ArticleFrontiers in endocrinology2025
Integrated transcriptomic, proteomic, and metabolomic analysis unveils key roles of protein and nucleic acid interactions in diabetic ulcer pathogenesis.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Network Pharmacology and Transcriptomic Analysis Reveal the Therapeutic Mechanisms of Xiebai Bining Decoction in Hyperuricemic Nephropathy.Combinatorial chemistry & high throughput screening · 2026Article
- Cell-engineered technologies for wound healing and tissue regeneration.npj biomedical innovations · 2025Review
- Single-cell multi-omics dissection of c-Myb/AURKA-mediated autophagy and metabolic reprogramming in diabetic adipose-derived stem cells.Frontiers in immunology · 2025Article
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Authors and funding
8 authors.
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Abstract
Background: Diabetes mellitus significantly increases the risk of complications, particularly diabetic foot ulcers (DFUs). However, the underlying mechanism remains unclear. This study aimed to assess the overall therapeutic approach in diabetic ulcers. Methods: Using integrated high-throughput multi-omics approaches, including transcriptomics, proteomics, and metabolomics, we constructed a compound-reaction-enzyme-gene network to identify the key molecular mechanisms involved in the pathogenesis of DFUs. Major findings were further validated in mouse models of diabetic and control ulcers. Results: Transcriptomics identified 653 differentially expressed genes (DEGs) between diabetic ulcers and control groups. Pathway analysis indicated that these genes were mostly related to inflammation, including the cytokine-cytokine receptor interaction, TNF signaling pathway, and NF-κB signaling pathway. Proteomics revealed 464 upregulated and 419 downregulated proteins, indicating many differentially expressed proteins (DEPs). The pathways with the highest representation of DEPs included diabetic cardiomyopathy, PPAR signaling pathway, and HIF-1 signaling pathway. Metabolomics identified 1,304 metabolites, predominantly lipids (32.1%) and organic acids (20.2%). Principal component analysis and partial least squares discriminant analysis confirmed the model's effectiveness in distinguishing sample groups, whereas bioinformatics analysis revealed significant metabolic pathways, particularly amino acid biosynthesis. Conclusion: Our findings identified critical molecular signatures associated with DFUs and lay the groundwork for developing innovative therapeutic strategies to improve clinical outcomes in patients with this challenging condition.
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