Integrative transcriptomic and machine learning analysis identifies candidate biomarkers and immune features in diabetic ulcers.

backgroundDiabetic ulcers (DU) are a severe complication of diabetes mellitus and are associated with infection, recurrence, amputation, and excess mortality. Robust molecular markers that distinguish ulcer tissue from non-ulcer tissue and clarify the biological basis of impaired healing remain limited. METHODS AND

resultsPublic transcriptomic datasets were integrated to identify DU-associated genes initially selected from lipid metabolism-related gene sets. Differential expression analysis, random forest modeling, single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), Gene Set Variation Analysis (GSVA), drug-gene interaction analysis, and single-cell RNA sequencing were used to prioritize candidate biomarkers. External transcriptomic cohorts were used for validation. A streptozotocin-induced diabetic wound model in C57BL/6 mice was evaluated by serial wound imaging, hematoxylin and eosin staining, quantitative real-time polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), and western blotting. A ten-gene panel comprising ANGPTL4, BNIP3, EEF2K, EIF4EBP1, KLHDC1, KLK10, KLK8, NFIX, QSOX1 and S100A8 showed strong discrimination and reproducible expression patterns across validation datasets. Immune analyses linked the panel to T helper 17 (Th17) cell infiltration and interleukin receptor activity. Single-cell analysis localized these genes to distinct wound-associated cell populations. Diabetic mice exhibited delayed wound closure and greater residual wound widths than control mice. Transcript and protein assays showed concordant changes in representative genes.

conclusionsThese findings identify a candidate biomarker panel for DU and connect its transcriptomic pattern with immune remodeling, cell-specific expression, and impaired wound repair. Further validation in larger human cohorts is required before clinical application.