A 20-Gene Expression Diagnostic Signature of Bovine Respiratory Disease in Cattle
Bovine Respiratory Disease (BRD) is a prevalent disease in cattle rearing systems globally with significant health and economic costs. Current diagnostic methods of BRD rely on subjective visual signs and physical examination, which are suboptimal. This study, therefore, aims to find a blood-based gene expression signature for the diagnostic identification of BRD in cattle. The Gene Expression Omnibus dataset, GSE152959, was downloaded and used for analysis. The analyses performed included differential gene expression (DGE), clustering and machine learning prediction. Ninety genes were differentially expressed in BRD samples compared to controls. The GSE150706 dataset was used as the test dataset for machine learning prediction. The DEGs identified clustered the GSE150706 samples with good accuracy. For the machine learning prediction, 92 % of correctly predicted samples were obtained using twenty genes as features. Therefore, the identified 20-gene expression signature has BRD diagnostic utility in cattle. This signature could potentially be used to develop standardized and reliable diagnostic tests of Bovine Respiratory Disease in cattle. Improved diagnostics will lead to early detection and treatment, reducing the health and economic costs associated with the disease. Further validation in larger cattle cohorts is required.
How to Cite
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
© Journal of Scientific Research
Articles published in the "Journal of Scientific Research" are Open Access articles under a Creative Commons Attribution-ShareAlike 4.0 International license (CC BY-SA 4.0). This license permits use, distribution and reproduction in any medium, provided the original work is properly cited and initial publication in this journal. In addition to that, users must provide a link to the license, indicate if changes are made and distribute using the same license as original if the original content has been remixed, transformed or built upon.