Identification and validation of potential biomarkers related to oxidative stress in idiopathic pulmonary fibrosis

• Published in: Immunobiology (1979) 2024-09, Vol.229 (5), p.152791, Article 152791
• Main Authors: Du, Xianglin, Ma, Zhen, Xing, Yanqing, Feng, Liting, Li, Yupeng, Dong, Chuanchuan, Ma, Xinkai, Huo, Rujie, Tian, Xinrui
• Format: Article
• Language: English
• Subjects: Bioinformatics; Diagnostic; Idiopathic pulmonary fibrosis; Immune microenvironment; Macrophages; Oxidative stress
• ISSN: 0171-2985; 1878-3279; 1878-3279
• DOI: 10.1016/j.imbio.2024.152791

•SNCA and CYP1B1 may have clinical value as potential biomarkers for diagnosing patients with idiopathic pulmonary fibrosis.•High levels of CYP1B1 and SNCA expression in idiopathic pulmonary fibrosis patients were associated with poor prognosis.•SNCA was expressed in basal cells, with no previous studies of this gene in idiopathic pulmonary fibrosis.•CYP1B1 was involved in the pathogenesis of idiopathic pulmonary fibrosis through the regulation of macrophages. Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrotic interstitial pneumonia with a poor prognosis and a pathogenesis that has not been fully elucidated. Oxidative stress is closely associated with IPF. In this research, we aimed to identify reliable diagnostic biomarkers associated with the oxidative stress through bioinformatics techniques. The gene expression profile data from the GSE70866 dataset was retrieved from the gene expression omnibus (GEO) database. We extracted 437 oxidative stress-related genes (ORGs) from gene set enrichment analysis (GSEA). The GSE141939 dataset was used for single-cell RNA-seq analysis to identify the expression of diagnostic genes in different cell clusters. A total of 10 differentially expressed oxidative stress-related genes (DE-ORGs) were screened. Subsequently, SOD3, CD36, ACOX2, RBM11, CYP1B1, SNCA, and MPO from the 10 DE-ORGs were identified as diagnostic genes based on random forest algorithm with randomized least absolute shrinkage and selection operator (LASSO) regression. A nomogram was constructed to evaluate the risk of disease. The decision curve analysis (DCA) and clinical impact curves indicated that the nomogram based on these seven biomarkers had extraordinary predictive power. Immune cell infiltration analysis results revealed that DE-ORGs were closely related to various immune cells, especially CYP1B1 was in positive correlation with monocytes and negative correlation with macrophages M1. Single-cell RNA-seq analysis showed that CYP1B1 was mainly associated with macrophages, and SNCA was mainly associated with basal cells. CYP1B1 and SNCA were diagnostic genes associated with oxidative stress in IPF.