Background: The development of liver graft disease is partially determined by individual genetic background. Interleukin 28B (IL28B) is strongly suspected to be involved in susceptibility for hepatitis C virus (HCV) infection, inflammation, and antiviral treatment response before and after liver transplantation (LT). Currently, the role of IL28B polymorphism (rs12979860) in the development of hepatocellular carcinoma (HCC) is unclear, and only limited data are available on the course of HCV recurrence.
Methods: One hundred sixty-seven HCV-positive patients after LT were genotyped for IL28B (C→T; rs12979860). Sixty-one patients with histologically confirmed HCC in the explanted liver were compared with 106 patients without HCC regarding IL28B genotypes. Among patients with HCC, IL28B genotypes were correlated with tumor histology and pretransplant α-fetoprotein (AFP) levels. Furthermore, the role of IL28B polymorphism was evaluated regarding interferon-based treatment success and fibrosis progression after LT.
Results: The prevalence of HCC in explanted livers was significantly higher among patients with TT genotype, suggesting a protective role of the C allele in HCC development (P=0.041). Median AFP level was closely to significance higher in the presence of T allele (P=0.052). Significant differences in IL28B genotype distribution were detected between AFP-negative and AFP-positive HCCs (<15 μg/L vs. >15 μg/L; P=0.008). Although no impact could be observed regarding acute cellular rejection (P=0.940), T allele was significantly associated with antiviral therapy failure (P=0.028) and faster development of advanced fibrosis (P=0.017) after LT.
Conclusion: IL28B polymorphism seems to be involved in the development of HCV-induced HCC and in the course of HCV recurrence after LT. T allele may be regarded as a genetic risk factor for HCV-related carcinogenesis, posttransplant fibrosis progression, and antiviral therapy failure.
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- Among 131 patients, 75 achieved clearance while 56 did not, with specific SNPs and baseline factors identified as key predictors.
- The best predictive model achieved a high performance of AUC = 0.877, indicating that certain genetic and laboratory factors before treatment can help forecast treatment success.
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