Bai Bing, Zhao Wenfei, Li Fazhan, Mi Yang, Zheng Pengyuan
Fuhua Street Branch of the Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Henan Key Laboratory of Helicobacter pylori and Microbiota and Gastrointestinal Cancer, Marshall Medical Research Center, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Genet. 2025 Jun 18;16:1602588. doi: 10.3389/fgene.2025.1602588. eCollection 2025.
Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive lung disorder characterized by excessive fibrosis and structural remodeling of lung tissue. The role of inflammation in developing and progressing IPF is increasingly recognized as critical. However, the precise mechanisms and pathways of inflammation in IPF remain unclear. This study aimed to identify inflammation-related genes in IPF and develop a prognostic risk model using machine learning approaches.
The IPF dataset GSE70866 from the Gene Expression Omnibus database was analyzed to identify inflammation-related genes. Unsupervised clustering algorithms were used to classify IPF samples, followed by weighted gene co-expression network analysis (WGCNA) to identify highly correlated genes. Least absolute shrinkage and selection operator (LASSO) regression was then applied, and the intersection of results pinpointed critical hub genes, primarily and . A rat model of pulmonary fibrosis was established, and lentivirus transfection was used to knock down expression. The transfection effect and hub gene expression were validated using Quantitative polymerase chain reaction, Western blot, immunohistochemistry, enzyme-linked immunosorbent assay, hematoxylin-eosin staining, and Masson's trichrome staining. Levels of α-SMA and COL1A1 were also assessed.
WGCNA and LASSO regression analyses identified and as significant contributors to IPF, closely associated with patient prognosis and immune cell infiltration. Protein-protein interaction network analysis established as a novel biomarker for IPF. In a rat model of IPF, expression was significantly elevated compared to that in the controls. Knockdown of expression alleviated pulmonary fibrosis and reduced the expression of COL1A1 protein and α-SMA protein. promotes the expression of COL1A1 protein and α-SMA proteins, suggesting that the mechanism of inflammation-induced pulmonary fibrosis may involve the regulation of COL1A1 and α-SMA by .
These findings establish as a promising biomarker and potential therapeutic target for IPF.
特发性肺纤维化（IPF）是一种慢性进行性肺部疾病，其特征是肺组织过度纤维化和结构重塑。炎症在IPF发生和发展中的作用日益被认为至关重要。然而，IPF中炎症的确切机制和途径仍不清楚。本研究旨在识别IPF中与炎症相关的基因，并使用机器学习方法建立预后风险模型。
分析来自基因表达综合数据库的IPF数据集GSE70866，以识别与炎症相关的基因。使用无监督聚类算法对IPF样本进行分类，随后进行加权基因共表达网络分析（WGCNA）以识别高度相关的基因。然后应用最小绝对收缩和选择算子（LASSO）回归，结果的交集确定了关键的枢纽基因，主要是和。建立了肺纤维化大鼠模型，并使用慢病毒转染来敲低表达。使用定量聚合酶链反应、蛋白质印迹、免疫组织化学、酶联免疫吸附测定、苏木精-伊红染色和Masson三色染色验证转染效果和枢纽基因表达。还评估了α-SMA和COL1A1的水平。
WGCNA和LASSO回归分析确定和是IPF的重要促成因素，与患者预后和免疫细胞浸润密切相关。蛋白质-蛋白质相互作用网络分析确定为IPF的新型生物标志物。在IPF大鼠模型中，与对照组相比，表达显著升高。敲低表达可减轻肺纤维化并降低COL1A1蛋白和α-SMA蛋白的表达。促进COL1A1蛋白和α-SMA蛋白的表达，表明炎症诱导肺纤维化的机制可能涉及对COL1A1和α-SMA的调控。
这些发现确定为IPF的有前景的生物标志物和潜在治疗靶点。
