ArticleInternational wound journal2023
Metabolism-related biomarkers, molecular classification, and immune infiltration in diabetic ulcers with validation.
Article in International wound journal, 2023. 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, 3 citations in OpenAlex.
- Integrative transcriptomic and machine learning analysis identifies candidate biomarkers and immune features in diabetic ulcers.Molecular biology reports · 2026Article
- Enhancing wound healing and overcoming cisplatin resistance in ovarian cancer.International wound journal · 2024Article
- Metabolism-related biomarkers, molecular classification, and immune infiltration in diabetic ulcers with validation.International wound journal · 2023Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
Diabetes mellitus (DM) can lead to diabetic ulcers (DUs), which are the most severe complications. Due to the need for more accurate patient classifications and diagnostic models, treatment and management strategies for DU patients still need improvement. The difficulty of diabetic wound healing is caused closely related to biological metabolism and immune chemotaxis reaction dysfunction. Therefore, the purpose of our study is to identify metabolic biomarkers in patients with DU and construct a molecular subtype-specific prognostic model that is highly accurate and robust. RNA-sequencing data for DU samples were obtained from the Gene Expression Omnibus (GEO) database. DU patients and normal individuals were compared regarding the expression of metabolism-related genes (MRGs). Then, a novel diagnostic model based on MRGs was constructed with the random forest algorithm, and classification performance was evaluated utilizing receiver operating characteristic (ROC) analysis. The biological functions of MRGs-based subtypes were investigated using consensus clustering analysis. A principal component analysis (PCA) was conducted to determine whether MRGs could distinguish between subtypes. We also examined the correlation between MRGs and immune infiltration. Lastly, qRT-PCR was utilized to validate the expression of the hub MRGs with clinical validations and animal experimentations. Firstly, 8 metabolism-related hub genes were obtained by random forest algorithm, which could distinguish the DUs from normal samples validated by the ROC curves. Secondly, DU samples could be consensus clustered into three molecular classifications by MRGs, verified by PCA analysis. Thirdly, associations between MRGs and immune infiltration were confirmed, with LYN and Type 1 helper cell significantly positively correlated; RHOH and TGF-β family remarkably negatively correlated. Finally, clinical validations and animal experiments of DU skin tissue samples showed that the expressions of metabolic hub genes in the DU groups were considerably upregulated, including GLDC, GALNT6, RHOH, XDH, MMP12, KLK6, LYN, and CFB. The current study proposed an auxiliary MRGs-based DUs model while proposing MRGs-based molecular clustering and confirmed the association with immune infiltration, facilitating the diagnosis and management of DU patients and designing individualized treatment plans.
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Registered trials
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