ArticleBMC genomics2024
A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 22 citations in OpenAlex.
- lncRNA-mRNA Co-expression Network Unveils Neutrophil Metabolic Reprogramming in Human Sepsis.Inflammation · 2026Article
- Unveiling the therapeutic role of 3D-cultured mesenchymal stem cells in diabetic foot ulcers through transcriptomic integration and fibroblast modulation.Molecular biology reports · 2026Article
- Construction of a prognostic model and multidimensional analysis of hepatocellular carcinoma based on palmitoylation-related genes.Discover oncology · 2026Article
- Exploring necroptosis-associated genes: implications for immune responses and therapeutic strategies in diabetic foot ulcers.Archives of medical science : AMS · 2026Article
- FGFR2 is a Candidate Immune-Associated Marker of Diabetic Foot Ulcer That Promotes Keratinocyte Function by Activating the PI3K/Akt and MAPK Pathways.Mediators of inflammation · 2026Article
- FGFR2 identified as a NETs-associated biomarker and therapeutic target in diabetic foot ulcers.European journal of medical research · 2025Article
- Identification of amino acid metabolism‑related genes as diagnostic and prognostic biomarkers in sepsis through machine learning.Experimental and therapeutic medicine · 2025Article
- Investigating the molecular mechanisms of resveratrol in treating diabetic foot ulcers: a comprehensive analysis of network pharmacology and experiment validation.Frontiers in molecular biosciences · 2025Article
- Development and external validation of machine-learning based models to predict diabetic foot ulcer in diabetes population.Frontiers in endocrinology · 2025Article
- A prognostic signature of Glutathione metabolism-associated long non-coding RNAs for lung adenocarcinoma with immune microenvironment insights.Frontiers in immunology · 2025Article
- Diabetes mellitus-driven pulmonary injury: multidimensional mechanisms linking metabolic dysregulation to gut-lung axis and promising therapies.Frontiers in pharmacology · 2025Review
- Machine learning combining external validation to explore the immunopathogenesis of diabetic foot ulcer and predict therapeutic drugs.PloS one · 2025Article
- Development of novel lysosome-related signatures and their potential target drugs based on bulk RNA-seq and scRNA-seq for diabetic foot ulcers.Human genomics · 2024Article
- The role of machine learning in advancing diabetic foot: a review.Frontiers in endocrinology · 2024Review
- Integrating bioinformatics and multiple machine learning to identify mitophagy-related targets for the diagnosis and treatment of diabetic foot ulcers: evidence from transcriptome analysis and drug docking.Frontiers in molecular biosciences · 2024Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundDiabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis.
methodsWe downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset.
resultsIn both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929).
conclusionThis study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.
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