Jin Qian, Kang Yi, Jin Wenwen, Liu Ying, Chen Qian, Liu Jian, Guo Yali, Wang Yuguang
Department of Respiratory Medicine, Beijing Hospital of Traditional Chinese Medicine, Affiliated to Capital Medical University, Beijing, China.
Beijing University of Chinese Medicine, Beijing, China.
Biol Direct. 2025 Jul 15;20(1):83. doi: 10.1186/s13062-025-00658-3.
Idiopathic pulmonary fibrosis (IPF) is a chronic progressive pulmonary disease characterized by alveolar structural destruction and fibrosis. In recent years, efferocytosis has been recognized as playing a crucial role in the occurrence and progression of IPF. This study aimed to identify and regulate key efferocytosis-related genes to elucidate their potential roles and clinical significance in IPF.
IPF-related datasets (GSE32537) were obtained from the Gene Expression Omnibus (GEO) database. Differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were applied to identify key genes associated with IPF, intersecting them with efferocytosis-related genes (ERGs) to obtain IPF-ERGs. Protein‒protein interaction (PPI) network construction and enrichment analysis were performed to elucidate the potential functions of these genes in IPF. Seven machine learning algorithms were employed to screen for hub genes with high diagnostic value. The GSE70866 dataset was used for validation, and a nomogram was constructed. Additionally, the CIBERSORT algorithm was used to analyze immune infiltration levels, and transcriptomic validation of the hub genes was conducted in animal experiments.
A total of 21 IPF-ERGs were identified, and machine learning further identified TLR2, ATG7, SPHK1, and ICAM1 as hub genes, which were significantly upregulated in the IPF group. Immune infiltration analysis revealed a significant increase in the infiltration levels of immune cell subsets, including memory B cells, CD8 + T cells, and resting dendritic cells, in the IPF group. Further clinical correlation analysis revealed a strong association between the expression levels of the hub genes and pulmonary function. A nomogram was constructed on the basis of the hub genes and validated for its potential clinical application. Consensus clustering classified IPF patients into two subtypes: C1, which was primarily by metabolic pathway activation, and C2, which was enriched in inflammatory and immune pathways. Transcriptomic analysis of animal experiments also confirmed the upregulation of hub gene expression in IPF.
This study identified TLR2, ATG7, SPHK1, and ICAM1 as four key hub genes, revealing their potential diagnostic value and biological functions in IPF. These genes may serve as potential diagnostic biomarkers and therapeutic targets, providing new insights for precision treatment.
Not applicable.
特发性肺纤维化（IPF）是一种以肺泡结构破坏和纤维化为特征的慢性进行性肺部疾病。近年来，胞葬作用被认为在IPF的发生和发展中起关键作用。本研究旨在识别和调控与胞葬作用相关的关键基因，以阐明它们在IPF中的潜在作用和临床意义。
从基因表达综合数据库（GEO）获取IPF相关数据集（GSE32537）。应用差异基因表达分析和加权基因共表达网络分析（WGCNA）来识别与IPF相关的关键基因，并将其与胞葬作用相关基因（ERGs）进行交叉分析，以获得IPF-ERGs。进行蛋白质-蛋白质相互作用（PPI）网络构建和富集分析，以阐明这些基因在IPF中的潜在功能。采用七种机器学习算法筛选具有高诊断价值的枢纽基因。使用GSE70866数据集进行验证，并构建列线图。此外，使用CIBERSORT算法分析免疫浸润水平，并在动物实验中对枢纽基因进行转录组验证。
共鉴定出21个IPF-ERGs，机器学习进一步将TLR2、ATG7、SPHK1和ICAM1鉴定为枢纽基因，它们在IPF组中显著上调。免疫浸润分析显示，IPF组中免疫细胞亚群的浸润水平显著增加，包括记忆B细胞、CD8+T细胞和静息树突状细胞。进一步的临床相关性分析显示，枢纽基因的表达水平与肺功能之间存在密切关联。基于枢纽基因构建了列线图，并对其潜在的临床应用进行了验证。共识聚类将IPF患者分为两个亚型：C1型主要通过代谢途径激活，C2型则富含炎症和免疫途径。动物实验的转录组分析也证实了IPF中枢纽基因表达的上调。
本研究将TLR2、ATG7、SPHK1和ICAM1鉴定为四个关键枢纽基因，揭示了它们在IPF中的潜在诊断价值和生物学功能。这些基因可能作为潜在的诊断生物标志物和治疗靶点，为精准治疗提供新的见解。
不适用。
