title: Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens.
abstract: The assessment of non-genotoxic hepatocarcinogens (NGHCs) is currently relying on two-year rodent bioassays. Toxicogenomics biomarkers provide a potential alternative method for the prioritization of NGHCs that could be useful for risk assessment. However, previous studies using inconsistently classified chemicals as the training set and a single microarray dataset concluded no consensus biomarkers. In this study, 4 consensus biomarkers of A2m, Ca3, Cxcl1, and Cyp8b1 were identified from four large-scale microarray datasets of the one-day single maximum tolerated dose and a large set of chemicals without inconsistent classifications. Machine learning techniques were subsequently applied to develop prediction models for NGHCs. The final bagging decision tree models were constructed with an average AUC performance of 0.803 for an independent test. A set of 16 chemicals with controversial classifications were reclassified according to the consensus biomarkers. The developed prediction models and identified consensus biomarkers are expected to be potential alternative methods for prioritization of NGHCs for further experimental validation.
authors.affiliation: Ph.D. Program in Toxicology, Kaohsiung Medical University, Kaohsiung, Taiwan.
authors.forename: Shan-Han
authors.initials: SH
authors.lastname: Huang
authors.affiliation: Ph.D. Program in Toxicology, Kaohsiung Medical University, Kaohsiung, Taiwan.|School of Pharmacy, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan.|Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.|National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, Taiwan.
authors.forename: Chun-Wei
authors.initials: CW
authors.lastname: Tung
mesh_terms: D000818:Animals; D014408:Biomarkers, Tumor / Q000235:genetics*; D024403:Carbonic Anhydrase III / Q000235:genetics; D002273:Carcinogens / Q000633:toxicity*; D054360:Chemokine CXCL1 / Q000235:genetics; D016208:Databases, Factual; D008114:Liver Neoplasms, Experimental / Q000139:chemically induced / Q000235:genetics*; D000069550:Machine Learning; D012372:ROC Curve; D018570:Risk Assessment; D013253:Steroid 12-alpha-Hydroxylase / Q000235:genetics; D043922:Toxicogenetics / Q000379:methods*; D000511:alpha-Macroglobulins / Q000235:genetics
chemical_list: C578813:A2M protein, human; D014408:Biomarkers, Tumor; C475829:CXCL1 protein, human; D002273:Carcinogens; D054360:Chemokine CXCL1; D000511:alpha-Macroglobulins; D013253:Steroid 12-alpha-Hydroxylase; D024403:Carbonic Anhydrase III