Through PANTHER ontology analysis, we found 12 significant pathways for hypermethylated and 11 pathways for hypomethylated genes http://www.selleckchem.com/products/Temsirolimus.html (Supporting Table 6). A number of potentially important cellular pathways involved in tumorigenesis were observed, such as the pathways of heterotrimeric G-protein signaling, endothelin signaling, phosphoinositide-3 kinase, interleukin

signaling, and inflammation mediated by chemokine/cytokine signaling and insulin/insulin growth factor, and so on. For the first time, Wnt and 5-hydroxytryptamine (5-HT)4-type receptor-mediated signaling pathways were identified. A two-sample t test was used to compare methylation levels among tumor and adjacent tissues separately for several HCC risk factors. No site was identified that was significantly differentially methylated by gender, HBV status, HCV status, or AFB1-DNA

adduct levels (i.e., high/medium versus low) (data not shown). However, the results may be partially caused by small numbers of females, viral status, and missing adduct data in some adjacent tissues. For alcohol consumption status, within adjacent tissues, methylation level at one CpG site in VPREB1 significantly differed between drinkers and nondrinkers, whereas within tumor tissues, seven CpG sites in CRISPLD1, PCDHB2, PCSK1, LXH1, KCTD8, TSHD3, and CXCL12 were identified after Bonferroni’s adjustment. Further unsupervised click here hierarchic cluster analysis clearly suggested an even better separation of drinkers from nondrinkers using the top differentially methylated sites among tumor tissues (Supporting Fig. 5A), compared to nontumor tissues (Supporting Fig. 5B). To select the list of candidate CpG sites for confirmatory analysis, method A with the complete data set of 62 pairs resulted in a list of 24 sites in 18 genes (Supporting Table 7). The heatmap of the selected 24 CpG sites shows good separation of tumor and adjacent tissues in general (Supporting

Fig. 6). Method B, based on 1,000 three-fold cross-validations of training sets with 40 pairs, resulted in a list of 24 top CpG sites that were most frequently selected (all ≥98% of times of 1,000 three-fold cross-validations) (Table 3). MCE The two panels of 24 CpG sites had 20 overlapping sites (Table 3; Supporting Table 7). Figure 3 shows the heatmap of the selected 24 CpG sites using method B. The two heatmaps show similar separations. Using the testing set, the selected panel of 24 CpG sites (method B) had high prediction accuracy in the testing set: 0.886 (SD = 0.044) based on diagonal linear discriminant analysis, 0.918 (SD = 0.044) based on support vector machines, and 0.877 (SD = 0.038) based on k-nearest neighbor. This suggests that the selected list of 24 CpG sites using the 3-fold cross-validation for second-stage confirmatory analysis is robust. Furthermore, compared to Fig.