in immunosurveillance against cancer cells, in multiple phenotypic effects on somatic cells, and in cancer cell escape. Thus, in the Discussion, we describe other roles of IFN-c, especially in terms of the way cancer cells or potentially atypical cells in CRC patients could adjust the local immune system via immunosuppression in order to escape from immunosurveillance. Association among focal adhesion, NK cell-mediated cytotoxicity, and the early-onset CRC predictor gene set As mentioned in the text above, Hong et al. reported that early-onset susceptibility was attributed to the upregulated gene set called the ��predictor gene set��in CRC patients that consists of CYR61, EGR1, FOSB, FOS, VIP, UCHL1, and KRT24. We inspected the associations among the genes listed in Comparison of our method with Gene Set Enrichment Analysis of the Hong Tideglusib 22189214″ title=View Abstract(s)”>PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/22189214 et al. dataset NK cell-mediated cytotoxicity pathway Our statistical analysis indicated significant agreement between the gene expression of the CRC patients and part of the immune Molecular Mechanism of a Cancer Predictor Gene Set 6 Molecular Mechanism of a Cancer Predictor Gene Set defined subpathways. The same p-value of 0.05 was used for the GSEA method. Our method reported 1,966 significant welldefined subpathways that corresponded to 78 KEGG pathways. The GSEA program reported 2 broad types of significant Focal adhesion AKT1 BIRC3 CAV1 CCND3 1.297 2.201 3.937 1.559 NK cell cytotoxicity ARAF CSF2 FAS GRB2 HLA-B HLA-C HLA-G HRAS IFNG IFNGR1 KIR2DL3 KIR3DL2 LAT LCP2 MAP2K1 MAPK1 PTPN11 SOS1 TNF FASLG 4.631 1.879 3.374 1.613 0.795 0.655 0.693 1.027 1.322 2.086 0.632 0.721 1.781 2.682 1.162 2.425 0.417 1.624 1.009 2.096 Pathways in cancer ARAF BCR CCND1 CDK4 4.631 1.241 1.180 1.315 CTNNB1 2.562 ELK1 FYN GRB2 GSK3B HRAS IGF1 ILK ITGB5 JUN MAP2K1 MAPK1 MAPK8 PAK3 PDGFRB PIK3CG PRKCA PTEN PTK2 RAC2 RAF1 SHC3 SOS1 VAV1 CYR61 2.593 4.286 1.613 0.735 1.027 2.529 1.467 1.431 4.179 1.162 2.425 2.355 2.780 2.851 3.224 3.061 0.599 2.151 2.502 1.813 1.838 1.624 1.945 80.630 CTNNB1 2.562 DAPK1 DVL3 ETS1 FGF13 FGFR1 FIGF FLT3 FLT3LG FOS FZD10 GRB2 GSK3B HRAS IGF1 IGF1R IL8 JUN KIT MAP2K1 MAPK1 MAPK8 MMP2 MYC NTRK1 PDGFB PDGFRB RALGDS RET RHOA SOS1 TCF7L1 WNT3 0.438 1.608 1.805 5.486 2.138 3.458 1.262 3.022 36.201 6.256 1.613 0.735 1.027 2.529 2.299 4.276 4.179 1.430 1.162 2.425 2.355 3.031 3.052 1.225 5.234 2.851 1.478 2.212 4.286 1.624 2.735 3.147 pathway lists: 10 activated pathways and 30 repressed pathways in the CRC patients. The number of overlapping pathways between the 2 methods was 6, which is not surprising when considering the differences between 2 methods. Nevertheless, it is interesting that the 2 methods identified 6 common cancerassociated pathways. To compare the 78 pathways identified by our method with the 40 pathways identified by GSEA, we used the cancer-related pathways reported by 
Vogelstein et al. as a gold standard. That is, we inspected which method provided more pathways consistent with the cancer-related pathways identified by Vogelstein et al. The cancer-related pathways from the Vogelstein et al. study were manually mapped to their corresponding KEGG pathways because KEGG pathway identifiers corresponding to the cancer-related pathways were not mentioned explicitly in the study. We then inspected the overlapping pathways between the Vogelstein cancer-related KEGG pathways and those identified by the 2 methods. As shown in Comparison between the pathway substructure of the Hong et al. datas
