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Molecular Profiles of HCV Cirrhotic Tissues Derived in a Panel of Markers with Clinical Utility for Hepatocellular Carcinoma Surveillance

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  • Ricardo C Gehrau
  • Kellie J Archer
  • Valeria R Mas
  • Daniel G Maluf

Abstract

Background: Early hepatocellular carcinoma (HCC) detection is difficult because low accuracy of surveillance tests. Genome-wide analyses were performed using HCV-cirrhosis with HCC to identify predictive signatures. Methodology/Principal Findings: Cirrhotic liver tissue was collected from 107 HCV-infected patients with diagnosis of HCC at pre-transplantation and confirmed in explanted livers. Study groups included: 1) microarray hybridization set (n = 80) including patients without (woHCC = 45) and with (wHCC = 24) HCC, and with incidental HCC (iHCC = 11); 2) independent validation set (n = 27; woHCC = 16, wHCC = 11). Pairwise comparisons were performed using moderated t-test. FDR

Suggested Citation

  • Ricardo C Gehrau & Kellie J Archer & Valeria R Mas & Daniel G Maluf, 2012. "Molecular Profiles of HCV Cirrhotic Tissues Derived in a Panel of Markers with Clinical Utility for Hepatocellular Carcinoma Surveillance," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-12, July.
  • Handle: RePEc:plo:pone00:0040275
    DOI: 10.1371/journal.pone.0040275
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    1. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
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