The prognosis of patients with advanced gastric cancer remains unfavorable. Even after curative resection, 40% of patients with advanced gastric cancer die of recurrence. Conventional clinicopathlogic findings are sometimes inadequate for predicting recurrence in individuals. Hence, we tried to construct a new diagnostic system, which predicts recurrence in patients with advanced gastric cancer after curative resection based on molecular analysis. Gastric cancer progression is a function of multiple genetic events that may affect the expression of large number of genes. We performed gene expression profiling with 2,304 genes in 60 advanced gastric cancer patients who underwent curative resection using a PCR array technique, a high-throughput quantitative RT-PCR technique. The diagnostic system, which was constructed from the learning set comprised of 40 patients with the most informative 29 genes, classified each case into a good-signature or poor-signature group. Then, we confirmed the predictive performance in an additional test set comprised of 20 patients, and the prediction accuracy for recurrence was 75%. Kaplan-Meier analysis revealed significant difference between the good-signature and the poor-signature group (p = 0.0125). Especially in patients with smaller tumor (< or = 5 cm), less developed LN metastasis (N(0,1)), or earlier stage (stages I and II), the prediction accuracy was high (88.9%, 84.6%, or 81.8%, respectively). Our diagnostic system based on systematic analysis of gene expression profiling can predict the recurrence at clinically meaningful level. By combining our system with conventional clinicopathologic factors, we can improve the prediction of recurrence in patients with advanced gastric cancer who underwent curative surgery.