Genomic alterations in hepatocellular carcinoma patients undergoing liver transplantation predict recurrence and prognosis

Author:

Li Xinqiang1,Wang Chengyu1,Qi Yingxue2,Yu Ting1,Zhang Qin2,Liu Huan1,Luo Ningning2,Cai Hailun3,Chen Jinhui3,Cheng Shuang2,Cai Jinzhen1,Wu Bin4ORCID

Affiliation:

1. The Affiliated Hospital of Qingdao University

2. Jiangsu Simcere Diagnostics Co., Ltd

3. Fujian Medical University Union Hospital

4. Fujian Medical University

Abstract

Abstract Liver transplantation (LT) stands as a pivotal treatment for hepatocellular carcinoma (HCC), outperforming comprehensive treatments in long-term efficacy. However, the 5-year post-LT survival rate hovers between 60% and 70%, largely due to recurrent HCC, spotlighting the critical need for biomarkers that can predict recurrence and prognosis following LT. Our study embarked on this challenge by retrospectively analyzing data from 37 HCC patients who underwent LT from January 2019 to January 2021. Employing whole exome sequencing on tissue and control blood samples, we segregated these patients into recurrence (n = 14) and non-recurrence groups (n = 23), based on a one-year postoperative threshold. Our analysis unveiled a distinctive genomic mutation spectrum in these patients, highlighting five predominantly mutated genes: BCLAF1, MUC4, TP53, FMN2, and TTC7A. Notably, clinical features between the two groups showed no significant divergence. However, the recurrence group demonstrated markedly inferior overall survival (OS) compared to their counterparts (p < 0.0001). Multivariate regression pinpointed 304 genes as independent predictors for recurrence-free survival (RFS) and 482 genes for OS (p < 0.05). Additionally, our research led to the development of a novel 13-gene model, which markedly influences both RFS and OS. Patients classified within the high-risk category of this model experienced significantly poorer outcomes. This study is a trailblazer in linking genomic alterations with the recurrence and survival rates of HCC patients post-LT, introducing a 13-gene model that offers substantial predictive and prognostic utility.

Publisher

Research Square Platform LLC

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