Integrative transcriptomic and machine learning analysis identifies candidate biomarkers and immune features in diabetic ulcers.
Diabetic 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.
Public 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.
These 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.
Public 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.
These 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.
Authors
Jiang Jiang, Cai Cai, Liu Liu, Jiang Jiang, Shen Shen, Zhang Zhang, Fei Fei, Kuai Kuai, Zhang Zhang, Luo Luo, Song Song, Ma Ma, Li Li, Ma Ma, Ru Ru
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