Background Considerable clinical heterogeneity in idiopathic pulmonary fibrosis (IPF) suggests the existence of multiple disease endotypes. Identifying these endotypes would improve our understanding of the pathogenesis of IPF and could allow for a biomarker-driven personalised medicine approach. We aimed to identify clinically distinct groups of patients with IPF that could represent distinct disease endotypes. Methods We co-normalised, pooled and clustered three publicly available blood transcriptomic datasets (total 220 IPF cases). We compared clinical traits across clusters and used gene enrichment analysis to identify biological pathways and processes that were over-represented among the genes that were differentially expressed across clusters. A gene-based classifier was developed and validated using three additional independent datasets (total 194 IPF cases). Findings We identified three clusters of patients with IPF with statistically significant differences in lung function (p=0.009) and mortality (p=0.009) between groups. Gene enrichment analysis implicated mitochondrial homeostasis, apoptosis, cell cycle and innate and adaptive immunity in the pathogenesis underlying these groups. We developed and validated a 13-gene cluster classifier that predicted mortality in IPF (high-risk clusters vs low-risk cluster: HR 4.25, 95% CI 2.14 to 8.46, p=3.7×10−5). Interpretation We have identified blood gene expression signatures capable of discerning groups of patients with IPF with significant differences in survival. These clusters could be representative of distinct pathophysiological states, which would support the theory of multiple endotypes of IPF. Although more work must be done to confirm the existence of these endotypes, our classifier could be a useful tool in patient stratification and outcome prediction in IPF. Data are available in a public, open access repository and available on reasonable request. All gene expression data used in this study are freely available on the Gene Expression Omnibus (). Additional clinical data for some participants were obtained directly from the study authors and are available on reasonable request.

中文翻译：

---

转录组数据集的聚类分析以确定特发性肺纤维化的内型

背景 特发性肺纤维化（IPF）相当大的临床异质性表明存在多种疾病内型。识别这些内型将提高我们对 IPF 发病机制的理解，并可能允许采用生物标志物驱动的个性化医疗方法。我们的目的是确定临床上不同的 IPF 患者群体，这些患者可以代表不同的疾病内型。方法 我们对三个公开的血液转录组数据集（总共 220 个 IPF 病例）进行共同归一化、汇集和聚类。我们比较了跨簇的临床特征，并使用基因富集分析来识别跨簇差异表达的基因中过度表达的生物途径和过程。使用另外三个独立数据集（总共 194 个 IPF 病例）开发并验证了基于基因的分类器。结果 我们确定了三组 IPF 患者，组间肺功能 (p=0.009) 和死亡率 (p=0.009) 存在统计学显着差异。基因富集分析表明线粒体稳态、细胞凋亡、细胞周期以及先天性和适应性免疫与这些群体的发病机制有关。我们开发并验证了一个 13 基因簇分类器，可预测 IPF 死亡率（高风险簇与低风险簇：HR 4.25，95% CI 2.14 至 8.46，p=3.7×10−5）。解释 我们已经确定了血液基因表达特征，能够区分存活率存在显着差异的 IPF 患者群体。这些簇可以代表不同的病理生理状态，这将支持 IPF 多种内型的理论。尽管还需要做更多的工作来确认这些内型的存在，但我们的分类器可能成为 IPF 患者分层和结果预测的有用工具。数据可在公共、开放访问存储库中获取，并可根据合理请求提供。本研究中使用的所有基因表达数据均可在基因表达综合库（Gene Expression Omnibus）上免费获取（）。一些参与者的额外临床数据直接从研究作者处获得，并可根据合理要求提供。