Unknown

Dataset Information

0

Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.


ABSTRACT: Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via microarray. A machine-learning technique known as genetic algorithm k-nearest neighbours (GA/kNN) was then used to identify a pattern of gene expression that could optimally discriminate between groups. This pattern of expression was then assessed via qRT-PCR in an independent validation cohort, where it was evaluated for its ability to discriminate between an additional 39 AIS patients and 30 neurologically asymptomatic controls, as well as 20 acute stroke mimics. GA/kNN identified 10 genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B and PLXDC2) whose coordinate pattern of expression was able to identify 98.4% of discovery cohort subjects correctly (97.4% sensitive, 100% specific). In the validation cohort, the expression levels of the same 10 genes were able to identify 95.6% of subjects correctly when comparing AIS patients to asymptomatic controls (92.3% sensitive, 100% specific), and 94.9% of subjects correctly when comparing AIS patients with stroke mimics (97.4% sensitive, 90.0% specific). The transcriptional pattern identified in this study shows strong diagnostic potential, and warrants further evaluation to determine its true clinical efficacy.

SUBMITTER: O'Connell GC 

PROVIDER: S-EPMC5685316 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

altmetric image

Publications

Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke.

O'Connell Grant C GC   Petrone Ashley B AB   Treadway Madison B MB   Tennant Connie S CS   Lucke-Wold Noelle N   Chantler Paul D PD   Barr Taura L TL  

NPJ genomic medicine 20161130


Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via  ...[more]

Similar Datasets

| S-EPMC9373395 | biostudies-literature
| S-EPMC7806470 | biostudies-literature
| S-EPMC7584611 | biostudies-literature
| S-EPMC5413803 | biostudies-literature
| S-EPMC10676587 | biostudies-literature
| S-EPMC10565242 | biostudies-literature
| S-EPMC10569207 | biostudies-literature
| S-EPMC9463529 | biostudies-literature
2022-05-20 | GSE203423 | GEO
| S-EPMC7398186 | biostudies-literature