Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis.

IF 4.1 2区 医学 Q2 IMMUNOLOGY Journal of Inflammation Research Pub Date : 2025-02-11 eCollection Date: 2025-01-01 DOI:10.2147/JIR.S489210
Yan Tan, Baojiang Qian, Qiurui Ma, Kun Xiang, Shenglan Wang
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Abstract

Background: 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.

Methods: 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.

Results: 27 DE-IFRGs were identified by intersecting 256 DEGs, 1793 immune-related genes, and 1019 inflammation-related genes. Three genes (RNASE3, S100A12, S100A8) 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.

Conclusion: This study identified three key genes (RNASE3, S100A12 and S100A8) associated with immune response and inflammation in IPF, providing valuable insights into the diagnosis and treatment of IPF.

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特发性肺纤维化关键免疫和炎症相关基因的鉴定和分析。
背景:研究表明免疫和炎症过程可能参与特发性肺纤维化(IPF)的发展;然而,他们的角色仍不清楚。本研究旨在利用生物信息学技术鉴定IPF中与免疫反应和炎症相关的关键基因。方法: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-IFRGs)。使用两种机器学习算法进一步筛选关键基因。受试者工作特征(ROC)曲线下面积(AUC)为bb0 0.7、数据集间表达显著且趋势一致的基因被认为是关键基因。基于这些关键基因,我们进行了nomogram构建、富集和免疫分析、调控网络作图、药物预测和表达验证。结果: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-IFRGs。通过Boruta和LASSO两种机器算法交叉得到3个基因(RNASE3、S100A12、S100A8),具有较好的AUC值诊断性能。这些关键基因都富集在相同的通路中,如GOCC_azurophil_granule,巨噬细胞中IL-12的信号传导和产生是关键基因作用最强的通路。鉴定出6种不同的免疫细胞,包括幼稚CD4 T细胞、CD4记忆静息T细胞、T细胞调节性(Tregs)、单核细胞、巨噬细胞M2和中性粒细胞。实时定量聚合酶链反应(RT-qPCR)结果与训练集和验证集一致,IPF样品中这些关键基因的表达显著上调。结论:本研究确定了IPF中与免疫反应和炎症相关的三个关键基因(RNASE3、S100A12和S100A8),为IPF的诊断和治疗提供了有价值的见解。
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来源期刊
Journal of Inflammation Research
Journal of Inflammation Research Immunology and Microbiology-Immunology
CiteScore
6.10
自引率
2.20%
发文量
658
审稿时长
16 weeks
期刊介绍: An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.
期刊最新文献
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