title: Plasma microRNA profile as a predictor of early virological response to interferon treatment in chronic hepatitis B patients.
abstract: BACKGROUND
  Interferon (IFN) and pegylated interferon (PEG-IFN) treatment of chronic hepatitis B leads to a sustained virological response in a limited proportion of patients and has considerable side effects. To find novel markers associated with prognosis of IFN therapy, we investigated whether a pretreatment plasma microRNA profile could be used to predict early virological response to IFN.
  
  
  METHODS
  We performed microRNA microarray analysis of plasma samples from 94 patients with chronic hepatitis B who received IFN therapy. The microRNA profiles from 13 liver biopsy samples were also measured. The OneR feature ranking and incremental feature selection method were used to rank and optimize the number of features in the model. Support vector machine prediction engine and jack-knife cross-validation were used to generate and evaluate the prediction model.
  
  
  RESULTS
  The optimized model consisting of 11 microRNAs yielded a 74.2% overall accuracy in the training group and was independently confirmed in the test group (71.4% accuracy). Univariate and multivariate logistic regression analyses confirmed its independent association with early virological response (OR=7.35; P=2.12×10(-5)). Combining the microRNA profile with the alanine aminotransferase level improved the overall accuracy from 73.4% to 77.3%. Co-transfection of an HBV replicative construct with microRNA mimics revealed that let-7f, miR-939 and miR-638 were functionally associated with the HBV life cycle.
  
  
  CONCLUSIONS
  The 11 microRNA signatures in plasma, together with basic clinical variables, might provide an accurate method to assist in medication decisions and improve the overall sustained response to IFN treatment.
authors.affiliation: Research Unit, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
authors.forename: Xiaonan
authors.initials: X
authors.lastname: Zhang
authors.forename: Cuncun
authors.initials: C
authors.lastname: Chen
authors.forename: Min
authors.initials: M
authors.lastname: Wu
authors.forename: Liang
authors.initials: L
authors.lastname: Chen
authors.forename: Jiming
authors.initials: J
authors.lastname: Zhang
authors.forename: Xinxin
authors.initials: X
authors.lastname: Zhang
authors.forename: Zhanqin
authors.initials: Z
authors.lastname: Zhang
authors.forename: Jingdi
authors.initials: J
authors.lastname: Wu
authors.forename: Jiefei
authors.initials: J
authors.lastname: Wang
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Chen
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.forename: Lixiang
authors.initials: L
authors.lastname: Chen
authors.forename: Zhenghong
authors.initials: Z
authors.lastname: Yuan
mesh_terms: D000328:Adult; D000410:Alanine Transaminase / Q000032:analysis; D001706:Biopsy; D002460:Cell Line; D005260:Female; D006515:Hepatitis B virus / Q000472:pathogenicity*; D019694:Hepatitis B, Chronic / Q000188:drug therapy* / Q000821:virology; D006801:Humans; D000077190:Interferon alpha-2; D016898:Interferon-alpha / Q000494:pharmacology*; D008099:Liver / Q000378:metabolism / Q000473:pathology / Q000821:virology; D016015:Logistic Models; D008297:Male; D035683:MicroRNAs / Q000097:blood* / Q000378:metabolism; D008875:Middle Aged; D016017:Odds Ratio; D020411:Oligonucleotide Array Sequence Analysis; D011092:Polyethylene Glycols / Q000494:pharmacology*; D011237:Predictive Value of Tests; D011379:Prognosis; D011994:Recombinant Proteins / Q000494:pharmacology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D000077190:Interferon alpha-2; D016898:Interferon-alpha; D035683:MicroRNAs; D011994:Recombinant Proteins; D011092:Polyethylene Glycols; D000410:Alanine Transaminase; C417083:peginterferon alfa-2b; C100416:peginterferon alfa-2a