biostudies-literatureUnknown5(9)Yang MTumor recurrence is a life-threatening complication after liver transplantation (LT) for hepatocellular carcinoma (HCC). Precise recurrence risk stratification before transplantation is essential for the management of recipients. Here, we aimed to establish an inflammation-related prediction model for posttransplant HCC recurrence based on pretransplant peripheral cytokine profiling. Two hundred and ninety-three patients who underwent LT in two independent medical centers were enrolled, and their pretransplant plasma samples were sent for cytokine profiling. We identified four independent risk factors, including alpha-fetoprotein, systemic immune-inflammation index, interleukin 6, and osteocalcin in the training cohort (<i>n</i> = 190) by COX regression analysis. A prediction model named iMedComme678https://www.ebi.ac.uk/biostudies/studies/S-EPMC11345533biostudies-literatureAn inflammatory liquid fingerprint predicting tumor recurrence after liver transplantation for hepatocellular carcinoma.PMC11345533Li HHe CHu ZChen JYang MZheng SLu DWei XLin ZChen HXie QDong SYang XYang ZSu RWang RZhuo JZhuang LPan LXu XShen WYin JfalseAn inflammatory liquid fingerprint predicting tumor recurrence after liver transplantation for hepatocellular carcinoma.Tumor recurrence is a life-threatening complication after liver transplantation (LT) for hepatocellular carcinoma (HCC). Precise recurrence risk stratification before transplantation is essential for the management of recipients. Here, we aimed to establish an inflammation-related prediction model for posttransplant HCC recurrence based on pretransplant peripheral cytokine profiling. Two hundred and ninety-three patients who underwent LT in two independent medical centers were enrolled, and their pretransplant plasma samples were sent for cytokine profiling. We identified four independent risk factors, including alpha-fetoprotein, systemic immune-inflammation index, interleukin 6, and osteocalcin in the training cohort (<i>n</i> = 190) by COX regression analysis. A prediction model named i2024-01-01T00:00:00Z2024 Sep2025-04-18T16:53:42.909Z2025-04-07T04:20:35.895ZS-EPMC113455333918893710.1002/mco2.678