Abstract
BackgroundEvaluating the toxicity of chemical mixture and their possible mechanism of action is still a challenge for humans and other organisms. Microarray classifier analysis has shown promise in the toxicogenomic area by identifying biomarkers to predict unknown samples. Our study focuses on identifying gene markers with better sensitivity and specificity, building predictive models to distinguish metals from non-metal toxicants, and individual metal from one another, and furthermore helping understand underlying toxic mechanisms.ResultsBased on an independent dataset test, using only 15 gene markers, we were able to distinguish metals from non-metal toxicants with 100% accuracy. Of these, 6 and 9 genes were commonly down- and up-regulated respectively by most of the metals. 8 out of 15 genes belong to membrane protein coding genes. Function well annotated genes in the list include ADORA2B, ARNT, S100G, and DIO3. Also, a 10-gene marker list was identified that can discriminate an individual metal from one another with 100% accuracy. We could find a specific gene marker for each metal in the 10-gene marker list. Function well annotated genes in this list include GSTM2, HSD11B, AREG, and C8B.ConclusionsOur findings suggest that using a microarray classifier analysis, not only can we create diagnostic classifiers for predicting an exact metal contaminant from a large scale of contaminant pool with high prediction accuracy, but we can also identify valuable biomarkers to help understand the common and underlying toxic mechanisms induced by metals.
Highlights
Evaluating the toxicity of chemical mixture and their possible mechanism of action is still a challenge for humans and other organisms
The microarray classifier is analyzed by comparing different feature types, sizes, and two feature selection methods based on Library for support vector machines (LibSVM) classification algorithm [10]
Microarray classifiers analyze the prospects in the field of toxic genomics in identifying biomarkers to predict unknown samples and to help understand toxicity mechanisms [8, 11]
Summary
Evaluating the toxicity of chemical mixture and their possible mechanism of action is still a challenge for humans and other organisms. Microarray classifier analysis has shown promise in the toxicogenomic area by identifying biomarkers to predict unknown samples. Our study focuses on identifying gene markers with better sensitivity and specificity, building predictive models to distinguish metals from non-metal toxicants, and individual metal from one another, and helping understand underlying toxic mechanisms. Applications of new approaches, including massive sequencing techniques make toxic genomics strategies to classify hepatotoxic and non-hepatotoxic compounds and explore these molecular mechanism [4]. The primary culture method of hepatocytes provides an in vitro system that is convenient to do toxic chemicals screening. Microarray classifiers analyze the prospects in the field of toxic genomics in identifying biomarkers to predict unknown samples and to help understand toxicity mechanisms [8, 11]
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