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.

A Genome Wide Association Study on plasma FV levels identified PLXDC2 as a new modifier of the coagulation process

“…In conclusion, all these observations point out the existence of a new player in the coagulation cascade whose exact molecular contribution needs to be extensively investigated. Its impact on FV‐mediated coagulation related disorders also warrants further investigations as whole blood PLXDC2 expression levels have been reported to be associated with stroke …”

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

“…Our group recently employed high-throughput transcriptomics in combination with a machine learning technique known at GA/kNN to identify a ten gene pattern of differential expression in peripheral blood which has potential utility for the detection of stroke. 7 However, patients in this discovery investigation were almost exclusively Caucasian, groups were not well matched in terms of CVD risk factors, and blood was only sampled at a single time point post-symptom onset. In the study reported here, we leveraged a publically available microarray dataset to evaluate the previously identified candidate pattern of gene expression at multiple pathological time points in a more ethnically diverse subject pool which was better matched in terms of CVD risk factors.…”

“…3,6 Our group recently employed high-throughput transcriptomics in combination with a machine learning technique known as genetic algorithm/k-nearest neighbors (GA/kNN) to identify a panel of ten candidate genes whose peripheral blood expression levels were able to differentiate between 78 ischemic stroke patients and 74 control subjects with a high degree of accuracy. 7 These candidate genes include seven whose expression levels were elevated in stroke patients relative to controls (CD163, ANTXR2, PDK4, PLXDC2, STK3, ID3, CTSZ, KIF1B), and three whose expression levels were down regulated (MAL, ID3, GRAP); their coordinate pattern of differential expression was able to discriminate between groups with levels of sensitivity and specificity approaching 100%. While the levels of diagnostic performance observed in this discovery investigation were unprecedented, limitations in study design necessitate further evaluation of the candidate genes in a validation analysis before definitive conclusions can be made regarding their true diagnostic efficacy.…”

High-Throughput Profiling of Circulating Antibody Signatures for Stroke Diagnosis Using Small Volumes of Whole Blood

“…The peripheral immune system plays a central role in stroke pathology; not only is there a rapid systemic inflammatory response to the acute injury [4], but emerging evidence suggests that peripheral immune changes may precede symptom onset and in some cases trigger the acute event itself [5]. Recent studies by our group and others have demonstrated that this phenomenon can be targeted diagnostically; genome-wide transcriptomic profiling of circulating immune cells has identified RNA-based biomarker panels with the ability to accurately identify ischemic stroke and predict various post-stroke complications [6][7][8][9][10]. However, due to technical limitations, RNA-based biomarkers have limited utility in prehospital and emergency care settings; viable methods for accurate measurement of nucleic acids at the point-of-care are lacking, as existing platforms for rapid blood biomarker screening are largely geared towards immunoassay-based protein detection.…”