Integrative Analysis Identifies MCEMP1 and VCAN as Novel Hpoxia-Associated Biomarkers in COPD through Multi-Omics and Machine Learning Aproaches

Background Chronic obstructive pulmonary disease (COPD) is a major global health burden, characterized by progressive airflow limitation and persistent respiratory symptoms. While hypoxia is a key pathogenic factor, the molecular mechanisms linking hypoxic damage to disease progression remain unclear. This study aimed to identify and validate novel hypoxia-associated biomarkers in COPD via an integrated multi-omics approach. Methods Transcriptomic profiles from two independent cohorts (GSE100153 and GSE146560) were analyzed using weighted gene co-expression network analysis (WGCNA) combined with machine learning. Candidate biomarkers were validated by quantitative PCR in blood samples from 10 COPD patients and 10 healthy controls at Yunnan University Affiliated Hospital. The immune microenvironment was characterized via single-sample gene set enrichment analysis (ssGSEA), and drug-gene network analysis explored therapeutic implications. Results MCEMP1 and VCAN were identified as novel hypoxia-associated biomarkers, showing consistent upregulation in discovery cohorts and clinical samples (P < 0.05). These markers demonstrated strong diagnostic potential (AUC > 0.7), correlated with inflammatory responses and immune cell infiltration. A diagnostic nomogram showed excellent predictive performance. Both markers negatively correlated with immature B cells (correlation < -0.3, P < 0.05) and associated with distinct drug responses. Conclusions This study establishes MCEMP1 and VCAN as promising COPD diagnostic biomarkers, providing insights into hypoxia-mediated mechanisms to facilitate novel therapeutic strategies and patient stratification.