Liu Xingren, Song Junmei, Guo Shujin, Liao Yi, Zou Jun, Yang Liqing, Jiang Caiyu
Department of Respiratory and Critical Care Medicine, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cardiovascular Disease, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Orphanet J Rare Dis. 2025 Jul 10;20(1):354. doi: 10.1186/s13023-025-03876-0.
Emerging evidence suggests a potential relationship between lipid metabolism and idiopathic pulmonary fibrosis (IPF). This study aimed to identify lipid-related genes implicated in IPF pathogenesis.
Lipid-associated genes were retrieved from the GeneCards database and analyzed using unsupervised consensus clustering to classify IPF samples. Weighted gene co-expression network analysis (WGCNA) was performed on the identified clusters to determine core modules and hub genes associated with IPF. Machine learning algorithms were applied to these hub genes to construct diagnostic and prognostic models, which were validated across multiple datasets. Single-cell sequencing was used to investigate the distribution of potential pathogenic genes, and their functional roles were further validated through cellular experiments.
Two distinct clusters were identified, showing significant differences in lung function parameters and fibrosis-related gene expression. WGCNA revealed that the blue module was strongly associated with IPF and served as the core module. Genes from this module were used to construct diagnostic and prognostic models, which demonstrated strong predictive performance across multiple validation datasets. Single-cell sequencing revealed that KLF4 was highly expressed in lung epithelial cells. Functional assays indicated that knockdown of KLF4 did not affect the proliferation of human alveolar type II epithelial cells but significantly enhanced their migratory capacity, thereby promoting the fibrotic process.
This study successfully constructed lipid-related diagnostic and prognostic models for IPF and identified KLF4 as a potential causative gene. These findings provide a foundation for further exploration of lipid metabolism in IPF pathogenesis and potential therapeutic strategies targeting KLF4.
新出现的证据表明脂质代谢与特发性肺纤维化（IPF）之间存在潜在关系。本研究旨在确定与IPF发病机制相关的脂质相关基因。
从GeneCards数据库中检索脂质相关基因，并使用无监督一致性聚类进行分析，以对IPF样本进行分类。对识别出的聚类进行加权基因共表达网络分析（WGCNA），以确定与IPF相关的核心模块和枢纽基因。将机器学习算法应用于这些枢纽基因，以构建诊断和预后模型，并在多个数据集中进行验证。使用单细胞测序研究潜在致病基因的分布，并通过细胞实验进一步验证其功能作用。
识别出两个不同的聚类，它们在肺功能参数和纤维化相关基因表达方面存在显著差异。WGCNA显示蓝色模块与IPF密切相关，并作为核心模块。来自该模块的基因被用于构建诊断和预后模型，这些模型在多个验证数据集中表现出强大的预测性能。单细胞测序显示KLF4在肺上皮细胞中高表达。功能分析表明，敲低KLF4不影响人肺泡II型上皮细胞的增殖，但显著增强其迁移能力，从而促进纤维化过程。
本研究成功构建了IPF的脂质相关诊断和预后模型，并确定KLF4为潜在的致病基因。这些发现为进一步探索IPF发病机制中的脂质代谢以及针对KLF4的潜在治疗策略提供了基础。
