Imperial College London
Portrait Picture

Professor Martin Wilkins

Faculty of Medicine, National Heart & Lung Institute

Professor of Clinical Pharmacology
 
 
 
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Contact

 

+44 (0)20 3313 6101m.wilkins Website

 
 
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Assistant

 

Mrs Elizabeth O'Brien +44 (0)20 3313 6101

 
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Location

 

NIHR Imperial Clinical Research FacilityICTEM buildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Errington:2021:10.1016/j.ebiom.2021.103444,
author = {Errington, N and Iremonger, J and Pickworth, JA and Kariotis, S and Rhodes, CJ and Rothman, AM and Condliffe, R and Elliot, CA and Kiely, DG and Howard, LS and Wharton, J and Thompson, AAR and Morrell, NW and Wilkins, MR and Wang, D and Lawrie, A},
doi = {10.1016/j.ebiom.2021.103444},
journal = {EBioMedicine},
title = {A diagnostic miRNA signature for pulmonary arterial hypertension using a consensus machine learning approach},
url = {http://dx.doi.org/10.1016/j.ebiom.2021.103444},
volume = {69},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: Pulmonary arterial hypertension (PAH) is a rare but life shortening disease, the diagnosis of which is often delayed, and requires an invasive right heart catheterisation. Identifying diagnostic biomarkers may improve screening to identify patients at risk of PAH earlier and provide new insights into disease pathogenesis. MicroRNAs are small, non-coding molecules of RNA, previously shown to be dysregulated in PAH, and contribute to the disease process in animal models. METHODS: Plasma from 64 treatment naïve patients with PAH and 43 disease and healthy controls were profiled for microRNA expression by Agilent Microarray. Following quality control and normalisation, the cohort was split into training and validation sets. Four separate machine learning feature selection methods were applied to the training set, along with a univariate analysis. FINDINGS: 20 microRNAs were identified as putative biomarkers by consensus feature selection from all four methods. Two microRNAs (miR-636 and miR-187-5p) were selected by all methods and used to predict PAH diagnosis with high accuracy. Integrating microRNA expression profiles with their associated target mRNA revealed 61 differentially expressed genes verified in two independent, publicly available PAH lung tissue data sets. Two of seven potentially novel gene targets were validated as differentially expressed in vitro in human pulmonary artery smooth muscle cells. INTERPRETATION: This consensus of multiple machine learning approaches identified two miRNAs that were able to distinguish PAH from both disease and healthy controls. These circulating miRNA, and their target genes may provide insight into PAH pathogenesis and reveal novel regulators of disease and putative drug targets.
AU  - Errington,N
AU  - Iremonger,J
AU  - Pickworth,JA
AU  - Kariotis,S
AU  - Rhodes,CJ
AU  - Rothman,AM
AU  - Condliffe,R
AU  - Elliot,CA
AU  - Kiely,DG
AU  - Howard,LS
AU  - Wharton,J
AU  - Thompson,AAR
AU  - Morrell,NW
AU  - Wilkins,MR
AU  - Wang,D
AU  - Lawrie,A
DO  - 10.1016/j.ebiom.2021.103444
PY  - 2021///
SN  - 2352-3964
TI  - A diagnostic miRNA signature for pulmonary arterial hypertension using a consensus machine learning approach
T2  - EBioMedicine
UR  - http://dx.doi.org/10.1016/j.ebiom.2021.103444
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34186489
UR  - http://hdl.handle.net/10044/1/90356
VL  - 69
ER  -
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