Current pharmaceutical biotechnology · 2026
Uncovering Immunometabolic Mechanisms and Biomarkers in Diabetic Foot Ulcers: A Multi-omics Approach.
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Zeng Guang, Huang Yi, Hu Junsheng, Li Mengfan, Tian Ming, Huang Rong, Jin Yongzhi
Аннотация
BACKGROUND: Abnormal lipid metabolism plays a crucial role in diabetic foot ulcers (DFU). This study aims to systematically uncover the cell-specific mechanisms of lipid metabolism disorders in DFUs and screen for potential diagnostic biomarkers.
METHODS: Bulk and single-cell transcriptomic data related to DFUs were integrated from the GEO database. Key cell types were identified through single-cell analysis, and candidate genes were screened by combining differential expression analysis with a lipid metabolism gene set. Random forest analysis, Elastic Net regression, and expression validation were subsequently performed to identify core genes, followed by the construction and validation of a nomogram model. The biological functions of the identified core genes were further investigated using immune infiltration analysis, cell subset analysis, pseudotemporal trajectory inference, and molecular docking.
RESULTS: Single-cell analysis identified macrophages as key cells in DFU, yielding 19 lipid metabolism-related candidate genes, with CCL3 and ADAP2 ultimately confirmed as core genes. The nomogram model based on these two genes exhibited good predictive performance (AUC=0.909). Macrophages were classified into three functionally heterogeneous subsets (Macro_ADAP2⁷, Macro_CCL3⁷, and Macro_IGKC⁷), and pseudotemporal analysis revealed their dynamic evolution from inflammation to metabolic regulation. Molecular docking suggested that Wortmannin and BX-471 could stably bind to ADAP2 and CCL3, respectively.
DISCUSSION: This single-cell study uncovers a central role for disordered macrophage lipid metabolism in DFU. Heterogeneous expression of CCL3 and ADAP2 defines functional macrophage subsets, linking lipid abnormalities to immune dysfunction and yielding a high-value diagnostic model with drug-target potential for precision interventions.
CONCLUSION: This study provides new insights into elucidating the immunometabolic regulatory mechanisms of DFU and developing targeted therapies.
METHODS: Bulk and single-cell transcriptomic data related to DFUs were integrated from the GEO database. Key cell types were identified through single-cell analysis, and candidate genes were screened by combining differential expression analysis with a lipid metabolism gene set. Random forest analysis, Elastic Net regression, and expression validation were subsequently performed to identify core genes, followed by the construction and validation of a nomogram model. The biological functions of the identified core genes were further investigated using immune infiltration analysis, cell subset analysis, pseudotemporal trajectory inference, and molecular docking.
RESULTS: Single-cell analysis identified macrophages as key cells in DFU, yielding 19 lipid metabolism-related candidate genes, with CCL3 and ADAP2 ultimately confirmed as core genes. The nomogram model based on these two genes exhibited good predictive performance (AUC=0.909). Macrophages were classified into three functionally heterogeneous subsets (Macro_ADAP2⁷, Macro_CCL3⁷, and Macro_IGKC⁷), and pseudotemporal analysis revealed their dynamic evolution from inflammation to metabolic regulation. Molecular docking suggested that Wortmannin and BX-471 could stably bind to ADAP2 and CCL3, respectively.
DISCUSSION: This single-cell study uncovers a central role for disordered macrophage lipid metabolism in DFU. Heterogeneous expression of CCL3 and ADAP2 defines functional macrophage subsets, linking lipid abnormalities to immune dysfunction and yielding a high-value diagnostic model with drug-target potential for precision interventions.
CONCLUSION: This study provides new insights into elucidating the immunometabolic regulatory mechanisms of DFU and developing targeted therapies.