Department of Gastroenterology, Yamagata University School of Medicine, Yamagata, Japan.
J Med Virol. 2010 Aug;82(8):1364-70. doi: 10.1002/jmv.21818.
The aim of the study was to identify a predictive marker for the virological response in hepatitis C virus 1b (HCV-1b)-infected patients treated with pegylated interferon plus ribavirin therapy. A total of 139 patients with chronic hepatitis C who received therapy for 48 weeks were enrolled. The secondary structure of the 120 residues of the amino-terminal HCV-1b non-structural region 3 (NS3) deduced from the amino acid sequence was classified into two major groups: A and B. The association between HCV NS3 protein polymorphism and virological response was analyzed in patients infected with group A (n = 28) and B (n = 40) isolates who had good adherence to both pegylated interferon and ribavirin administration (>95% of the scheduled dosage) for 48 weeks. A sustained virological response (SVR) representing successful HCV eradication occurred in 33 (49%) in the 68 patients. Of the 28 patients infected with the group A isolate, 18 (64%) were SVR, whereas of the 40 patients infected with the group B isolate only 15 (38%) were SVR. The proportion of virological responses differed significantly between the two groups (P < 0.05). These results suggest that polymorphism in the secondary structure of the HCV-1b NS3 amino-terminal region influences the virological response to pegylated interferon plus ribavirin therapy, and that virus grouping based on this polymorphism can contribute to prediction of the outcome of this therapy.
本研究旨在寻找丙型肝炎病毒 1b(HCV-1b)感染患者接受聚乙二醇干扰素加利巴韦林治疗时病毒学应答的预测标志物。共纳入 139 例接受 48 周治疗的慢性丙型肝炎患者。根据氨基酸序列推导的 HCV-1b 非结构区 3(NS3)氨基末端的 120 个残基的二级结构分为 A 和 B 两组。分析了对聚乙二醇干扰素和利巴韦林治疗具有良好依从性(>95%的计划剂量)的 48 周的 A 组(n = 28)和 B 组(n = 40)分离株感染患者的 HCV NS3 蛋白多态性与病毒学应答之间的关系。代表 HCV 成功清除的持续病毒学应答(SVR)在 68 例患者中的 33 例(49%)中发生。28 例 A 组分离株感染患者中,18 例(64%)为 SVR,而 40 例 B 组分离株感染患者中仅 15 例(38%)为 SVR。两组之间的病毒学应答比例差异有统计学意义(P < 0.05)。这些结果表明,HCV-1b NS3 氨基末端区域二级结构的多态性影响聚乙二醇干扰素加利巴韦林治疗的病毒学应答,基于这种多态性的病毒分组有助于预测该治疗的结果。