Tan Yan, Qian Baojiang, Ma Qiurui, Xiang Kun, Wang Shenglan
Department of Respiratory and Critical Care Medicine, the First People's Hospital of Yunnan Province, Kunming, People's Republic of China.
Medical School of Kunming University of Science and Technolog, Kunming, People's Republic of China.
J Inflamm Res. 2025 Feb 11;18:1993-2009. doi: 10.2147/JIR.S489210. eCollection 2025.
Studies suggest that immune and inflammation processes may be involved in the development of idiopathic pulmonary fibrosis (IPF); however, their roles remain unclear. This study aims to identify key genes associated with immune response and inflammation in IPF using bioinformatics.
We identified differentially expressed genes (DEGs) in the GSE93606 dataset and GSE28042 dataset, then obtained differentially expressed immune- and inflammation-related genes (DE-IFRGs) by overlapping DEGs. Two machine learning algorithms were used to further screen key genes. Genes with an area under curve (AUC) of > 0.7 in receiver operating characteristic (ROC) curves, significant expression and consistent trends across datasets were considered key genes. Based on these key genes, we carried out nomogram construction, enrichment and immune analyses, regulatory network mapping, drug prediction, and expression verification.
27 DE-IFRGs were identified by intersecting 256 DEGs, 1793 immune-related genes, and 1019 inflammation-related genes. Three genes () were obtained by crossing two machine algorithms (Boruta and LASSO),which had good diagnostic performance with AUC values. These key genes were all enriched in the same pathways, such as GOCC_azurophil_granule, IL-12 signalling and production in macrophages is the pathway with the strongest role for key genes. Six distinct immune cells, including naive CD4 T cells, T cells CD4 memory resting, T cells regulatory (Tregs), Monocytes, Macrophages M2, Neutrophils were identified. Real-time quantitative polymerase chain reaction (RT-qPCR) results were consistent with the training and validation sets, and the expression of these key genes was significantly upregulated in the IPF samples.
This study identified three key genes ( and ) associated with immune response and inflammation in IPF, providing valuable insights into the diagnosis and treatment of IPF.
研究表明免疫和炎症过程可能参与特发性肺纤维化（IPF）的发病；然而，它们的作用仍不明确。本研究旨在利用生物信息学确定与IPF免疫反应和炎症相关的关键基因。
我们在GSE93606数据集和GSE28042数据集中鉴定差异表达基因（DEG），然后通过重叠DEG获得差异表达的免疫和炎症相关基因（DE-IFRG）。使用两种机器学习算法进一步筛选关键基因。在受试者工作特征（ROC）曲线中曲线下面积（AUC）>0.7、表达显著且跨数据集趋势一致的基因被视为关键基因。基于这些关键基因，我们进行了列线图构建、富集和免疫分析、调控网络映射、药物预测及表达验证。
通过交叉256个DEG、1793个免疫相关基因和1019个炎症相关基因，鉴定出27个DE-IFRG。通过交叉两种机器学习算法（Boruta和LASSO）获得了三个基因（），其AUC值具有良好的诊断性能。这些关键基因均富集于相同的通路，如GOCC_嗜天青颗粒、巨噬细胞中的IL-12信号传导和产生是对关键基因作用最强的通路。鉴定出六种不同的免疫细胞，包括初始CD4 T细胞、静息记忆CD4 T细胞、调节性T细胞（Tregs）、单核细胞、M2巨噬细胞、中性粒细胞。实时定量聚合酶链反应（RT-qPCR）结果与训练集和验证集一致，且这些关键基因在IPF样本中的表达显著上调。
本研究确定了三个与IPF免疫反应和炎症相关的关键基因（和），为IPF的诊断和治疗提供了有价值的见解。
