Hepatitis C virus is a leading cause of chronic liver disease, with over 170 million people infected worldwide.

It is also the leading indication for liver transplantation. Complications from chronic hepatitis C infection include cirrhosis, hepatic decompensation, and hepatocellular carcinoma. As a result, treatment strategies to prevent such complications have been widely researched, although many questions remain unanswered. To date, the standard therapy for chronic hepatitis C infection is the combination of peginterferon and .ribavirin.A large proportion of patients do not respond to therapy for reasons that are unclear. The heterogeneity of viral and host phenotypes makes it unlikely that any single factor will accurately predict the cellular response to treatment.It was supposed that liver tissue of nonresponder and responder show consistent differences in gene expression levels and that these differences could be used to predict treatment outcomes.Hepatic gene expression profiling identified consistent differences in patients who subsequently fail treatment with pegylated IFN-α plus ribavirin: up-regulation of a specific set of IFN-responsive genes predicts nonresponse to exogenous therapy.

These data may be of use in predicting clinical responses to treatment. 18 genes, confirmed by real-time PCR, with expression levels that differed consistently between nonresponders and responders liver tissue were detected. Levels for these 18 genes in responders liver were closer to uninfected tissue than to nonresponders liver, with a general up-regulation of gene expression in nonresponders liver. Many of these genes are IFN responsive, suggesting that the nonresponders patients have adopted a different, yet characteristic, equilibrium in their host-virus immune response(Chen L et al. Gastroentology 2005:128:1437-1444) We have found a method that can predict success or failure of the treatment of chronic hepatitis C based on the weighted expression level of a small number of genes (4-5) We derive the genes and their decision weights from the microarray data set obtained from the liver biopsy of the patients in the above mentioned paper . We would like to test the prediction power of this method in an independent set of liver tissues of treated patients in our department.