Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.
Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Beech Hill Road, Sheffield, UK.
EBioMedicine. 2021 Jul;69:103444. doi: 10.1016/j.ebiom.2021.103444. Epub 2021 Jun 26.
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.
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.
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.
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.
This work was supported by a National Institute for Health Research Rare Disease Translational Research Collaboration (R29065/CN500) and British Heart Foundation Project Grant (PG/11/116/29288).
肺动脉高压（PAH）是一种罕见但危及生命的疾病，其诊断常常被延误，需要进行有创的右心导管检查。确定诊断生物标志物可能有助于更早地筛选出患有 PAH 的风险患者，并为疾病发病机制提供新的见解。微小 RNA 是一种小的非编码 RNA 分子，先前已显示在 PAH 中失调，并在动物模型中为疾病过程做出贡献。
通过 Agilent 微阵列对 64 名未经治疗的 PAH 患者和 43 名疾病和健康对照者的血浆进行 microRNA 表达谱分析。经过质量控制和归一化后，将队列分为训练集和验证集。将四种独立的机器学习特征选择方法应用于训练集，并进行单变量分析。
通过所有四种方法的共识特征选择，确定了 20 个 microRNA 作为潜在的生物标志物。两种 microRNA（miR-636 和 miR-187-5p）被所有方法选中，可准确预测 PAH 诊断。将 microRNA 表达谱与其相关的靶 mRNA 整合在一起，揭示了两个独立的、可公开获得的 PAH 肺组织数据集中验证的 61 个差异表达基因。在体外，7 个潜在新基因靶标中的 2 个在人肺动脉平滑肌细胞中被证实差异表达。
这一致同意采用多种机器学习方法，确定了两种能够区分 PAH 与疾病和健康对照者的 microRNA。这些循环的 microRNA 及其靶基因可能为 PAH 发病机制提供新的认识，并揭示疾病的新调节因子和潜在的药物靶点。
这项工作得到了国家卫生研究院罕见病转化研究合作研究项目（R29065/CN500）和英国心脏基金会项目资助（PG/11/116/29288）的支持。
