Gene expression profiling predicts clinical outcome of prostate cancer
J. Clin. Invest. Gennadi V. Glinsky, et al. 113:913 doi:10.1172/JCI20032 [Go to this article.]

Figure 2
Kaplan-Meier analysis of the probability that patients would remain disease-free among 21 prostate cancer patients constituting a signature discovery group according to whether they had good-prognosis or poor-prognosis signatures defined by the recurrence predictor signature 1 (A), recurrence predictor signature 2 (B), recurrence predictor signature 3 (C), and the recurrence predictor algorithm, which takes into account calls from all three signatures (D). The cut-off values for each marker were identified through the detailed analysis of behavior of log-rank test P values across the range of the measurements for each marker. We selected the prognosis discrimination cut-off value for each signature based on highest level of statistical significance in patients’ stratification into poor- and good-prognosis groups as determined by the log-rank test (lowest P value and highest hazard ratio; see Supplemental Table 6S). CI, confidence interval.