title: Prediction of prognostic biomarkers for interferon-based therapy to hepatitis C virus patients: a meta-analysis of the NS5A protein in subtypes 1a, 1b, and 3a.
abstract: BACKGROUND
  Hepatitis C virus (HCV) is a worldwide health problem with no vaccine and the only approved therapy is Interferon-based plus Ribavarin. Response prediction to treatment has health and economic impacts, and is a multi-factorial problem including both host and viral factors (e.g: age, sex, ethnicity, pre-treatment viral load, and dynamics of the HCV non-structural protein NS5A quasispecies). We implement a novel approach for extracting features including informative markers from mutations in the non-structural 5A protein (NS5A), specifically its Interferon sensitivity determining region (ISDR) and V3 regions, and use a novel bioinformatics approach for pattern recognition on the NS5A protein and its motifs to find biomarkers for response prediction using class association rules and comparing the predictability of the different features.
  
  
  RESULTS
  A total of 58 sequences from sustained responders and 94 from non-responders were downloaded from the HCV LANL database. Site-specific signatures for response prediction from the NS5A protein were extracted from the alignments. Class association rules were generated (e.g.: sustained response is associated with position A2368T in subtype 1a (support 100% and confidence 52.19%); in subtype 1b, response is associated with E2356G/D/K (support 76.3% and confidence 67.3%).
  
  
  CONCLUSIONS
  The V3 region was a more accurate biomarker than the ISDR region. Subtype-specific class association rules gave better support and confidence than profile hidden Markov models HMMs scores, genetic distances or number of variable sites, and would thus aid in the prediction of prognostic biomarkers and improve the accuracy of prognosis. Sites-specific class association rules in the V3 region of the NS5A protein have given the best support and confidence.
authors.affiliation: Informatics and Systems Department, Division of Engineering Research, National Research Centre, Tahrir Street, Cairo, Egypt. mahef@aucegypt.edu
authors.forename: Mahmoud M
authors.initials: MM
authors.lastname: ElHefnawi
authors.forename: Suher
authors.initials: S
authors.lastname: Zada
authors.forename: Iman A
authors.initials: IA
authors.lastname: El-Azab
mesh_terms: D000595:Amino Acid Sequence; D000998:Antiviral Agents / Q000627:therapeutic use*; D015415:Biomarkers; D016174:Hepacivirus / Q000145:classification / Q000235:genetics*; D006526:Hepatitis C / Q000175:diagnosis / Q000188:drug therapy*; D006801:Humans; D007372:Interferons / Q000627:therapeutic use*; D008969:Molecular Sequence Data; D009154:Mutation; D012254:Ribavirin / Q000627:therapeutic use; D017361:Viral Nonstructural Proteins / Q000737:chemistry / Q000235:genetics*; D012324:RNA-Dependent RNA Polymerase
chemical_list: D000998:Antiviral Agents; D015415:Biomarkers; D007372:Interferons; D012254:Ribavirin; D017361:Viral Nonstructural Proteins; C087036:NS-5 protein, hepatitis C virus; D012324:RNA-Dependent RNA Polymerase