Prediction of prognostic biomarkers for Interferon-based therapy to Hepatitis C Virus patients : a metaanalysis of the NS5A protein in subtypes 1a, 1b

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 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 (CARs) 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.

CARs 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%).

Conclusion: The V3 region was a more accurate biomarker than the ISDR region. Subtype-specific CARs gave better support and confidence than hidden Markov models HMMs scores, genetic distances (GDs) or number of variable sites (NVS), and would thus aid in the prediction of prognostic biomarkers and improve the accuracy of prognosis.

Sites-specific CARs in the V3 region of the NS5A protein have given the best support and confidence.

Author: Mahmoud ElHefnawiSuher ZadaIman El-AzabCredits/Source: Virology Journal 2010, 7:130

Published on: 2010-06-15

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