Abstract
BACKGROUND: A high recurrence rate has always been a serious problem for treatment of hepatocellular carcinoma (HCC). Exploring predictors of postoperative and posttransplantation recurrence in patients with HCC can guide treatment strategies for clinicians. RESULTS: In this study, logistic regression and multivariate Cox regression models were constructed with microRNA expression profile data from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO). The accuracy of predictions was assessed using receiver operating characteristic curve (ROC) and Kaplan‒Meier survival curve analyses. The results showed that the combination of 10 miRNAs (including hsa-miR-509-3p, hsa-miR-769-3p, hsa-miR-671-3p, hsa-miR-296-5p, hsa-miR-767-5p, hsa-miR-421, hsa-miR-193a-3p, hsa-miR-139-3p, hsa-miR-342-3p, and hsa-miR-193a-5p) accurately predicted postoperative and posttransplantation malignancy recurrence in HCC patients and was also valuable for prognostic evaluation of HCC patients. The 10-miRNA prediction model might assist doctors in making prognoses for HCC patients who have a high probability of relapse following surgery and in offering additional, individualized treatment to lessen that risk.