with entry in the Ingenuity Knowledge Base; http://www.ingenuity.com). The most significant networks, functions and canonical pathways are listed. b KEGG analysis was performed on respectively 2.033 and 1.285 Dibutyryl-cAMP cost probesets upregulated in
the good and bad PDAC samples using GENECODIS. c A selection of upregulated genes contributing to the pathways, is given. Gene expression profiling of ‘Bad’ PDAC versus control Microarray analysis comparing ‘Bad’ versus control samples defined 1905 differentially expressed genes. IPA analysis on 1692 mapped genes generated networks, such as the network related to ‘Drug metabolism’, including TGFβ1 (fold 2.4) and LOXL2 (fold mTOR inhibitor 3.9), (p < 0.001). Similar to the ‘Good’ versus control comparison, the functions ‘Cancer’, ‘Cellular growth and proliferation’ and ‘Cellular movement’
were differentially expressed, but with even higher fold changes. Analysis of canonical pathways also revealed the Integrin pathway as most significant (including ITGA2: fold 5.0, ITGA3: fold 3.1, ITGA6: fold 5.3, ITGB1: fold 2.0, ITGB4: fold 5.8, ITGB5: fold 5.0 and ITGB6: fold 5.4; all p < 0.001), on top of the Ephrin receptor signalling (including EPHA2: fold 7.3, xEPHB4: fold 2.0, EFNA5: fold 3.9 and EFNB2: fold 3.0; all p < 0.001), the Wnt/β-catenin pathway and pancreatic adenocarcinoma signalling (Table 2).
Genes involved in the p53 signalling pathway, the Wnt/β-catenin and the Notch signalling were highly upregulated (Table 2) in ‘Bad’ PDAC samples (KEGG analysis, GENECODIS). Alanine-glyoxylate transaminase Molecular characteristics of ‘Bad’ versus ‘Good’ PDAC To study gene expression profiling related to poor outcome, we first studied differentially expressed genes between ‘Bad’ and ‘Good’ PDAC samples (Figure 3A). A total of 131 genes were differentially expressed, i.e. 69 upregulated and 62 downregulated genes in ‘Bad’ PDAC (Table 3). The networks ‘Cell morphology’ (including SNAI2 (fold 2.9) and TGFβR1 (fold 3.3); p < 0.001), ‘Cell signalling’ and ‘Cellular movement’ were generated from differentially expressed genes (IPA). No cancer-related canonical pathways or KEGG pathways were differentially expressed between both PDAC groups. Figure 3 Molecular characteristics of ‘Bad’ vs. ‘Good’ PDAC. (A) First, genes differentially expressed between the ‘Good’ and the ‘Bad’ PDAC samples were used for IPA analysis. (B) Secondly, we compared genes differentially expressed between the ‘Good’ versus control and the ‘Bad’ versus control analysis to exclude pancreas-related genes. The control samples in both experiments were the same.