Evaluation of database-derived pathway development for enabling biomarker discovery for hepatotoxicity

Dennie G. A. Hebels*, Marlon J. A. Jetten, Hugo J. W. Aerts, Ralf Herwig, Daniël H.J. Theunissen, Stan Gaj, Joost H. van Delft, Jos C. S. Kleinjans

*Corresponding author for this work

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Abstract

Current testing models for predicting drug-induced liver injury are inadequate, as they basically under-report human health risks. We present here an approach towards developing pathways based on hepatotoxicity-associated gene groups derived from two types of publicly accessible hepatotoxicity databases, in order to develop drug-induced liver injury biomarker profiles. One human liver omics-based and four text-mining-based databases were explored for hepatotoxicity-associated gene lists. Over-representation analysis of these gene lists with a hepatotoxicant-exposed primary human hepatocytes data set showed that human liver omics gene lists performed better than text-mining gene lists and the results of the latter differed strongly between databases. However, both types of databases contained gene lists demonstrating biomarker potential. Visualizing those in pathway format may aid in interpreting the biomolecular background. We conclude that exploiting existing and openly accessible databases in a dedicated manner seems promising in providing venues for translational research in toxicology and biomarker development.
Original languageEnglish
Pages (from-to)185-200
JournalBiomarkers in Medicine
Volume8
Issue number2
DOIs
Publication statusPublished - Feb 2014

Keywords

  • "omics databases
  • biomarker
  • DILI
  • diXa
  • drug-induced liver injury
  • hepatotoxicity
  • over-representation analysis
  • pathway development
  • text-mining databases
  • toxicogenomics

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