Discovery and validation of an expression signature for recurrence prediction in high-risk diffuse-type gastric cancer

Background Diffuse type gastric cancer (DGC), represented by low sensitivity to chemotherapy and poor prognosis, is a heterogenous malignancy in which patient subsets exhibit diverse oncological risk-profiles. This study aimed to develop molecular biomarkers for robust prognostic risk-stratification and improve survival outcomes in patients with diffuse type gastric cancer (DGC). Methods We undertook a systematic and comprehensive discovery and validation effort to identify recurrence prediction biomarkers by analyzing genome-wide transcriptomic profiling data from 157 patients with DGC, followed by their validation in 254 patients from 2 clinical cohorts. Results Genome-wide transcriptomic profiling identified a 7-gene panel for robust prediction of recurrence in DGC patients (AUC = 0.91), which was successfully validated in an independent dataset (AUC = 0.86). Examination of 180 specimens from a training cohort allowed us to establish a gene-based risk prediction model (AUC = 0.78; 95% CI 0.71-0.84), which was subsequently validated in an independent cohort of 74 GC patients (AUC = 0.83; 95% CI 0.72-0.90). The Kaplan-Meier analyses exhibited a consistently superior performance of our risk-prediction model in the identification of high- and low-risk patient subgroups, which was significantly improved when we combined our gene signature with the tumor stage in both clinical cohorts (AUC of 0.83 in the training cohort and 0.89 in the validation cohort). Finally, for an easier clinical translation, we established a nomogram that robustly predicted prognosis in patients with DGC. Conclusions Our novel transcriptomic signature for risk-stratification and identification of high-risk patients with recurrence could serve as an important clinical decision-making tool in patients with DGC.