A microRNA-based prediction model for lymph node metastasis in hepatocellular carcinoma

Abstract

We developed an efficient microRNA (miRNA) model that could predict the risk of lymph node metastasis (LNM) in hepatocellular carcinoma (HCC). We first evaluated a training cohort of 192 HCC patients after hepatectomy and found five LNM associated predictive factors: vascular invasion, Barcelona Clinic Liver Cancer stage, miR-145, miR-31, and miR-92a. The five statistically independent factors were used to develop a predictive model. The predictive value of the miRNA-based model was confirmed in a validation cohort of 209 consecutive HCC patients. The prediction model was scored for LNM risk from 0 to 8. The cutoff value 4 was used to distinguish high-risk and low-risk groups. The model sensitivity and specificity was 69.6 and 80.2 %, respectively, during 5 years in the validation cohort. And the area under the curve (AUC) for the miRNA-based prognostic model was 0.860. The 5-year positive and negative predictive values of the model in the validation cohort were 30.3 and 95.5 %, respectively. Cox regression analysis revealed that the LNM hazard ratio of the high-risk versus low-risk groups was 11.751 (95 % CI, 5.110–27.021; P < 0.001) in the validation cohort. In conclusion, the miRNA-based model is reliable and accurate for the early prediction of LNM in patients with HCC.