Guo Yali, Jin Qian, Kang Yi, Jin Wenwen, Liu Ying, Chen Qian, Liu Jian, Wang Yu Guang
Department of Respiratory Medicine, Beijing Hospital of Traditional Chinese Medicine, Affiliated to Capital Medical University, Beijing, China.
Beijing University of Chinese Medicine, Beijing, China.
PLoS One. 2025 Apr 24;20(4):e0320242. doi: 10.1371/journal.pone.0320242. eCollection 2025.
Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. This study aims to identify and screen potential biomarkers for IPF diagnosis, thereby providing new diagnostic approaches.
We choosed datasets from the Gene Expression Omnibus (GEO) database, including samples from both IPF patients and healthy controls. For the training set, we combined two gene array datasets (GSE24206 and GSE10667) and utilized GSE32537 as the test set. We identified differentially expressed genes (DEGs) between IPF and normal tissues and determined IPF-related modules using Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, we employed two machine learning strategies to screen potential diagnostic biomarkers. Candidate biomarkers were quantitatively evaluated using Receiver Operating Characteristic (ROC) curves to identify key diagnostic genes, followed by the construction of a nomogram. Further validation of the expression of these genes through transcriptomic sequencing data from IPF and normal group animal models. Next, we conducted immune infiltration analysis, single-gene Gene Set Enrichment Analysis (GSEA), and targeted drug prediction. Finally, we created an artificial neural network model specifically for IPF.
We identified ASPN, COMP, and GPX8 as candidate biomarker genes for IPF, all of which exhibited Area Under the Curve (AUC) above 0.90. These genes were validated by RT-qPCR. Immune infiltration analysis revealed that specific immune cell types are closely related to IPF, suggesting that these immune cells may play a significant role in the pathogenesis of IPF.
ASPN, COMP, and GPX8 have been identified as potential diagnostic genes for IPF, and the most relevant immune cell types have been determined. Our research results propose potential biomarkers for diagnosing IPF and present new pathways for investigating its pathogenesis and devising novel therapeutic approaches.
特发性肺纤维化（IPF）是一种具有致命结局的间质性肺疾病，以其快速进展和不可预测的临床病程而闻名。然而，可用于诊断和治疗IPF的工具相当有限。本研究旨在识别和筛选用于IPF诊断的潜在生物标志物，从而提供新的诊断方法。
我们从基因表达综合数据库（GEO）中选择数据集，包括IPF患者和健康对照的样本。对于训练集，我们合并了两个基因阵列数据集（GSE24206和GSE10667），并将GSE32537用作测试集。我们鉴定了IPF与正常组织之间的差异表达基因（DEG），并使用加权基因共表达网络分析（WGCNA）确定了IPF相关模块。随后，我们采用两种机器学习策略筛选潜在的诊断生物标志物。使用受试者工作特征（ROC）曲线对候选生物标志物进行定量评估，以识别关键诊断基因，随后构建列线图。通过IPF和正常组动物模型的转录组测序数据进一步验证这些基因的表达。接下来，我们进行了免疫浸润分析、单基因基因集富集分析（GSEA）和靶向药物预测。最后，我们创建了一个专门用于IPF的人工神经网络模型。
我们鉴定出ASPN、COMP和GPX8作为IPF的候选生物标志物基因，它们的曲线下面积（AUC）均高于0.90。这些基因通过RT-qPCR得到验证。免疫浸润分析表明，特定免疫细胞类型与IPF密切相关，这表明这些免疫细胞可能在IPF的发病机制中起重要作用。
ASPN、COMP和GPX8已被鉴定为IPF的潜在诊断基因，并且确定了最相关的免疫细胞类型。我们的研究结果提出了用于诊断IPF的潜在生物标志物，并为研究其发病机制和设计新的治疗方法提供了新途径。
