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

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.