We previously developed a DNA microarray-based system that out-performs traditionally used clinical parameters for prediction of early intrahepatic recurrence (IHR) of hepatocellular carcinoma (HCC). Because DNA microarray is too expensive for daily clinical use, we used a quantitative real-time reverse transcription-polymerase chain reaction (QRT-PCR) to develop a lower-cost predictor for early IHR. From the 12 early IHR-related genes integrated in the previous predictor, we selected 6 genes whose levels showed the strongest association between data from the 2 distinct DNA microarray platforms with the same sample set. Expression of these 6 genes relative to that of GAPDH was measured by QRT-PCR in 82 HCCs. Of the 82 HCCs, 39 and 43 were assigned to training and independent test sets, respectively. By searching all combinations (n=2-6) of the 6 genes, we found an optimal combination of 3 genes (HLADRA, DDX17 and LAPTM5) that minimized the leave-one-out error for prediction of early IHR in the training set. The 3-gene predictor constructed with the Fisher linear classifier correctly predicted early IHR or non-recurrence in 35 (81.4%) of 43 HCCs in the independent test set and had a high positive predictive value of 72.7% and a high negative predictive value of 84.4%. Multivariate analysis with the stepwise logistic regression showed that the 3-gene predictor [F(x)<0] was an independent risk factor for early IHR (risk ratio, 13.6; p=0.006), indicating its potential as an easy-to-use predictor for accurate prediction of early IHR of HCC.