1D,E). We also assessed predictive performance of 3-year OS of three prognosis models by calculating AUCs from ROC analysis. Not surprisingly, the AUC of the 65-gene risk score (0.68; 95% CI, 0.604-0.761) is highly similar to those from original prognosis models (Supporting Fig. 1). This result strongly suggests that the expression patterns of the 65 genes are sufficient to predict the prognosis of HCC patients, although this dataset represents only 5.8% of genes in the NCI proliferation signature and 10.3% of genes in the SNU recurrence signature. To test whether genes not shared by two prognostic signatures have similar discriminatory

power, two additional risk scores were generated from 65 genes that were randomly selected from nonoverlapped gene lists in each prognostic signature and applied to NCI and SNU cohorts. As expected, the NCI proliferation signature risk score showed

www.selleckchem.com/products/lee011.html significant predictive performance on patients in NCI cohorts (Supporting Fig. 2B). However, it failed to show significant predictive performance on patients in SNU cohorts (Supporting Fig. 2C). The SNU recurrence signature risk score also showed opposite predictive performance on patients from two different cohorts (Supporting Fig. 2B,C). However, common gene risk scores showed consistent Neratinib clinical trial predictive performance on patients from both cohorts. These data suggest that genes shared in two independent prognostic signatures might be more robust than those only present in one signature. We next sought to validate the risk score using expression data of the 65 genes from the independent HCC cohort. Gene expression data for 100 tumors from Korean patients with HCC were collected and used as an independent test set. The coefficient and threshold value (8.36) MCE derived from the NCI cohort were directly applied. When patients

in the Korean cohort were stratified according to their risk score, the patient group with a low risk score had a significantly better prognosis (P = 5.6 × 10−5 for OS, log-rank test) (Fig. 2A) than patients with a high risk score. The risk score was further validated in another independent cohort (LCI cohort, P = 5.0 × 10−4 for OS, log-rank test) (Fig. 2B). Taken together, these results demonstrate that it is possible to determine a risk score on the basis of the expression of a small number of genes. We next combined clinical data from two test cohorts and assessed the prognostic association between our newly developed 65-gene risk score and other known clinical risk factors using univariate Cox regression analyses. In addition to the alpha-fetoprotein (AFP) level, tumor size, grade, and vasculature invasion, which are already well-known risk factors, the risk score was a significant indicator for OS (Table 3).