Phosphoproteomic biomarkers predicting histologic nonalcoholic steatohepatitis and fibrosis

J Proteome Res. 2010 Jun 4;9(6):3218-24. doi: 10.1021/pr100069e.

Abstract

The progression of nonalcoholic fatty liver disease (NAFLD) has been linked to deregulated exchange of the endocrine signaling between adipose and liver tissue. Proteomic assays for the phosphorylation events that characterize the activated or deactivated state of the kinase-driven signaling cascades in visceral adipose tissue (VAT) could shed light on the pathogenesis of nonalcoholic steatohepatitis (NASH) and related fibrosis. Reverse-phase protein microarrays (RPMA) were used to develop biomarkers for NASH and fibrosis using VAT collected from 167 NAFLD patients (training cohort, N = 117; testing cohort, N = 50). Three types of models were developed for NASH and advanced fibrosis: clinical models, proteomics models, and combination models. NASH was predicted by a model that included measurements of two components of the insulin signaling pathway: AKT kinase and insulin receptor substrate 1 (IRS1). The models for fibrosis were less reliable when predictions were based on phosphoproteomic, clinical, or the combination data. The best performing model relied on levels of the phosphorylation of GSK3 as well as on two subunits of cyclic AMP regulated protein kinase A (PKA). Phosphoproteomics technology could potentially be used to provide pathogenic information about NASH and NASH-related fibrosis. This information can lead to a clinically relevant diagnostic/prognostic biomarker for NASH.

MeSH terms

  • Adipose Tissue / chemistry
  • Adipose Tissue / metabolism
  • Adult
  • Area Under Curve
  • Biomarkers / chemistry
  • Cohort Studies
  • Fatty Liver / diagnosis*
  • Fatty Liver / metabolism
  • Female
  • Histocytochemistry
  • Humans
  • Liver / chemistry
  • Liver / metabolism
  • Liver Cirrhosis / diagnosis*
  • Liver Cirrhosis / metabolism
  • Male
  • Middle Aged
  • Phosphoproteins / chemistry*
  • Phosphoproteins / metabolism
  • Predictive Value of Tests
  • Regression Analysis
  • Reproducibility of Results
  • Signal Transduction

Substances

  • Biomarkers
  • Phosphoproteins