Suppressor of cytokine signaling 3 (SOCS3) expression and hepatitis C virus–related chronic hepatitis: Insulin resistance and response to antiviral therapy


  • Potential conflict of interest: Nothing to report.


The response to antiviral therapy is lower in hepatitis C virus (HCV) patients with genotype 1 than in those with genotype 2. Overexpression of the suppressor of cytokine signaling 3 (SOCS3) gene in liver tissue is associated with a poorer treatment outcome in patients with chronic hepatitis C viral genotype 1. Also, insulin resistance has been implicated in nonresponse to an anti-HCV treatment. To understand why HCV genotype 1 patients respond differently, we investigated SOCS3 gene expression, metabolic syndrome (MS), and the response to therapy in a cohort of patients with HCV-related hepatitis. A total of 198 patients (108 with genotype 1 and 90 with genotype 2) treated with pegylated interferon plus ribavirin were consecutively enrolled in the study. We measured SOCS3 expression in Epstein-Barr virus–transformed lymphoblastoid cell lines derived from peripheral lymphocytes of a subset of 130 patients. MS was more frequent in genotype 1 patients than in genotype 2 patients (P < 0.01). Nonresponders (P < 0.01), MS (P < 0.001), and genotype 1 (P < 0.001) were significantly related to SOCS3 overexpression. However, SOCS3 levels were higher in nonresponders also, regardless of the genotype (P < 0.01). In a univariate analysis, the genotype (P < 0.001), age (P < 0.001), SOCS3 (P < 0.001), and MS (P < 0.001) were significantly related to the response to therapy. However, in a multivariate analysis, SOCS3 was the only independent predictor of the response (odds ratio = 6.7; P < 0.005). Conclusion: We speculate that SOCS3 expression per se may influence the response to antiviral therapy and that the genotype 1b virus might induce its up-regulation. This may account for the different responses to therapy between genotype 1–infected and genotype 2–infected patients. (HEPATOLOGY 2007.)

Hepatitis C virus (HCV)-related chronic hepatitis is a multifactorial disease whose clinical expression is conditioned by viral, genetic, and metabolic cofactors. The progression of liver fibrosis and the natural history of the disease are related to (1) the virus genotype, (2) the apparent duration of the disease, (3) the presence of steatosis, (4) coinfection with hepatitis B virus and/or human immunodeficiency virus, and (5) chronic alcohol consumption.1 It is considered curable. In fact, the prevalence of a sustained virologic response to antiviral therapy ranges between 40% and 90%.2, 3 However, some patients are refractory to interferon (IFN) treatment. The factors associated with nonresponse to the treatment are the viral genotype, viral load, age, sex, degree of liver fibrosis, and cirrhosis.4, 5 Metabolic factors may also interfere with the treatment response. Patients infected with a genotype other than 3 and affected by steatosis have a poorer treatment outcome than their counterparts without steatosis.6 In line with this finding, diabetes, obesity, and insulin resistance have been found to be strong cofactors of hepatitis C virus (HCV)–related liver disease, and this indicates that they could predict nonresponse to an antiviral treatment.7 Lastly, 2 recent studies have identified a link between the response to an antiviral treatment of HCV-related liver disease and a variety of genes, some of which are up-regulated and some of which are down-regulated.8 In particular, the overexpression of the suppressor of cytokine signaling 3 (SOCS3) gene in liver tissue is associated with a poorer treatment outcome.9, 10

Despite the previously reported data, it is still not possible to predict an individual's response to an IFN-based treatment. In addition, it is not known why genotype 1b HCV–infected patients respond to antiviral therapy differently than patients infected with a genotype other than 1. Is this difference related to the virus, to peculiar genetic characteristics of the host, or to both?

In an attempt to shed light on these issues, we assessed, in a large cohort of treated patients affected by HCV-related chronic disease, the prevalence of SOCS3 and its relationship to the virus genotype and to the response to therapy. We also evaluated whether metabolic syndrome (MS), which is the clinical expression of insulin resistance, is associated with SOCS3 expression and with the response to treatment.


ALT, alanine aminotransferase; BMI, body mass index; Ct, cycle threshold; HC, healthy controls; HCV, hepatitis C virus; IFN, interferon; MS, all patients with metabolic syndrome; MS, metabolic syndrome; MS NR1, genotype 1b nonresponder patients with metabolic syndrome; MS R1, genotype 1b responder patients with metabolic syndrome; NR, nonresponder patients; NR1, genotype 1b nonresponders; NR2, genotype 2 nonresponders; NS, not significant; OR, odds ratio; PCR, polymerase chain reaction; R, responder patients; R1, genotype 1b responders; R2, genotype 2 responders; SOCS3, suppressor of cytokine signaling 3.

Patients and Methods

One hundred ninety eight consecutive inpatients with HCV-related chronic hepatitis were enrolled in the study. Patients were recruited at 2 liver centers (Naples and Trieste). All patients had received antiviral therapy (pegylated IFN-α and ribavirin). The inclusion criteria were (1) elevated alanine aminotransferase (ALT) levels during the last 6 months, (2) HCV antibodies, (3) non–genotype 3 HCV RNA, and (4) no history of alcohol abuse. The exclusion criteria were (1) HCV antibodies with genotype 3, (2) other overt hepatitis virus infections (that is, hepatitis B surface antigen+), (3) alcohol abuse (<20 mg/day in women and <30 mg/day in men in the 5 years before enrollment) evaluated according to Reid,11 (4) a history of drug abuse, and (5) a human immunodeficiency virus–positive test.

Virological, epidemiological, biochemical, and ultrasound data were recorded on admission to the center. The body mass index [BMI (kg/m2)] was calculated at the time of liver biopsy. When possible, the apparent disease duration was determined by the consideration of the exposure to major risk factors as the start of infection.

Diabetes mellitus was identified according to the American Diabetes Association criteria, namely, fasting glucose greater than 126 mg/dL on 2 separate occasions or a positive oral glucose tolerance test on 2 separate occasions.12 The total cholesterol, triglycerides, gamma-glutamyl transferase, ALT, and ferritin were measured after a 12-hour fast. MS was diagnosed according to the National Cholesterol Education Program Adult Treatment Panel III criteria.13 Markers of HBV infection were tested by a commercially available enzyme-linked immunosorbent assay from Abbott Laboratories (Abbott Park, IL). The study was approved by the Ethics Committee of the Second University of Naples, and the patients gave their informed consent.

Liver Biopsy and Histology

Hepatic percutaneous biopsy was performed with a Surecut 17-gauge needle by the intercostal route and an echo-assisted method. Liver specimens were used for histological examinations if they were at least 1.5 cm long and contained more than 5 portal spaces. Specimens were fixed in formalin, embedded in paraffin, and stained with hematoxylin-eosin. Biopsies were evaluated with the Ishak score.14

Western Blotting

Frozen liver tissue (1 g) from each patient was homogenized in 10 mL of a radioimmunoreactive protein extraction assay lysis buffer [50 mM Tris-HCL, pH 8.0, 150 mM NaCl, 1% Triton X-100, 0.1% sodium dodecyl sulfate, 10% glycerol, and a complete protease inhibitor cocktail (Roche, Milan, Italy)]. The protein extract concentrations were determined with a bicinchoninic acid assay (Pierce, Rockford, IL). Fifty micrograms of the lysates was diluted 1:1 with a Laemmli sodium dodecyl sulfate–polyacrylamide gel electrophoresis sample buffer, loaded onto 12% polyacrylamide gels, and blotted onto poly(vinylidene fluoride) membranes (Bio-Rad, Milan, Italy). The membranes were blocked with 5% nonfat milk (Bio-Rad) in phosphate-buffered saline (pH 7.6) with 0.2% Tween-20 overnight at 4°C. The membranes were then incubated with a specific commercial mouse anti-SOCS3 antibody (1:500; BioLegend, San Diego, CA). After being washed in phosphate-buffered saline (pH 7.6) with 0.2% Tween-20, the membranes were incubated with a horseradish peroxidase–conjugated goat anti-mouse immunoglobulin G antibody (1:10.000; Santa Cruz Biotechnology, Santa Cruz, CA), and the immunoblots were visualized with enhanced chemiluminescence detection kits with enhanced chemiluminescence (Pierce). A mouse anti–β-actin antibody (1:1.000; Sigma-Aldrich, Milan, Italy) was used as the control for equal loading.

A semiquantitative analysis of the protein expression was performed. The bands were quantified by densitometry to obtain an integral optical density value, which then was normalized with respect to the β-actin value.

RNA Preparation and HCV RNA Determination

All steps were carried out under ribonuclease-free conditions. The polymerase chain reaction (PCR) procedure was used to determine HCV RNA. Sera were rapidly (within 30 minutes of blood drawing) frozen at −20°C. RNA was extracted according to Chomczynsky and Sacchi,15 and complementary DNA was derived. To identify HCV RNA, a nested PCR was performed with primers that expanded the highly conserved 5′ noncoding genomic region. Carryover PCR contamination was avoided by the application of the measures suggested by Kwok and Higuchi.16

HCV Genotyping

To classify the HCV genotypes, serum PCR products were hybridized to type-specific and subtype-specific probes 1a, 1b, 2a, 2b, and 3a. The probes had to fulfill 2 main criteria: there could be no more than 2 mismatches in comparison with the corresponding published sequences of the same subtype, and they had to differ by 3 or more mismatches in comparison with published sequences of other types and subtypes. The only exception was probe 2b, which had only 2 mismatches in comparison with the corresponding sequence of type 3a.17

Genetic Analysis

The SOCS3 expression was measured in a subset of 130 HCV-infected patients (65 with genotype 1b and 65 with genotype 2) and in 84 healthy subjects with the procedures described next.

Generation of Cell Lines.

Epstein-Barr virus–transformed lymphoblastoid cell lines were established through the culturing of peripheral blood mononuclear cells in the presence of 100 μg/mL cyclosporin A (Sigma-Aldrich) with the Epstein-Barr virus–transformed supernatant harvested from the B95-8 cell line (German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany). The Epstein-Barr virus–transformed lymphoblastoid cell lines were maintained in an RPMI-1640 medium supplemented with 20% fetal bovine serum, 2 mM glutamine, 20 U/mL penicillin, and 0.2 mg/mL streptomycin (CELBIO, Pero, Milan, Italy).

RNA Isolation and Reverse Transcription.

The total cellular RNA was extracted from the cultured cells by the guanidinium isothiocyanate method with the TRIzol reagent. All reverse transcriptase reactions were performed with SuperScript II RNase H-Reverse Transcriptase (Invitrogen, San Diego, CA), with oligo-dT priming, according to the manufacturer's instructions.

Quantitative Real-Time PCR Analysis.

Quantitative real-time PCR was performed with the SYBER green method with an Applied Biosystem model 7900HT sequence detection system. The primers were designed with the Primer Express 2.0 program (Applied Biosystems, Branchburg, NJ), and the sequences were as follows: forward primer, CTTTCTGATCCGCGACAGCT, and reverse primer, TGGTCCCAGACTGGGTCTTG. All PCR reactions were performed in duplicate. This quantitative real-time PCR was validated by the analysis of 2 different genes (myxovirus resistance A and 2′,5′-oligoadenylate synthetase), which were down-regulated in nonresponders.18 The relative gene expressions were calculated with the 2(−ΔCt) method, where Ct indicates the cycle threshold, the fractional cycle number at which the fluorescent signal reaches the detection threshold. β-Actin was used as an internal control.19 ΔCt was calculated from the differences in the mean Ct between the SOCS3 gene and the internal controls.

Statistical Analysis

The absolute frequencies were compared with the χ2 test. When it was appropriate, clinical and laboratory data were compared with the Student t test, the Kruskal-Wallis test, or the Mann-Whitney test. A multivariate analysis was used to calculate associations among dependent and independent variables. We used the Statistical Package for the Social Sciences program (SPSS, version for Windows) to analyze the data. P < 0.05 was considered significant.


Table 1 reports epidemiological, clinical, and histological data for patients affected by HCV genotype 1b or genotype 2. Age, sex, calorie intake per day, serum bilirubin, serum albumin, platelet count, HCV RNA serum levels, PCR, and fibrosis staging did not differ between the 2 groups. On the contrary, MS and BMI were significantly higher in genotype 1b patients (P < 0.002), whereas, as expected, the ALT values and the sustained virologic response were higher (P < 0.001) in genotype 2 patients (Table 1). Similarly, SOCS3 expression (2−Δct) was significantly higher (P < 0.001) in genotype 1b patients than in genotype 2 patients (Table 1) and in nonresponder patients than in either controls (P < 0.01) or responder patients (P < 0.01; Fig. 1).

Table 1. Epidemiological, Clinical, and Genetic Data for Genotype 1 and Genotype 2 Patients
 Genotype 1bGenotype 2P
  1. The data are reported as the mean ± the standard deviation. ALT indicates alanine aminotransferase; BMI, body mass index; HCV, hepatitis C virus; MS, metabolic syndrome; NS, not significant; PCR, polymerase chain reaction; and Socs3, suppressor of cytokine signaling 3. *The data refer to a subset of 130 patients.

Sex (male/female)46/620/40NS
Age55 ± 1055 ± 11NS
BMI28 ± 5.525 ± 4.250.002
Calorie intake (kcal/day)2500 ± 4002400 ± 350NS
ALT70.6 ± 55.698 ± 930.03
PCR (g/L)0.4 ± 0.20.3 ± 0.6NS
Serum bilirubin (mg/dL)0.8 ± 0.40.8 ± 0.3NS
Serum albumin (g/L)40.2 ± 4.541.0 ± 5.3NS
Platelets (×109/L)152.3 ± 41.8160.0 ± 42.3NS
HCV RNA (103 copies/L)5.047 ± 5.3585.513 ± 6.002NS
Staging —Median —Range3 1–63 1–6NS
MS (%)44/108 4120/90 220.006
Socs3 (2ΔcT)* —Median —Range0.46 0.191–5.040.25 0.08–1.480.001
Figure 1.

Correlation between the SOCS3 expression, response to antiviral therapy, and metabolic syndrome. The box plot shows the median values of 2−ΔCT in healthy controls (HC), all patients with metabolic syndrome (MS), responder patients (R), and nonresponder patients (NR). *P < 0.001 for HC versus R.*P < 0.01 for R versus NR. *P < 0.01 for HC versus MS.

These data were confirmed with 18 samples from liver tissue of nonresponder patients (9 genotype 1 and 9 genotype 2). The SOCS3 protein level was significantly higher in liver samples of genotype 1 subjects (Fig. 2). Figure 1 also shows that SOCS3 expression was significantly higher in responders (<0.001) and patients with MS (P < 0.01) than in healthy subjects.

Figure 2.

Levels of the SOCS3 protein in liver tissue from HCV patients. (A) Western blotting of SOCS3 and β-actin for 9 nonresponder HCV genotype 1b (Genotype 1b) and 9 nonresponder HCV genotype 2 (Genotype 2) subjects. (B) Groupwise comparison of normalized levels of SOCS3 between the 2 groups. The protein levels were significantly higher in the Genotype 1b group (15.85 ±0.17) than in the Genotype 2 group (14.81 ±0.27; Mann-Whitney test; P < 0.05). The signal intensity of the specific bands was determined with a densitometer. The levels of SOCS3 in each of the patients were normalized to those of β-actin. The y axis shows the optical density of the SOCS3 expression. The data are presented as the means ± the standard error of the mean.

Given this scenario, we measured SOCS3 gene expression in responder and nonresponder HCV-infected patients divided according to the genotype. As shown in Fig. 3, in both groups, the median SOCS3 expression was significantly higher in genotype 1b–infected patients (P < 0.001). However, it was also significantly higher in nonresponders than in responders in both genotype 1b and 2 patients (P < 0.001). In agreement to that, obesity which is deeply involved in the development of insulin resistance as well as metabolic syndrome significantly relates to SOCS3 (Fig. 4). Similarly, SOCS3 expression was significantly higher (P < 0.01) in patients with the MS (Fig. 1). All these patients were infected with genotype 1b virus, and enhanced SOCS3 expression was significantly related to nonresponse to antiviral therapy (Fig. 5). Moreover, only genotype 1b patients with MS had higher levels of SOCS3 expression than healthy controls (P < 0.001), and among MS patients, nonresponders had significantly higher SOCS3 levels than responders (P < 0.0001).

Figure 3.

Correlation between the SOCS3 expression and the response to antiviral therapy according to the genotype: box plot showing the median values of 2−ΔCT in genotype 1b responders (R1), genotype 2 responders (R2), genotype 1b nonresponders (NR1), and genotype 2 nonresponders (NR2). *P < 0.001 for R1 versus R2 and NR1 versus NR2. **P < 0.001 for R1 versus NR1 and R2 versus NR2.

Figure 4.

Correlation between the SOCS3 expression and obesity: linear regression of 2−ΔCT and BMI. r = 0.255; adjusted r2 = 0.055; P < 0.001.

Figure 5.

Correlation between the SOCS3 expression and metabolic syndrome in all patients and in genotype 1b patients according to the response to the treatment. The box plot shows median values of 2−ΔCT in healthy controls (HC), all patients with metabolic syndrome (MS), genotype 1b nonresponder patients with metabolic syndrome (MS NR1), and genotype 1b responder patients with metabolic syndrome (MS R1). *P < 0.0001 for HC versus MS. **P < 0.028 for MS NR1 versus MS R1

We carried out both univariate and multivariate analyses to identify factors predictive of the response to antiviral treatment (Table 2). At univariate analysis, genotype (P < 0.038), age (P < 0.038), SOCS3 (P < 0.001) and MS (P < 0.007) were significantly related to treatment outcome. However, at multivariate analysis, SOCS3 expression was the only independent risk factor predictive of treatment response with an odds ratio (OR) of 6.6 (1.78-24.26) (P < 0.005).

Table 2. Univariate and Multivariate Analyses of Factors Associated with the Response to Antiviral Treatment
Univariate AnalysisMultivariate Analysis
VariableOR95% confidence intervalPOR95% confidence intervalP
  1. Abbreviations: BMI, body mass index; MS, metabolic syndrome; OR, odds ratio; Socs3, suppressor of cytokine signaling 3.



Our study shows that SOCS3 expression is significantly associated with response to antiviral therapy and this association is genotype-dependent.

The suggestive evidence that HCV infection per se plays a role in SOCS3 gene regulation is certainly the main pathophysiological scenario which these results could be based on. Other previous reports showed that HCV core directly affects SOCS3 regulation10, 20 enhancing that the above mentioned hypothesis coming out from our study. Differently from other previous reports we hereby showed the possibility to detect this SOCS3 overexpression by peripheral blood sample, mimicking the molecular profile detected in the liver tissue.

SOCS3 may interfere at the level of the insulin receptor phosphorylation in obese patients21 and the hyperexpression of SOCS3 in patients affected by MS confirms that SOCS3 may impair the insulin signaling pathway responsible for the onset of MS.

SOCS3 was also found high in NR patients. This is in line with data showing that the defective response to interferon treatment in HCV infected subjects might be mediated through the activation of SOCS39, 10 and highlights the possibility that a peculiar genetic background of SOCS3 (i.e., specific polymorphisms) per se might explain the nonresponse of many HCV positive patients to antiviral therapy. The finding that SOCS3 was higher in genotype 1b patients also suggests that this genotype might exert its pathogenetic effect by modulating the expression of SOCS3 gene. This hypothesis is supported by recent evidence that genotype 1b C virus links HCV core with SOCS3 activating receptors.22 To explain the differences between genotypes here reported, one might assume that specific genetic sequences of the genotype 1b viral genome (i.e., core, hyper variable region, etc) might exert a direct induction of SOCS3 expression.

Taken together, our results suggest that one of the factors underlying the lower response to antiviral treatment in “difficult-to-treat” (genotype 1b+/−MS) versus “easy-to-treat” patients (genotype 2+/−MS) may be the induction, by HCV genotype 1b, of the SOCS3 gene whose up-regulation per se may be involved in non-response to interferon treatment.

Admittedly various genotype-dependent mechanism(s) may be involved in producing HCV-related liver damage, and the “virological steatosis” related to genotype 3a infection differs from the “metabolic steatosis” associated with nongenotype 3-infected subjects.23–27 There appears to be a link between HCV infection and clinical evidence of insulin resistance (i.e., the MS),28 represented histologically by evidence of steatosis/steatohepatitis.29 In line with this concept is the report of a significant correlation between HCV viremia, serum levels of TNF-α and HOMA values.30, 31 This observation is also in accordance with HCV-induced up-regulation of SOCS3 in hepatocytes.32

Our finding that the MS was significantly more frequent in the genotype 1b group of patients (Table 1) reveals a hitherto unknown intriguing association between genotype 1b virus and insulin resistance.

The higher levels for SOCS3 in NR1 than in R1 patients affected by MS is in line with Walsh's results9 which reported a significant correlation between nonresponse to antiviral therapy, high serum levels of TNF-α and high hepatic levels of SOCS3 protein. This, together with our data, supports the idea that the association between HCV infection and insulin resistance may result from a specific genotype 1b-related mechanism involving up-regulation of SOCS3 expression, the latter being responsible for both non-response and MS onset.

SOCS3 was the only independent factor associated with non-response to the antiviral treatment by the multivariate analysis. The highly significant OR and the finding that genotype and MS were no longer related to non-response to therapy at logistic regression strongly suggest that both genotype and MS are related to SOCS3 gene expression. Consequently, enhanced SOCS3 expression seems to be the prime determinant to nonresponse to antiviral treatment and of the clinical onset of MS, both of which are strictly associated with genotype 1 infection.

In conclusion, response to antiviral therapy may be conditioned by a specific genetic background that involves enhanced expression of SOCS3. Genotype 1b-mediated induction of SOCS3 may account for the different response to treatment between genotype 1b-infected and genotype 2-infected patients. SOCS3 induction might also explain the higher prevalence of metabolic syndrome in genotype 1b patients, and the higher association of metabolic syndrome with non-response to treatment in these patients. Based on our results, insulin resistance and its clinical expression should not be considered a metabolic cofactor of non-response to antiviral treatment but rather another expression of SOCS3 up-regulation. Based on these findings, antiviral treatment associated to drugs aimed at reducing insulin resistance is unlikely to improve response to antiviral treatment. Further biological and genetic studies are required to verify these hypotheses and to explore the possibility of a therapeutic approach aimed at reducing SOCS3 gene expression.


We are grateful to Professors Piero Almasio and Tommaso Stroffolini for their assistance with the statistical analysis. We are indebted to Jean Ann Gilder for text editing.