Identification of molecular signatures predicting the carcinogenicity of polycyclic aromatic hydrocarbons (PAHs)

Assessing the potential carcinogenicity of human toxins represents an ongoing challenge. Chronic rodent bioassays predict human cancer risk with limited reliability, and are expensive and time-consuming. To identify alternative prediction methods, we evaluated a transcriptomics-based human in vitro model to classify carcinogens by their modes of action. The aim of this study was to determine the transcriptomic response and identify specific molecular signatures of polycyclic aromatic hydrocarbons (PAHs), which can be used as predictors of carcinogenicity of environmental toxins in human in vitro systems. We found that characteristic molecular signatures facilitate identification and prediction of carcinogens. To evaluate the change in gene expression levels, human hepatocellular carcinoma (HepG2) cells were exposed to nine different PAHs (benzo[a]pyrene, dibenzo[a,h]anthracene, 3-methylcholanthrene, naphthalene, chrysene, phenanthrene, benzo[a]anthracene, benzo[k]fluoranthene, and indeno[1,2,3-c,d]pyrene) for 48 h.Unsupervised gene expression analysis resulted in a characteristic molecular signature for each toxin, and a supervised analysis identified 31 outlier genes as distinct molecular signatures distinguishing carcinogens from noncarcinogens. Further analysis and multi-classification revealed 430 genes as surrogate markers for predicting carcinogenic potencies of each PAH with 100% accuracy. Our results suggest that these expression signatures can be used as predictable and discernible surrogate markers for detecting and predicting PAH exposure, and their carcinogenic potential. Furthermore, the use of these markers can be more widely applied in combination with traditional techniques for assessing and predicting toxic exposure to PAHs.

► We found that characteristic molecular signatures facilitate identification and prediction of carcinogens.
► 31 genes distinguished carcinogens from noncarcinogens, regardless of the toxin.
► Supervised analysis resulted in 430 genes that exactly annotated five different classes with 100% accuracy.