Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.
In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4022579?pdf=render |
| _version_ | 1811265895879671808 |
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| author | Michael Römer Johannes Eichner Ute Metzger Markus F Templin Simon Plummer Heidrun Ellinger-Ziegelbauer Andreas Zell |
| author_facet | Michael Römer Johannes Eichner Ute Metzger Markus F Templin Simon Plummer Heidrun Ellinger-Ziegelbauer Andreas Zell |
| author_sort | Michael Römer |
| collection | DOAJ |
| description | In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens. |
| first_indexed | 2024-04-12T20:32:12Z |
| format | Article |
| id | doaj.art-fbc0923c72014ff8b57a46859d6857ea |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T20:32:12Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-fbc0923c72014ff8b57a46859d6857ea2022-12-22T03:17:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0195e9764010.1371/journal.pone.0097640Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.Michael RömerJohannes EichnerUte MetzgerMarkus F TemplinSimon PlummerHeidrun Ellinger-ZiegelbauerAndreas ZellIn the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.http://europepmc.org/articles/PMC4022579?pdf=render |
| spellingShingle | Michael Römer Johannes Eichner Ute Metzger Markus F Templin Simon Plummer Heidrun Ellinger-Ziegelbauer Andreas Zell Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat. PLoS ONE |
| title | Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat. |
| title_full | Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat. |
| title_fullStr | Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat. |
| title_full_unstemmed | Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat. |
| title_short | Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat. |
| title_sort | cross platform toxicogenomics for the prediction of non genotoxic hepatocarcinogenesis in rat |
| url | http://europepmc.org/articles/PMC4022579?pdf=render |
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