A Simple Nomogram Developed for Predicting HCC Metastasis Based on Micrornas 

Abstract BackgroundOwing to lack of predictive models for HCC metastasis based on the expression of miRNAs, we aimed to develop a simple model for identification of HCC patients at high risk of metastasis. MethodsHCC datasets with metastasis information were acquired from the Gene Expression Omnibus (GEO), and samples were randomly divided into training group (n=169) and testing group (n=72). Based on expression of miRNAs in the training group, we developed a predictive nomogram for HCC metastasis and evaluated its performance using area under the receiver operating characteristic curve (AUC), calibration curve, decision curve and clinical impact curve analysis.ResultsWe found that the expression of miR-30c, miR-185 and miR-323 in HCC correlated with metastasis by the least absolute shrinkage and selection operator regression (LASSO) method and multivariate logistic regression. Based on these three miRNAs, we generated the nomogram for predicting metastasis in the training group (AUC 0.869 [95% CI .813-0.925], sensitivity 80.5%, specificity 78.9%); in testing group (0.821 [0.770-0.872], 48.5%, 92.3%). The calibration curve showed a good agreement between actual observation and prediction by nomogram. The nomogram represented high clinical net benefits using decision curve and clinical impact curve analysis. Moreover, total scores calculated by nomogram were higher in dead patients than that in alive patients. In addition, the predicted target genes of these 3 miRNAs correlated with tumor metastasis by functional enrichment analysis, such as filopodium. Our easy-to-use nomogram could assist in identifying HCC patients at high risk of metastasis, which offer valuable information for clinical treatment.