School of Pharmacy
Our group recently employed genome-wide transcriptional profiling in tandem with machine-learning based analysis to identify a ten-gene pattern of differential expression in peripheral blood which may have utility for detection of stroke. The objective of this study was to assess the diagnostic capacity and temporal stability of this stroke-associated transcriptional signature in an independent patient population. Publicly available whole blood microarray data generated from 23 ischemic stroke patients at 3, 5, and 24 h post-symptom onset, as well from 23 cardiovascular disease controls, were obtained via the National Center for Biotechnology Information Gene Expression Omnibus. Expression levels of the ten candidate genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B, and PLXDC2) were extracted, compared between groups, and evaluated for their dis- criminatory ability at each time point. We observed a largely identical pattern of differential expression between stroke patients and controls across the ten candidate genes as reported in our prior work. Furthermore, the coordinate expression levels of the ten candidate genes were able to discriminate between stroke patients and controls with levels of sensitivity and specificity upwards of 90% across all three time points. These findings confirm the diagnostic robustness of the previously identified pattern of differential expression in an in- dependent patient population, and further suggest that it is temporally stable over the first 24 h of stroke pa- thology.
Digital Commons Citation
O'Connell, Grant C.; Chantler, Paul D.; and Barr, Taura L., "Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population" (2017). Faculty & Staff Scholarship. 1290.
O’Connell, G. C., Chantler, P. D., & Barr, T. L. (2017). Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population. Genomics Data, 14, 47–52. https://doi.org/10.1016/j.gdata.2017.08.006