Transcriptomics of interstitial lung disease: a systematic review and meta-analysis.

The aim of this study was to identify and validate consensus transcriptomic signatures of interstitial lung disease subtypes.

本研究的目的是识别并验证间质性肺疾病亚型的共识转录组学特征。

Methods

We performed a systematic reviewand meta-analysisof fibrotic interstitial lung disease transcriptomics studies using an individual participant data approach.

我们对纤维化间质性肺病转录组学研究进行了系统综述和荟萃分析，采用的是个体参与者数据方法。

We included studies examining bulk transcriptomics of human adult interstitial lung disease samples and excluded those focusing on individual cell populations.

我们纳入了研究人类成人间质性肺病样本的批量转录组学研究，并排除了那些专注于个别细胞群体的研究。

Patient-level data and expression matrices were extracted from 43 studies and integrated using a multivariable integrative algorithm to develop interstitial lung disease classification models.

从43项研究中提取了患者级别的数据和表达矩阵，并使用多变量综合算法进行整合，以开发间质性肺病的分类模型。

Results

Using 1459 samples from 24 studies, we identified transcriptomic signatures for idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, idiopathic nonspecific interstitial pneumonia and systemic sclerosis-associated interstitial lung disease against control samples, which were validated on 308 samples from eight studies (idiopathic pulmonary fibrosis area under receiver operating curve (AUC) 0.99, 95% CI 0.99-1.00; hypersensitivity pneumonitis AUC 0.91, 95% CI 0.84-0.99; nonspecific interstitial pneumonia AUC 0.94, 95% CI 0.88-0.99; systemic sclerosis-associated interstitial lung disease AUC 0.98, 95% CI 0.93-1.00).

我们使用来自24项研究的1459个样本，识别了特发性肺纤维化、过敏性肺炎、特发性非特异性间质性肺炎和系统性硬化症相关间质性肺病相对于对照样本的转录组学特征，这些特征在来自八项研究的308个样本上进行了验证（特发性肺纤维化受试者工作特征曲线下面积（AUC）为0.99，95%置信区间为0.99-1.00；过敏性肺炎AUC为0.91，95%置信区间为0.84-0.99；非特异性间质性肺炎AUC为0.94，95%置信区间为0.88-0.99；系统性硬化症相关间质性肺病AUC为0.98，95%置信区间为0.93-1.00）。

Significantly, meta-analysisallowed us to identify, for the first time, robust lung transcriptomics signatures to discriminate idiopathic pulmonary fibrosis (AUC 0.71, 95% CI 0.63-0.79) and hypersensitivity pneumonitis (AUC 0.76, 95% CI 0.63-0.89) from other fibrotic interstitial lung disease, and unsupervised learning algorithms identified putative molecular endotypes of interstitial lung disease associated with decreased forced vital capacity and diffusing capacity of the lungs for carbon monoxide % predicted.

显著的是，元分析首次使我们能够识别出稳健的肺部转录组学特征，以区分特发性肺纤维化（AUC 0.71，95% CI 0.63-0.79）和过敏性肺炎（AUC 0.76，95% CI 0.63-0.89）与其他纤维化间质性肺病，无监督学习算法识别了与降低的用力肺活量和一氧化碳弥散能力预测值相关的间质性肺病的潜在分子表型。

Transcriptomics signatures were reflective of both cell-specific and disease-specific changes in gene expression.

转录组学特征反映了细胞特异性以及疾病特异性基因表达的变化。

Conclusions

We present the first systematic reviewand largest meta-analysisof fibrotic interstitial lung disease transcriptomics to date, identifying reproducible transcriptomic signatures with clinical relevance.

我们呈现了迄今为止首个系统综述和最大的纤维化间质性肺疾病转录组学荟萃分析，确定了具有临床相关性的可重复转录组学特征。