Staging of biliary atresia at diagnosis by molecular profiling of the liver

Katie Moyer1, Vivek Kaimal, Cristina Pacheco

  • 1Division of Pediatric Gastroenterology, Hepatology and Nutrition of Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229, USA. katie.moyer@cchmc.org.

Genome Medicine
|May 15, 2010
PubMed

Insights

Molecular profiling of liver biopsies in infants with biliary atresia can identify distinct inflammation or fibrosis signatures. This may help stage the disease at diagnosis and predict clinical outcomes, including transplant-free survival.

Area of Science:

  • Hepatology
  • Molecular Biology
  • Pediatric Surgery

Background:

  • Biliary atresia (BA) outcome is linked to early portoenterostomy, but disease progression may be influenced by pre-existing biological factors.
  • Identifying early disease markers is crucial for managing BA progression.

Purpose of the Study:

  • To investigate if molecular profiling of liver tissue at diagnosis can identify distinct stages of biliary atresia.
  • To correlate molecular signatures with histological findings, disease severity, surgical response, and survival.

Main Methods:

  • Liver biopsies from 47 infants with BA were analyzed for histology (inflammation, fibrosis), gene expression, and association with clinical outcomes.
  • Gene expression profiling identified unique molecular signatures in livers with predominant inflammation or fibrosis.

Main Results:

  • Fourteen of 47 BA livers showed distinct inflammation (N=9) or fibrosis (N=5) signatures.
  • Molecular signatures classified 29/33 additional livers into inflammation or fibrosis groups, validated by histological markers.
  • Inflammation signature was associated with younger age; fibrosis signature correlated with decreased transplant-free survival.

Conclusions:

  • Molecular profiling at BA diagnosis reveals distinct inflammation or fibrosis signatures in most livers.
  • These signatures may represent disease staging at diagnosis and have implications for predicting clinical outcomes.
Abstract

Related Concept Videos