RESUMO
BACKGROUND: Despite a radical operation, about half of gastric cancer (GC) patients with advanced GC experience peritoneal metastasis (PM), and the patients with PM have a poor prognosis. However, because staging laparoscopy was a highly invasive procedure for patients, identification of PM using a liquid biopsy can be useful for patients with GC. METHODS: This study analyzed two genome-wide miRNA expression profiling datasets (GSE164174 and TCGA). The study prioritized biomarkers in pretreatment plasma specimens from clinical training and validation cohorts of patients with GC. The authors developed an integrated exosomal miRNA panel and established a risk-stratification model, which was combined with the miRNA panel and currently used tumor markers (CEA, CA19-9, CA125, and CA72-4 levels). RESULTS: The comprehensive discovery effort identified a four-miRNA panel that robustly predicted the metastasis with excellent accuracy in the TCGA dataset (area under the curve [AUC] 0.86). A circulating exosomal miRNA panel was established successfully with remarkable diagnostic accuracy in the clinical training (AUC 0.85) and validation (AUC 0.86) cohorts. Moreover, the predictive accuracy of the panel was significantly superior to that of conventional clinical factors (P < 0.01), and the risk-stratification model was dramatically superior to the panel and currently used clinical factors for predicting PM (AUC 0.94; univariate: odds ratio [OR] 77.00 [P < 0.01]; multivariate OR 57.71 [P = 0.01]). CONCLUSIONS: The novel risk-stratification model for predicting PM has potential for clinical translation as a liquid biopsy assay for patients with GC. The study findings highlight the potential clinical impact of the model for improved selection and management of patients with GC.