Genome-wide identification of a novel miRNA-based signature to predict recurrence in patients with gastric cancer.

The current Tumor Node Metastasis (TNM) staging system is inadequate at identifying high-risk gastric cancer (GC) patients. Using a systematic and comprehensive-biomarker discovery and validation approach, we attempted to build a miRNA-recurrence classifier (MRC) to improve prognostic prediction of GC. We identified 312 differentially expressed miRNAs in 446 GC tissues compared to 45 normal controls by analyzing high-throughput data from The Cancer Genome Atlas (TCGA). Using Cox regression model, we developed an 11-miRNA signature that could successfully discriminate the high-risk patients in the training set (n=372; P<0.0001). The RT-qPCR based validation in an independent clinical cohort (n=88) of formalin fixed paraffin-embedded (FFPE) clinical GC samples showed that MRC-derived high-risk patients succumb to significantly poor recurrence-free survival (RFS) in GC patients (P<0.0001). Cox and stratification analysis indicated that the prognostic value of this signature was independent of clinicopathological risk factors. Time-dependent receiver operating characteristic (ROC) analysis revealed that the area under ROC curve (AUC) of this signature was significantly larger than that of TNM stage in the TCGA (0.733 versus 0.589 at 3 years, P=0.004; 0.802 versus 0.635 at 5 years, P = 0.005) and validation cohort (0.835 versus 0.689 at 3 years, P=0.003). A nomogram was constructed for clinical use, which integrated both MRC and clinical-related variables (depth of invasion, lymph node status and distance metastasis), and did well in the calibration plots. In conclusion, this novel miRNA-based signature is superior to currently used clinicopathological features in identifying high-risk GC patients. It can be readily translated into clinical practice with FFPE specimens for specific decision-making applications.