Toxicogenomic markers for corticosteroid treatment in beef cattle: integrated analysis of transcriptomic data

In the present work, an integrated analysis was performed on DNA-microarray data of bovine muscle samples belonging to controls, animals treated with various growth promoters (GPs) and unknown commercial samples. The aim was identify a robust gene expression signature of corticosteroid treatment for the classification of commercial samples, despite the effects of biological variation and other confounding factors. DNA-Microarray data from 5 different batches of bovine skeletal muscle samples were analyzed (146 samples). After preprocessing, expression data from animals treated with corticosteroids and controls from the different batches (89 samples) were used to train a Support Vector Machines (SVMs) classifier. The optimal number of gene probes chosen by our classification framework was 73. The SVMs with linear kernel built on these 73 biomarker genes was predicted to perform on novel samples with a high classification accuracy (Matthew's correlation coefficient equal to 0.77) and an average percentage of false positive and false negative equal to 5% and 6%, respectively. Concluding, a relatively small set of genes was able to discriminate between controls and corticosteroid-treated animals, despite different breeds, animal ages, and combination of GPs. The results are extremely promising, suggesting that integrated analysis provides robust transcriptomic signatures for GP abuse.