Abstract
BackgroundPulmonary 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.MethodsPlasma 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.Findings20 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.InterpretationThis 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.
Highlights
Pulmonary arterial hypertension (PAH) is a rare but progressive cardiopulmonary disease characterised by increased pulmonary1 these authors contributed 2 these authors share senior authorship vascular resistance driven by a sustained pulmonary arterial vasoconstriction and pulmonary vascular remodelling that leads to right heart failure and premature death
PAH can be further sub-categorised into seven sub-groups: Idiopathic PAH (IPAH), heritable PAH (HPAH), drug and toxin induced, PAH associated with other associated diseases, PAH long term responders to calcium channel blockers, PAH with overt features of venous/
Our findings extend preliminary evidence that microRNAs may be able to classify PAH patients from controls, and suggest that a machine learning approach may allow for the detection of novel disease regulators
Summary
Pulmonary arterial hypertension (PAH) is a rare but progressive cardiopulmonary disease characterised by increased pulmonary1 these authors contributed 2 these authors share senior authorship vascular resistance driven by a sustained pulmonary arterial vasoconstriction and pulmonary vascular remodelling that leads to right heart failure and premature death. We hypothesised applying machine learning to microRNAs in PAH may provide novel insights. 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. Methods: Plasma from 64 treatment naïve patients with PAH and 43 disease and healthy controls were profiled for microRNA expression by Agilent Microarray. 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.
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