Abstract
BACKGROUND AND OBJECTIVE: Diabetic foot ulcers (DFUs) are chronic complications of diabetes, driven by metabolic dysregulation and impaired wound healing. This study investigates the roles of hypoxia, glycolysis, and lactylation in DFUs and identifies potential diagnostic and therapeutic biomarkers. METHODS: Single-cell RNA sequencing (scRNA-seq) was employed to assess cellular diversity, metabolic states, and intercellular communication in DFUs. KEGG/GO enrichment, pseudotime trajectory analysis, and cell-cell communication profiling were conducted to explore metabolic and cellular dynamics. Bulk RNA-seq was integrated for differential expression analysis and biomarker validation. Machine learning methods, including LASSO, Support vector machine, and Random Forest, were applied to identify and validate biomarkers across external datasets. RESULTS: Metabolic shifts in hypoxia, glycolysis, and lactylation were observed, with keratinocytes displaying the highest metabolic activity. Pseudotime analysis revealed distinct wound-healing phases, while cell-cell communication profiling identified increased signaling among keratinocytes, fibroblasts, and SMCs in high-metabolic states, disrupting key pathways like ECM-receptor interaction and focal adhesion. Machine learning integration of scRNA-seq and bulk RNA-seq identified PKM, GAMT, and EGFR as diagnostic biomarkers strongly linked to metabolic and immune regulation. Functional analyses highlighted their roles in energy metabolism, cellular proliferation, and immune signaling, providing new insights into DFU pathogenesis. CONCLUSION: This study reveals metabolic dysregulation and disrupted cellular communication as central to the non-healing DFU microenvironment, with validated biomarkers and pathways offering potential targets for improved diagnosis and treatment.