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Our previous collaborative studies has developed a molecular diagnosis tool, which is characterized with a prediction model consisting single nucleotide polymorphisms (SNPs), for assessing the efficacy of interferon combined therapy for chronic hepatitis C (CHC) patients prior to treatment.
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A combination of pegylated-interferon (PEG-IFN) with ribavirin has been the choice for treating chronic hepatitis C (CHC) patients. In addition to the high cost, the treatment takes 6 to 12 months and often brings significant adverse reactions to some patients. It would therefore be beneficial to include a pre-treatment evaluation and post-treatment monitoring system to maximize the efficacy of CHC therapy. We have established a molecular diagnosis tool for assessing the efficacy of interferon combined therapy for CHC patients prior to treatment. This diagnostic tool is characterized with a prediction model consisting single nucleotide polymorphisms (SNPs) strongly associated with interferon efficacy in treating CHC patients. The prediction model has been validated by hepatitis C samples in Taiwan, and it achieved 80 % accuracy, higher than the current efficacy rates. Supplemented with the viral genotype information would further increase the prediction accuracy to 85%.
In this project, we aim to analyze patients' gene expression profiling during the treatment. The current study will focus on 30 PEG-IFN and ribavirin treated patients with HCV genotype 1 infection. After the responding status of those patients has been confirmed, we will compare gene expression profiling among the different responses before and during treatment. By using this information, we can properly select the candidate genes, and establish a monitoring model with these biomarkers. This monitoring model can thus be applied to assess the efficacy of PEG-IFN plus ribavirin combination treatment.
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30 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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