Zhicheng Fan1, Qiaoxin Zhang2, Huanzhu Chen3, Ping He4, Yazhen Li5, Mengya Si6, Xiaoyang Jiao7. 1. Cell Biology and Genetics Department of Shantou University Medical College, Shantou, China. Electronic address: 841276469@qq.com. 2. The First Affiliated Hospital of Shantou University Medical College, Shantou, China. Electronic address: qiaoxinzhang@126.com. 3. Cell Biology and Genetics Department of Shantou University Medical College, Shantou, China. Electronic address: 617239833@qq.com. 4. Shantou University Medical College, Shantou, China. Electronic address: Phe@stu.edu.cn. 5. Cell Biology and Genetics Department of Shantou University Medical College, Shantou, China. Electronic address: 742795693@qq.com. 6. The First Affiliated Hospital of Shantou University Medical College, Shantou, China. Electronic address: simengyajc@163.com. 7. Cell Biology and Genetics Department of Shantou University Medical College, Shantou, China. Electronic address: xyjiao@stu.edu.cn.
Abstract
BACKGROUND: Hepatitis C virus (HCV) genotype exerts a major influence on therapeutic response; however, the underlying mechanisms remain unclear. The aim of the study is to investigate the circulating microRNAs as the biomarkers to predict the response to therapy in chronic hepatitisC patients (HepC) with different genotypes. METHODS: HepC patients were separated into 4 groups by genotype, healthy individuals were enrolled as the control. microRNA-122 (miR-122), microRNA-155 (miR-155) and HCV RNA in serum and exosome were measured, associations between microRNAs, viral load and other conventional biomarkers were analyzed. RESULTS: Serum and exosomal HCV RNA in genotype 6a group was highest, followed by genotype 3a/2a, and in genotype 1b were the lowest. The significant correlations existed between exosomal HCV RNA and serum HCVRNA. MiR-122, both in serum (miR-122ser) and in exosome (miR-122exo), was higher in normal control than in HCV group. Specifically, miR-122exo were significantly higher in genotype 1b than other genotype groups (p < 0.05). On the contrary, miR-155exowas significantly lower in genotype 1b than in other groups (p < 0.05 for both). A strongly positive association was found between miR-122/155 and HCV viral load in patients with various genotypes. Higher miR-122ser at the start of therapy predicts a better outcome. CONCLUSIONS: Expression of miR-122/155 differ in each genotypes, miR-122ser could be independent factor affecting the therapy efficacy, which had higher diagnostic value in predicting HCV outcome.
BACKGROUND:Hepatitis C virus (HCV) genotype exerts a major influence on therapeutic response; however, the underlying mechanisms remain unclear. The aim of the study is to investigate the circulating microRNAs as the biomarkers to predict the response to therapy in chronic hepatitisC patients (HepC) with different genotypes. METHODS: HepC patients were separated into 4 groups by genotype, healthy individuals were enrolled as the control. microRNA-122 (miR-122), microRNA-155 (miR-155) and HCV RNA in serum and exosome were measured, associations between microRNAs, viral load and other conventional biomarkers were analyzed. RESULTS: Serum and exosomal HCV RNA in genotype 6a group was highest, followed by genotype 3a/2a, and in genotype 1b were the lowest. The significant correlations existed between exosomal HCV RNA and serum HCVRNA. MiR-122, both in serum (miR-122ser) and in exosome (miR-122exo), was higher in normal control than in HCV group. Specifically, miR-122exo were significantly higher in genotype 1b than other genotype groups (p < 0.05). On the contrary, miR-155exowas significantly lower in genotype 1b than in other groups (p < 0.05 for both). A strongly positive association was found between miR-122/155 and HCV viral load in patients with various genotypes. Higher miR-122ser at the start of therapy predicts a better outcome. CONCLUSIONS: Expression of miR-122/155 differ in each genotypes, miR-122ser could be independent factor affecting the therapy efficacy, which had higher diagnostic value in predicting HCV outcome.
Authors: Ashish Kumar; Susy Kim; Yixin Su; Mitu Sharma; Pawan Kumar; Sangeeta Singh; Jingyun Lee; Cristina M Furdui; Ravi Singh; Fang-Chi Hsu; Jeongchul Kim; Christopher T Whitlow; Michael A Nader; Gagan Deep Journal: EBioMedicine Date: 2021-01-05 Impact factor: 8.143