Elevated expression and polymorphisms of SOCS3 influence patient response to antiviral therapy in chronic hepatitis C. Issue 4 (19th September 2007) - Persico, M; Capasso, M; Russo, R; Persico, E; Crocè, L; Tiribelli, C; Iolascon, A
Abstract : Background: The response to antiviral therapy of chronic hepatitis C virus (HCV) infection is determined by virological, environmental and genetic factors. Objective: The hypothesis was tested that the expression of specific genes and their haplotype frequencies can differentiate between non-responders (NRs) and sustained virological responders (SVRs) to antiviral treatment. Methods: A methodological approach based on molecular marker discovery and validation was used to study the genes influencing the antiviral treatment in lymphoblastoid cell lines from 74 genotype 1b HCV patients (44 from Southern Italy and 30 from Northern Italy) treated with pegylated interferon (IFN) α and ribavirin. Furthermore, an association study was performed, testing three single nucleotide polymorphisms (SNPs) of suppressor of cytokine signalling 3 (SOCS3) in 162 NR and 184 SVR subjects (SOCS3 −8464 A/C (rs12952093), −4874 A/G (rs4969170) and 1383 A/G, (rs4969168)). Results: SOCS3 basal expression levels were significantly increased in two independent sets of NR groups (p<0.05). A highly significant association was found between NRs and both the positively associated haplotype (OR = 2.01, 95% CI 1.45 to 2.79, p = 0.0002) and the negatively associated haplotype (OR = 0.56, 95% CI 0.42 to 0.76, p = 0.0014). In particular, the SOCS3 −4874 AA genotype was strongly associated with failure of antiviral therapy (OR = 4.00, 95% CI 2.09 to 7.66, p = 0.0003) and the AA genotype carriers had significantly higher SOCS3 mRNA and protein levels (p<0.05). Conclusions: Basal levels of SOCS3, an inhibitor of the IFNα-induced Janus kinase–signal transducer and activator of transcription pathways, and its genetic polymorphisms influence the outcome of antiviral treatment. SOCS3 thus represents a novel blood biomarker for the a priori prediction of treatment response.