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Computational models of liver fibrosis progression for hepatitis C virus chronic infection.

ABSTRACT: BACKGROUND: Chronic infection with hepatitis C virus (HCV) is a risk factor for liver diseases such as fibrosis, cirrhosis and hepatocellular carcinoma. HCV genetic heterogeneity was hypothesized to be associated with severity of liver disease. However, no reliable viral markers predicting disease severity have been identified. Here, we report the utility of sequences from 3 HCV 1b genomic regions, Core, NS3 and NS5b, to identify viral genetic markers associated with fast and slow rate of fibrosis progression (RFP) among patients with and without liver transplantation (n = 42). METHODS: A correlation-based feature selection (CFS) method was used to detect and identify RFP-relevant viral markers. Machine-learning techniques, linear projection (LP) and Bayesian Networks (BN), were used to assess and identify associations between the HCV sequences and RFP. RESULTS: Both clustering of HCV sequences in LP graphs using physicochemical properties of nucleotides and BN analysis using polymorphic sites showed similarities among HCV variants sampled from patients with a similar RFP, while distinct HCV genetic properties were found associated with fast or slow RFP. Several RFP-relevant HCV sites were identified. Computational models parameterized using the identified sites accurately associated HCV strains with RFP in 70/30 split cross-validation (90-95% accuracy) and in validation tests (85-90% accuracy). Validation tests of the models constructed for patients with or without liver transplantation suggest that the RFP-relevant genetic markers identified in the HCV Core, NS3 and NS5b genomic regions may be useful for the prediction of RFP regardless of transplant status of patients. CONCLUSIONS: The apparent strong genetic association to RFP suggests that HCV genetic heterogeneity has a quantifiable effect on severity of liver disease, thus presenting opportunity for developing genetic assays for measuring virulence of HCV strains in clinical and public health settings.

SUBMITTER: Lara J

PROVIDER: S-EPMC4120150 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Computational models of liver fibrosis progression for hepatitis C virus chronic infection.

Lara James J López-Labrador F F González-Candelas Fernando F Berenguer Marina M Khudyakov Yury E YE

BMC bioinformatics 20140714

<h4>Background</h4>Chronic infection with hepatitis C virus (HCV) is a risk factor for liver diseases such as fibrosis, cirrhosis and hepatocellular carcinoma. HCV genetic heterogeneity was hypothesized to be associated with severity of liver disease. However, no reliable viral markers predicting disease severity have been identified. Here, we report the utility of sequences from 3 HCV 1b genomic regions, Core, NS3 and NS5b, to identify viral genetic markers associated with fast and slow rate of ...[more]

PMID: 25081062

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Project description:Knowing the rate of liver fibrosis progression in hepatitis C virus (HCV)-infected persons can help inform patients and providers (clinicians, medical institutions or organizations, and third-party payers) in making treatment decisions.To determine the rate and factors associated with liver fibrosis progression and hepatic decompensation in persons after acquiring HCV infection.Secondary data analysis of persons in the Electronically Retrieved Cohort of HCV Infected Veterans (ERCHIVES), a national Veterans Affairs (VA) database, between 2002 and 2012. Among 610?514 persons in ERCHIVES (half were HCV positive), we identified those with an initial negative and subsequent positive test result for HCV antibody and positive HCV RNA test result (HCV+). Controls had 2 negative HCV antibody test results (HCV-) in a comparable time frame and were matched 1:1 on age (in 5-year blocks), race, and sex. We excluded persons with human immunodeficiency virus, hepatitis B, less than 24 months of follow-up, hepatocellular carcinoma, and cirrhosis at baseline.Progression of liver fibrosis as estimated by the Fibrosis-4 (FIB-4) index; development of cirrhosis, defined by a FIB-4 score greater than 3.5; and development of hepatic decompensation.The evaluable data set consisted of 1840 persons who were HCV+ and 1840 HCV- controls. The HCV+ persons were younger and had a lower mean (SD) body mass index (27.39 [5.51] vs 29.49 [6.16]; P?<?.001), a higher prevalence of alcohol and drug abuse and dependence diagnoses, and higher serum aminotransferase levels, but had a lower prevalence of diabetes and hypertension. Fibrosis progression started early after infection among HCV+ persons and tapered off after 5 years. A total of 452 cirrhosis and 85 hepatic decompensation events were recorded. After 10 years of follow-up, HCV+ persons were more likely to have a diagnosis of cirrhosis compared with HCV- controls (18.4% vs 6.1%). Nine years after diagnosis of cirrhosis, hepatic decompensation events were uncommon but had a higher rate in the HCV+ group (1.79% vs 0.33%).Persons who seroconverted for HCV have a more rapid progression of liver fibrosis and accelerated time to development of cirrhosis after seroconversion compared with HCV- controls. Fibrosis progression occurs early after infection; however, hepatic decompensation is uncommon after diagnosis of cirrhosis.

Dataset Information

Computational models of liver fibrosis progression for hepatitis C virus chronic infection.

Publications

Computational models of liver fibrosis progression for hepatitis C virus chronic infection.

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