Value of a microRNA risk score model in predicting the prognosis of hepatocellular carcinoma / 临床肝胆病杂志

ABSTRACT

ObjectiveTo screen out the microRNAs (miRNAs) associated with the prognosis of hepatocellular carcinoma (HCC) through data mining of miRNA transcriptome data of HCC downloaded from The Cancer Genome Atlas (TCGA) database, to establish a miRNA risk score model, and to investigate its value in predicting the prognosis of HCC. MethodsThe miRNA expression data and clinical data of HCC samples were downloaded from TCGA database and R language was used to screen out differentially expressed miRNAs between HCC tissue and adjacent tissue, which were randomly divided into training set and testing set after being integrated into clinical data. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed for the training set to screen out the miRNAs associated with the prognosis of HCC, and then a miRNA risk score model was established. The Kaplan-Meier method was used to evaluate the robustness of the model and whether it could predict the prognosis of patients in the same clinical stage. Finally, the receiver operating characteristic (ROC) curve was plotted and the area under the ROC curve (AUC) was calculated to compare the predictive accuracy of the model versus TNM staging in the training set, the testing set, and the entire set. ResultsA total of 300 differentially expressed miRNAs were screened out and the LASSO Cox regression analysis revealed that hsa-miR-139-5p, hsa-miR-1180-3p, hsa-miR-1269b, hsa-miR-3680-3p, hsa-miR-509-3-5p, and hsa-miR-31-5p were associated with the prognosis of HCC. The risk score was calculated for each sample according to the established miRNA risk score model, and the samples were divided into high-risk group and low-risk group according to the median risk score. The Kaplan-Meier curve showed that in both training and testing sets, the high-risk group had a significantly lower survival rate than the low-risk group (P＜0.05). The ROC curve was used to evaluate the prediction efficiency of this model, and the results showed that in the training set, the testing set, and the entire set, the miRNA model had an AUC of 0.817, 0.808, and 0.814, respectively, while TNM staging had an AUC of 0.667, 0.665, and 0.663, respectively. The results of independent prognostic analysis also showed that this miRNA score model could be used as an independent prognostic factor for HCC (P＜0.05). ConclusionHsa-miR-139-5p, hsa-miR-1180-3p, hsa-miR-1269b, hsa-miR-3680-3p, hsa-miR-509-3-5p, and hsa-miR-31-5p are associated with the prognosis of HCC, and the miRNA risk score model has a better prediction accuracy than TNM staging in the training set, the testing set, and the entire set. The stratified analysis also shows that the model can predict the prognosis of patients within the same TNM stage, and therefore, it has a certain reference value in clinical practice and can be used as an independent model for predicting the prognosis of HCC patients.