A diagnostic miRNA signature for pulmonary arterial hypertension using a consensus machine learning approach

Abstract

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.

MicroRNA-325-3p Targets Human Epididymis Protein 4 to Relieve Right Ventricular Fibrosis in Rats with Pulmonary Arterial Hypertension

“…miR-214 antagonists have been reported to alleviate the phenotypic changes and proliferation of smooth muscle cells in vascular hyperproliferative diseases, including PAH [ 7 ]. In a consensus machine learning approach study, it was found that miR-636 and miR-187-5p could predict PAH diagnosis with high accuracy [ 8 ]. These studies suggest that miRNAs may be new therapeutic targets.…”

Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension

“…Supervised ML has also been exploited to detect circulating molecular signatures of PAH. For example, Errington et al 51 Finally, researchers used supervised ML to preemptively sniff out PAH from electronic health record data. 32 Training a gradient boosted decision trees algorithm on patient-level resource utilization data, a model capable of classifying individuals most likely to later develop idiopathic PAH was developed.…”

“…Supervised ML has also been exploited to detect circulating molecular signatures of PAH. For example, Errington et al 51 used an ensemble ML strategy (consensus predictions across random forest, regression partition trees, least absolute shrinkage and selection operator regression, and boosted decision trees algorithms) to uncover a microRNA signature of PAH. Network-based pathway analyses identified the corresponding miRNA gene targets, which accurately discriminated PAH cases in public transcriptomic data sets.…”

The Landscape of Noncoding RNA in Pulmonary Hypertension

“…In addition, studies have looked at the correlation between the expression profiles of dysregulated miRNAs and the disease severity or survival rate of pulmonary arterial hypertension (PAH) patients. Multiple promising miRNAs have been considered as biomarker of PAH disease that are being actively evaluated [ 16 , 17 ]. Aside from miRNAs, lncRNAs and circRNAs are other two major groups of ncRNAs relatively less investigated and understood in the PH field.…”