A simple and practical microRNA-based nomogram to predict metastatic HCC

Background: Notwithstanding that unprecedented scientific progress has been achieved over the years, there is no established microRNA-based model for predicting hepatocellular carcinoma (HCC) metastasis. To this end, we aimed to develop a simple model based on the expression of miRNAs to identify patients at high risk of metastatic HCC. Methods: HCC datasets with metastasis data were acquired from the Gene Expression Omnibus (GEO) database, and samples were randomly divided into training (n=169) and validation (n=72) groups. Based on the expression of miRNAs in the training group, we developed a predictive nomogram for metastatic HCC. We evaluated its performance using the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve and clinical impact curve analysis. Results: By applying the absolute shrinkage and selection operator regression (LASSO) and multivariate logistic regression, we found that the expression of miR-30c, miR-185, and miR-323 were independent predictors of metastatic HCC. These miRNAs were used to construct a nomogram that yielded good performance in predicting metastasis in training (AUC 0.869 [95% CI 813-0.925], sensitivity 92.7%, specificity 57.8%) and validation groups (0.821 [CI 0.720-0.923], sensitivity 94.7%, specificity 60%). The calibration curve showed a good agreement between actual and predicted outcomes. Decision curve analysis showed a high clinical net benefit of nomogram predictions for our patients. Moreover, higher total scores of our nomogram were associated with dead patients. In addition, functional enrichment analysis showed that the predicted target genes of these 3 miRNAs correlated with tumor metastasis-associated terms, such as filopodium, and identified their related hub genes. Conclusions: Our easy-to-use nomogram could assist in identifying HCC patients at high risk of metastasis, which offers valuable information for clinical treatment.