UPP1 as a Diagnostic Biomarker: Insights from Integrative Bioinformatics and Immune Infiltration Analyses in COPD

COPD is a progressive respiratory disorder characterized by persistent airflow limitation and chronic inflammation, yet the role of N4-acetylcytidine (ac4C) RNA modification in its pathogenesis remains largely unexplored. This study aimed to systematically screen for ac4C-related genes (ac4C-RGs) from a published database and investigate their regulatory networks in COPD, thereby identifying potential biomarkers for further mechanistic studies without assuming a direct regulatory relationship between any specific gene and ac4C modification. Differentially expressed genes (DEGs) were identified from transcriptomic profiles, and weighted gene co-expression network analysis (WGCNA) was applied to uncover key co-expression modules. Cross-analysis among DEGs, significant modules, and ac4C-RGs was conducted. Key genes were screened using LASSO regression, XGBoost, and random forest algorithms, followed by logistic regression‑based diagnostic model construction. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis, area under the curve (AUC) with 95% confidence intervals, calibration curve assessment, and decision curve analysis (DCA). A total of 160 overlapping genes were identified, and six hub genes (PTRF, PRKCDBP, UPP1, TOR3A, FAM168B, and B4GALT2) were consistently selected by all three machine learning algorithms. The diagnostic model demonstrated good discriminative performance, with AUCs of 0.766, 0.759, and 0.723 in the training, internal test, and external validation sets, respectively. Regulatory network analysis suggested potential ceRNA axes and transcription factor interactions, while immune infiltration profiling revealed significant correlations between key genes and multiple immune cell subsets. Drug-gene interaction analysis and molecular docking indicated that fluorouracil, capecitabine, and 5-benzylacyclouridine may exhibit favorable predicted binding affinities with UPP1. In conclusion, PTRF, PRKCDBP, UPP1, TOR3A, FAM168B, and B4GALT2 were identified as potential ac4C-related biomarkers in COPD, potentially involved in immune and metabolic regulation, providing a foundation for future functional investigations and therapeutic exploration.