Identification of candidate selenium-associated transcriptomic biomarkers in chronic obstructive pulmonary disease: an integrated bioinformatics and expression validation study.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) is marked by long-lasting airflow obstruction accompanied by persistent inflammation and disturbed redox balance. Although oxidative stress is well established in COPD, it remains unclear whether PBMC transcriptional patterns that co-vary with a curated selenium-metabolism and selenoprotein signature can identify candidate COPD markers. We therefore evaluated such patterns as an exploratory transcriptomic prioritization strategy rather than as evidence of selenium-specific regulation.

METHODS: COPD-related transcriptomic datasets were downloaded from the GEO database, and differentially expressed genes (DEGs) were determined using conventional differential expression analysis. Selenium metabolism and selenoprotein (SMASP) genes were collected from the MSigDB. WGCNA was then used to identify a module closely associated with COPD status and SMASP scores, and candidate genes were defined by intersecting module genes with DEGs. XGBoost and SVM-RFE, together with cross-validation, were applied to select biomarkers. A nomogram was subsequently generated to assess their diagnostic utility. Gene set enrichment analysis (GSEA) and RT-qPCR validation were finally conducted to examine the potential involvement of these biomarkers in COPD.

RESULTS: We detected 235 DEGs and 40 selenoprotein-related genes, from which 17 overlapping candidate genes were obtained. ERBB4 and ZKSCAN3 were ultimately retained as biomarkers and were significantly decreased in COPD samples. The nomogram based on these two genes showed favorable predictive ability. GSEA indicated that both biomarkers were associated with ribosome and olfactory transduction pathways. ERBB4 was also linked to neuroactive ligand-receptor interaction, whereas ZKSCAN3 was related to extracellular matrix (ECM)-receptor interaction. RT-qPCR in a small exploratory cohort (n = 5 per group) showed directionally consistent downregulation of ERBB4 and ZKSCAN3 in COPD samples, supporting the bioinformatics findings. This exploratory analysis was cross-sectional and does not establish causality, selenium deficiency, or clinical utility. The findings are hypothesis-generating and require prospective validation.

CONCLUSION: ERBB4 and ZKSCAN3 were prioritized as exploratory candidate expression markers in the analyzed COPD datasets. Their diagnostic value, selenium specificity, and biological functions require confirmation in larger independent cohorts and functional studies.