OBJECTIVE Gene expression (transcriptomics) studies have revealed potential mechanisms of interstitial lung disease (ILD), yet sample sizes of studies are often limited and between-subtype comparisons are scarce. The aim of this study was to identify and validate consensus transcriptomic signatures of ILD subtypes. METHODS We performed a systematic review and meta-analysis of fibrotic ILD transcriptomics studies using an individual participant data approach, and included studies examining bulk transcriptomics of human adult ILD samples and excluding those focusing on individual cell populations. Patient-level data and expression matrices were extracted from 43 studies and integrated using a multivariable integrative algorithm to develop ILD classification models. RESULTS Using 1459 samples from 24 studies, we identified transcriptomic signatures for idiopathic pulmonary fibrosis (IPF), hypersensitivity pneumonitis (HP), idiopathic nonspecific interstitial pneumonia (NSIP), and systemic sclerosis-associated ILD (SSc-ILD) against control samples, which were validated on 308 samples from 8 studies (area under receiver operating curve [AUC]=0.99 [95% CI: 0.99-1.00], HP AUC=0.91 [0.84-0.99], NSIP AUC=0.94 [0.88-0.99], SSc-ILD AUC=0.98 [0.93-1.00]). Significantly, meta-analysis allowed, for the first time, identification of robust lung transcriptomics signatures to discriminate IPF (AUC=0.71 [0.63-0.79]) and HP (AUC=0.76 [0.63-0.89]) from other fibrotic ILDs, and unsupervised learning algorithms identified putative molecular endotypes of ILD associated with decreased forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO) % predicted. Transcriptomics signatures were reflective of both cell-specific and disease-specific changes in gene expression. CONCLUSION We present the first systematic review and largest meta-analysis of fibrotic ILD transcriptomics to date, identifying reproducible transcriptomic signatures with clinical relevance.

中文翻译：

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间质性肺病的转录组学：系统评价和荟萃分析。


目的 基因表达（转录组学）研究揭示了间质性肺病 （ILD） 的潜在机制，但研究的样本量通常有限，并且亚型间比较很少。本研究的目的是识别和验证 ILD 亚型的共有转录组特征。方法 我们使用个体参与者数据方法对纤维化 ILD 转录组学研究进行了系统评价和荟萃分析，并纳入了检查人类成人 ILD 样本的大量转录组学的研究，并排除了那些专注于个体细胞群的研究。从 43 项研究中提取患者水平的数据和表达矩阵，并使用多变量综合算法进行整合以开发 ILD 分类模型。结果 使用来自 24 项研究的 1459 个样本，我们确定了特发性肺纤维化 （IPF） 、过敏性肺炎 （HP）、特发性非特异性间质性肺炎 （NSIP） 和系统性硬化症相关 ILD （SSc-ILD） 与对照样本的转录组特征，这些特征在来自 8 项研究的 308 个样本中进行了验证 （受试者工作曲线下面积 [AUC]=0.99 [95% CI： 0.99-1.00]，HP AUC=0.91 [0.84-0.99]，NSIP AUC=0.94 [0.88-0.99]，这些特征在来自 8 项研究的 308 个样本中得到了验证 （受试者工作曲线下面积 [AUC]=0.99 [95% CI： 0.99-1.00]，HP AUC=0.91 [0.84-0.99]，NSIP AUC=0.94 [0.88-0.99]， SSc-ILD AUC=0.98 [0.93-1.00]）。值得注意的是，荟萃分析首次允许识别稳健的肺转录组学特征，以区分 IPF （AUC=0.71 [0.63-0.79]）和 HP （AUC=0.76 [0.63-0.89]）与其他纤维化 ILD，无监督学习算法确定了与用力肺活量 （FVC） 和肺一氧化碳弥散量 （DLCO） % 预测相关的推定 ILD 分子内型。 转录组学特征反映了基因表达的细胞特异性和疾病特异性变化。结论 我们提出了迄今为止纤维化 ILD 转录组学的首个系统评价和最大的荟萃分析，确定了具有临床相关性的可重复转录组学特征。