Table_1_Identification of diagnostic hub genes related to neutrophils and infiltrating immune cell alterations in idiopathic pulmonary fibrosis.docx

BackgroundThere is still a lack of specific indicators to diagnose idiopathic pulmonary fibrosis (IPF). And the role of immune responses in IPF is elusive. In this study, we aimed to identify hub genes for diagnosing IPF and to explore the immune microenvironment in IPF.MethodsWe identified differentially expressed genes (DEGs) between IPF and control lung samples using the GEO database. Combining LASSO regression and SVM-RFE machine learning algorithms, we identified hub genes. Their differential expression were further validated in bleomycin-induced pulmonary fibrosis model mice and a meta-GEO cohort consisting of five merged GEO datasets. Then, we used the hub genes to construct a diagnostic model. All GEO datasets met the inclusion criteria, and verification methods, including ROC curve analysis, calibration curve (CC) analysis, decision curve analysis (DCA) and clinical impact curve (CIC) analysis, were performed to validate the reliability of the model. Through the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm (CIBERSORT), we analyzed the correlations between infiltrating immune cells and hub genes and the changes in diverse infiltrating immune cells in IPF.ResultsA total of 412 DEGs were identified between IPF and healthy control samples, of which 283 were upregulated and 129 were downregulated. Through machine learning, three hub genes (ASPN, SFRP2, SLCO4A1) were screened. We confirmed their differential expression using pulmonary fibrosis model mice evaluated by qPCR, western blotting and immunofluorescence staining and analysis of the meta-GEO cohort. There was a strong correlation between the expression of the three hub genes and neutrophils. Then, we constructed a diagnostic model for diagnosing IPF. The areas under the curve were 1.000 and 0.962 for the training and validation cohorts, respectively. The analysis of other external validation cohorts, as well as the CC analysis, DCA, and CIC analysis, also demonstrated strong agreement. There was also a significant correlation between IPF and infiltrating immune cells. The frequencies of most infiltrating immune cells involved in activating adaptive immune responses were increased in IPF, and a majority of innate immune cells showed reduced frequencies.ConclusionOur study demonstrated that three hub genes (ASPN, SFRP2, SLCO4A1) were associated with neutrophils, and the model constructed with these genes showed good diagnostic value in IPF. There was a significant correlation between IPF and infiltrating immune cells, indicating the potential role of immune regulation in the pathological process of IPF.

背景：尽管目前尚缺乏针对特发性肺纤维化（IPF）的特异性诊断指标，且免疫反应在IPF中的作用尚不明确。本研究旨在鉴定用于诊断IPF的中心基因，并探讨IPF中的免疫微环境。方法：利用GEO数据库，我们鉴定了IPF与对照肺样本之间的差异表达基因（DEGs）。结合LASSO回归和SVM-RFE机器学习算法，我们确定了中心基因。其在博来霉素诱导的肺纤维化小鼠模型和由五个合并的GEO数据集构成的meta-GEO队列中的差异表达得到了进一步验证。随后，我们利用这些中心基因构建了诊断模型。所有GEO数据集均符合纳入标准，并通过ROC曲线分析、校准曲线（CC）分析、决策曲线分析（DCA）和临床影响曲线（CIC）分析等方法对模型的可靠性进行了验证。通过细胞类型识别算法（CIBERSORT），我们分析了浸润免疫细胞与中心基因之间的相关性以及IPF中不同浸润免疫细胞的变化。结果：在IPF与健康对照样本之间鉴定出412个DEGs，其中283个上调，129个下调。通过机器学习筛选出三个中心基因（ASPN、SFRP2、SLCO4A1）。我们利用qPCR、蛋白质印迹和免疫荧光染色及分析meta-GEO队列确认了它们的差异表达。这三个中心基因的表达与中性粒细胞呈强相关性。随后，我们构建了诊断IPF的诊断模型。训练集和验证集的曲线下面积分别为1.000和0.962。其他外部验证队列的分析、CC分析、DCA和CIC分析也显示了一致的结果。IPF与浸润免疫细胞之间存在显著相关性。在IPF中，参与激活适应性免疫反应的多数浸润免疫细胞的频率增加，而多数先天免疫细胞的频率则降低。结论：本研究证实了三个中心基因（ASPN、SFRP2、SLCO4A1）与中性粒细胞相关，并证实了由这些基因构建的模型在IPF诊断中的良好诊断价值。IPF与浸润免疫细胞之间存在显著相关性，这表明免疫调节可能在IPF的病理过程中发挥潜在作用。