title: Prediction of liver fibrosis and cirrhosis in chronic hepatitis B infection by serum proteomic fingerprinting: a pilot study.
abstract: BACKGROUND
  Most noninvasive predictive models of liver fibrosis are complicated and have suboptimal sensitivity. This study was designed to identify serum proteomic signatures associated with liver fibrosis and to develop a proteome-based fingerprinting model for prediction of liver fibrosis.
  
  
  METHODS
  Serum proteins from 46 patients with chronic hepatitis B (CHB) were profiled quantitatively on surface-enhanced laser desorption/ionization (SELDI) ProteinChip arrays. The identified liver fibrosis-associated proteomic fingerprint was used to construct an artificial neural network (ANN) model that produced a fibrosis index with a range of 0-6. The clinical value of this index was evaluated by leave-one-out cross-validation.
  
  
  RESULTS
  Thirty SELDI proteomic features were significantly associated with the degree of fibrosis. Cross-validation showed that the ANN fibrosis indices derived from the proteomic fingerprint strongly correlated with Ishak scores (r = 0.831) and were significantly different among stages of fibrosis. ROC curve areas in predicting significant fibrosis (Ishak score >or=3) and cirrhosis (Ishak score >or=5) were 0.906 and 0.921, respectively. At 89% specificity, the sensitivity of the ANN fibrosis index in predicting fibrosis was 89%. The sensitivity for prediction increased with degree of fibrosis, achieving 100% for patients with Ishak scores >4. The accuracy for prediction of cirrhosis was also 89%. Inclusion of International Normalized Ratio, total protein, bilirubin, alanine transaminase, and hemoglobin in the ANN model improved the predictive power, giving accuracies >90% for the prediction of fibrosis and cirrhosis.
  
  
  CONCLUSIONS
  A unique serum proteomic fingerprint is present in the sera of patients with fibrosis. An ANN fibrosis index derived from this fingerprint could differentiate between different stages of fibrosis and predict fibrosis and cirrhosis in CHB infection.
authors.affiliation: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, New Territories, Hong Kong Special Administrative Region, China. tcwpoon@cuhk.edu.hk
authors.forename: Terence C W
authors.initials: TC
authors.lastname: Poon
authors.forename: Alex Y
authors.initials: AY
authors.lastname: Hui
authors.forename: Henry L Y
authors.initials: HL
authors.lastname: Chan
authors.forename: Irene Ling
authors.initials: IL
authors.lastname: Ang
authors.forename: Shuk Man
authors.initials: SM
authors.lastname: Chow
authors.forename: Nathalie
authors.initials: N
authors.lastname: Wong
authors.forename: Joseph J Y
authors.initials: JJ
authors.lastname: Sung
mesh_terms: D001798:Blood Proteins / Q000032:analysis*; D005260:Female; D006505:Hepatitis / Q000097:blood; D019698:Hepatitis C, Chronic / Q000097:blood / Q000150:complications*; D006801:Humans; D008099:Liver / Q000503:physiopathology; D008103:Liver Cirrhosis / Q000175:diagnosis* / Q000209:etiology; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010865:Pilot Projects; D011237:Predictive Value of Tests; D011379:Prognosis; D040081:Protein Array Analysis; D040901:Proteomics; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods
chemical_list: D001798:Blood Proteins