血浆蛋白质组学和机器学习实现纤维化性过敏性肺炎与特发性肺纤维化的无创区分
Plasma proteomics and machine learning deliver non-invasive distinction between fibrotic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis.
摘要(中文译文)
过敏性肺炎（HP）表现为纤维化型（FHP）和非纤维化型（NFHP）两种表型。尽管治疗方案不同，但由于表型重叠，临床上区分FHP与特发性肺纤维化（IPF）仍具有挑战性。本研究旨在建立基于血浆蛋白质组学的框架，用于上述疾病之间的鉴别诊断。研究于2018年7月至2022年6月从中国间质性肺疾病（ILD）国家队列和PORTRAY IPF队列共纳入119名受试者，包括32名健康对照（HC）、31名NFHP患者、28名FHP患者和28名IPF患者。对血浆样本进行定量蛋白质组学分析、加权基因共表达网络分析（WGCNA）和生物信息学分析，以识别差异表达蛋白、核心通路和共表达模块。筛选关键蛋白，通过七种机器学习算法构建并验证诊断模型。本研究描绘了FHP和IPF的血浆蛋白质组学图谱，共鉴定出813种蛋白。WGCNA显示糖酵解/糖异生和丙酮酸代谢通路显著富集，提示代谢重编程参与FHP的发病机制。差异分析鉴定出9种差异表达蛋白，并通过LASSO回归和递归特征消除从中衍生出一个六蛋白特征标签（H2BC12、SHBG、APCS、PTPRG、IGHV1-58和GAPDH）。在七种机器学习算法中，支持向量机（SVM）在独立测试集上表现最佳，准确率为71.4%，能够有效区分FHP与IPF。该模型有望成为一种用于非典型间质性肺疾病鉴别诊断的非侵入性分子工具。本研究建立了FHP的血浆蛋白质组学图谱，将代谢重编程与疾病发病机制相联系，并为生物标志物引导的鉴别诊断提供了机器学习框架。
Abstract
Hypersensitivity pneumonitis (HP) manifests as fibrotic (FHP) and non-fibrotic (NFHP) phenotypes. Clinically distinguishing FHP from idiopathic pulmonary fibrosis (IPF) remains challenging owing to phenotypic overlap, despite divergent management protocols. This investigation sought to develop a plasma proteomics-based framework for differential diagnosis between these entities. A total of 119 subjects were enrolled from the Chinese Interstitial Lung Disease (ILD) National Cohort and the PORTRAY IPF Cohort between July 2018 and June 2022, comprising 32 healthy controls (HCs), 31 NFHPs, 28 FHPs, and 28 IPF patients. The plasma samples were subject to quantitative proteomic profiling, weighted gene co-expression network analysis (WGCNA), and bioinformatics analysis to identify differentially expressed proteins, core pathways, and co-expression modules. Key proteins were selected to construct and validate diagnostic models via seven machine learning algorithms. This study delineated the plasma proteomic landscape of FHP and IPF, identifying 813 proteins. WGCNA revealed significant enrichment of the glycolysis/gluconeogenesis and pyruvate metabolism pathways, implicating metabolic reprogramming in FHP pathogenesis. Differential analysis identified nine differentially expressed proteins, from which a six-protein signature (H2BC12, SHBG, APCS, PTPRG, IGHV1-58, and GAPDH) was derived through LASSO regression and recursive feature elimination. Among seven machine learning algorithms, support vector machine (SVM) achieved the optimal performance on the independent test set with an accuracy of 71.4%, effectively discriminating FHPs from IPFs. This model represents a promising non-invasive molecular tool for the differential diagnosis of atypical interstitial lung diseases. This study established the plasma proteomic landscape of FHP, linking metabolic reprogramming to disease pathogenesis and providing a machine learning framework for biomarker-guided differential diagnosis.
如何引用
AMA
Yizhuo Tian, Jing Geng, Mingyao Wang, Wenfeng Zhu, Jiyu Xu, Xiaoyue Tang, et al. Plasma proteomics and machine learning deliver non-invasive distinction between fibrotic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis.. Journal of translational medicine. 2026; doi:10.1186/s12967-026-08643-8.
APA
Yizhuo Tian, Jing Geng, Mingyao Wang, Wenfeng Zhu, Jiyu Xu, Xiaoyue Tang, et al (2026). Plasma proteomics and machine learning deliver non-invasive distinction between fibrotic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis.. Journal of translational medicine. https://doi.org/10.1186/s12967-026-08643-8
GB/T 7714
Yizhuo Tian, Jing Geng, Mingyao Wang, Wenfeng Zhu, Jiyu Xu, Xiaoyue Tang, et al. Plasma proteomics and machine learning deliver non-invasive distinction between fibrotic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis.[J]. Journal of translational medicine, 2026 doi:10.1186/s12967-026-08643-8.
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