Combining tissue-derived microRNAs with clinical risk models for prediction of HCC recurrence after liver transplantation: A proof-of-concept study

Abstract

Abstract To evaluate the utility of microRNAs (miRNAs) integrated with current clinical risk models as predictive models for hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT). This retrospective proof-of-concept study included 20 patients with HCC who underwent LT between 2007 and 2021 ( n  = 10 recurrent, n  = 10 5-year recurrence-free). MiRNA profiling was performed on formalin-fixed, paraffin-embedded (FFPE) HCC explant tissue at the time of transplantation and clinical data were collected. The predictive value of miRNA expression for HCC recurrence was evaluated in a hybrid data- and hypothesis-driven approach and combined with clinical risk models (Milan, UCSF, Metroticket 2.0 and AFP). Kaplan-Meier analysis was performed to analyze recurrence-free survival (RFS). We identified a 3-miRNA signature - miR-3692-5p, miR-424, and miR-718 - that revealed discriminatory capacity between recurrence and non-recurrence. Adding this signature to clinical models increased the area under the receiver operating characteristic curve (AUC) for modeling HCC recurrence from 0.5 to 0.7 to 0.94–0.96. The combined models were used to categorize patients as high- or low-risk, with patients in the high-risk group having a shorter estimated median RFS (17.0 months vs. 38.5 months, p  < 0.05). Integrating tissue-derived molecular miRNA signatures with existing clinical risk models may enhance the prediction of HCC recurrence following LT. Incorporating molecular approaches into current protocols could refine post-transplant risk stratification and surveillance guidance.