Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population.

ABSTRACT: 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,

SUBMITTER: O'Connell GC 

PROVIDER: S-EPMC5596252 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature