Abstract:   Objective  Diabetic foot ulcer （DFU） is a severe complication in diabetic patients， where the hypoxic microenvironment plays a critical role in its pathogenesis and delayed healing， though the underlying molecular mechanisms remain unclear. To systematically analyze the regulatory network of hypoxia-related genes in DFU using bioinformatics approaches， identify key biomarkers， and provide insights for targeted therapies. Methods Integrated datasets from GEO and MSigDB hypoxia-related gene sets were utilized. Differential expression analysis（ limma， DESeq2）， weighted gene coexpression network analysis （WGCNA）， and GO/KEGG functional enrichment were performed. Hub genes were screened using three machine learning algorithms（ Lasso， SVM-RFE， and random forest）， and their diagnostic efficacy was validated. Results  A total of 152 differentially expressed genes （DEGs） were identified， including 14 hypoxia-related DEGs （HRDEGs）. Enrichment analysis revealed HRDEGs involvement in glucose metabolism， lipid metabolism， and immune cell regulation. Machine learning further pinpointed the hub gene BGN. BGN exhibited significantly lower expression in DFU groups， with area under the ROC curve （AUC） values of 0.833 （training set） and 0.931 （validation set）， indicating high diagnostic accuracy. Single-gene GSEA demonstrated that BGN participates in DFU pathology by regulating tissue repair， inflammatory responses， and extracellular matrix interactions. Conclusion BGN is a key biomarker in the hypoxic microenvironment of DFU and may serve as a potential molecular target for early diagnosis and targeted therapy. This study provides new directions for understanding DFU mechanisms and clinical interventions.

Key words: &emsp, Diabetic foot ulcer, &emsp, Hypoxia, &emsp, Bioinformatics