COPDにおける酸化ストレス関連遺伝子TPPP3およびVEGFA：バルクおよびシングルセルシーケンシング解析による解明

Chronic obstructive pulmonary disease (COPD) is characterized by chronic airway inflammation and is closely linked to oxidative stress. This study aimed to identify and validate key oxidative stress-related genes and pathways involved in COPD using integrated bioinformatics and experimental approaches. Public COPD datasets were obtained from the Gene Expression Omnibus (GEO) database, and oxidative stress-related genes were retrieved from the GeneCards database. Differentially expressed genes (DEGs) were screened and analyzed for functional enrichment. Machine-learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest, were used to identify hub genes and evaluate diagnostic value by calculating the area under the receiver operating characteristic (ROC) curve (AUC). Single-cell RNA sequencing (scRNA-seq) data were analyzed to determine the distribution of hub genes across different cell types. Finally, a COPD combined oxidative stress cell model was established using human bronchial epithelial cells (BEAS-2B), and key gene expression was experimentally validated. We identified 76 overlapping genes associated with both COPD and oxidative stress, mainly enriched in necroptosis, JAK-STAT, MAPK, and related pathways. 12 hub genes were screened using machine-learning methods. Single-cell analysis showed that TPPP3 and VEGFA were predominantly expressed in epithelial cells. Experimental validation confirmed the bioinformatics predictions at the gene level. This study identified and validated 12 oxidative stress-related hub genes in COPD, highlighting TPPP3 and VEGFA as key genes enriched in epithelial cells and potentially involved in tissue remodeling. These findings not only provide insights for exploring new therapeutic strategies but may also serve as potential diagnostic biomarkers or candidate therapeutic targets for COPD.