Literature DB >> 17557906

A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by nongenotoxic chemicals.

Mark R Fielden1, Richard Brennan, Jeremy Gollub.   

Abstract

There are currently no accurate and well-validated short-term tests to identify nongenotoxic hepatic tumorigens, thus necessitating an expensive 2-year rodent bioassay before a risk assessment can begin. Using hepatic gene expression data from rats treated for 5 days with one of 100 structurally and mechanistically diverse nongenotoxic hepatocarcinogens and nonhepatocarcinogens, a novel multigenebiomarker (i.e., signature) was derived to predict the likelihood of nongenotoxic chemicals to induce liver tumors in longer term studies. Independent validation of the signature on 47 test chemicals indicates an assay sensitivity and specificity of 86% and 81%, respectively. Alternate short-term in vivo pathological and genomic biomarkers were evaluated in parallel for comparison, including liver weight, hepatocellular hypertrophy, hepatic necrosis, serum alanine aminotransferase activity, induction of cytochrome P450 genes, and repression of Tsc-22 or alpha2-macroglobulin messenger RNA. In contrast to these biomarkers, the gene expression-based signature was more accurate. Unlike existing tests, an understanding of potential modes of action for hepatic tumorigenicity can be derived by comparison of the signature profile of test chemicals to hepatic tumorigens of known mechanism, including regenerative proliferation, proliferation associated with xenobiotic receptor activation, peroxisome proliferation, and steroid hormone-mediated mechanisms. This signature is not only more accurate than current methods, but also facilitates the identification of mode of action to aid in the early assessment of human cancer risk.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17557906     DOI: 10.1093/toxsci/kfm156

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  45 in total

1.  Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.

Authors:  Ivan Rusyn; Alexander Sedykh; Yen Low; Kathryn Z Guyton; Alexander Tropsha
Journal:  Toxicol Sci       Date:  2012-03-02       Impact factor: 4.849

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

Authors:  J Christopher Corton; Thomas Hill; Jeffrey J Sutherland; James L Stevens; John Rooney
Journal:  Toxicol Sci       Date:  2020-09-01       Impact factor: 4.849

3.  In vitro transcriptomic prediction of hepatotoxicity for early drug discovery.

Authors:  Feng Cheng; Dan Theodorescu; Ira G Schulman; Jae K Lee
Journal:  J Theor Biol       Date:  2011-08-27       Impact factor: 2.691

4.  Gene expression biomarkers provide sensitive indicators of in planta nitrogen status in maize.

Authors:  Xiaofeng S Yang; Jingrui Wu; Todd E Ziegler; Xiao Yang; Adel Zayed; M S Rajani; Dafeng Zhou; Amarjit S Basra; Daniel P Schachtman; Mingsheng Peng; Charles L Armstrong; Rico A Caldo; James A Morrell; Michelle Lacy; Jeffrey M Staub
Journal:  Plant Physiol       Date:  2011-10-06       Impact factor: 8.340

Review 5.  Systems biology and functional genomics approaches for the identification of cellular responses to drug toxicity.

Authors:  Alison Hege Harrill; Ivan Rusyn
Journal:  Expert Opin Drug Metab Toxicol       Date:  2008-11       Impact factor: 4.481

Review 6.  Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays.

Authors:  Yen Sia Low; Alexander Yeugenyevich Sedykh; Ivan Rusyn; Alexander Tropsha
Journal:  Curr Top Med Chem       Date:  2014       Impact factor: 3.295

7.  Resveratrol Improves Recovery and Survival of Diet-Induced Obese Mice Undergoing Extended Major (80%) Hepatectomy.

Authors:  Xiaoling Jin; Teresa A Zimmers; Zongxiu Zhang; Leonidas G Koniaris
Journal:  Dig Dis Sci       Date:  2018-10-03       Impact factor: 3.199

8.  Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.

Authors:  W Shi; M Bessarabova; D Dosymbekov; Z Dezso; T Nikolskaya; M Dudoladova; T Serebryiskaya; A Bugrim; A Guryanov; R J Brennan; R Shah; J Dopazo; M Chen; Y Deng; T Shi; G Jurman; C Furlanello; R S Thomas; J C Corton; W Tong; L Shi; Y Nikolsky
Journal:  Pharmacogenomics J       Date:  2010-08       Impact factor: 3.550

9.  A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data.

Authors:  J Luo; M Schumacher; A Scherer; D Sanoudou; D Megherbi; T Davison; T Shi; W Tong; L Shi; H Hong; C Zhao; F Elloumi; W Shi; R Thomas; S Lin; G Tillinghast; G Liu; Y Zhou; D Herman; Y Li; Y Deng; H Fang; P Bushel; M Woods; J Zhang
Journal:  Pharmacogenomics J       Date:  2010-08       Impact factor: 3.550

10.  Phenobarbital elicits unique, early changes in the expression of hepatic genes that affect critical pathways in tumor-prone B6C3F1 mice.

Authors:  Jennifer M Phillips; Lyle D Burgoon; Jay I Goodman
Journal:  Toxicol Sci       Date:  2009-03-06       Impact factor: 4.849

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.