Regulation of Peroxisome Proliferator-Activated Receptor-α mRNA in Rat Liver

1996 ◽  
Vol 326 (2) ◽  
pp. 281-289 ◽  
Author(s):  
Paul F. Sterchele ◽  
Hong Sun ◽  
Richard E. Peterson ◽  
John P. Vanden Heuvel
Keyword(s):  
Rat Liver ◽  
Peroxisome Proliferator ◽  
Peroxisome Proliferator Activated Receptor

scholarly journals A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays

2020 ◽  
Vol 177 (1) ◽  
pp. 11-26 ◽  
Author(s):  
J Christopher Corton ◽  
Thomas Hill ◽  
Jeffrey J Sutherland ◽  
James L Stevens ◽  
John Rooney
Keyword(s):  
Gene Expression ◽  
Liver Cancer ◽  
Rat Liver ◽  
Adverse Outcome ◽  
Peroxisome Proliferator ◽  
Short Term ◽  
Chronic Injury ◽  
Peroxisome Proliferator Activated Receptor ◽  
Aryl Hydrocarbon ◽  
Adverse Outcome Pathways

Abstract Chemical-induced liver cancer occurs in rodents through well-characterized adverse outcome pathways. We hypothesized that measurement of the 6 most common molecular initiating events (MIEs) in liver cancer adverse outcome pathways in short-term assays using only gene expression will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in 2-year bioassays. We tested this hypothesis using transcript data from a rat liver microarray compendium consisting of 2013 comparisons of 146 chemicals administered at doses with previously established effects on rat liver tumor induction. Five MIEs were measured using previously characterized gene expression biomarkers composed of gene sets predictive for genotoxicity and activation of 1 or more xenobiotic receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Because chronic injury can be important in tumorigenesis, we also developed a biomarker for cytotoxicity that had a 96% balanced accuracy. Characterization of the genes in each biomarker set using the unsupervised TXG-MAP network model demonstrated that the genes were associated with distinct functional coexpression modules. Using the Toxicological Priority Index to rank chemicals based on their ability to activate the MIEs showed that chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Balanced accuracies using thresholds derived from either TG-GATES or DrugMatrix data sets to predict tumorigenicity in independent sets of chemicals were up to 93%. These results show that a MIE-directed approach using only gene expression biomarkers could be used in short-term assays to identify chemicals and their doses that cause tumors.

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