A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration

Abstract

Background Diabetic 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. Methods We 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. Results In 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). Conclusion This 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.

Development of novel lysosome-related signatures and their potential target drugs based on bulk RNA-seq and scRNA-seq for diabetic foot ulcers

“…Wound healing is a regulated and highly complex biological process. It is reported that immune/inflammatory regulation is crucial in the wound healing process [ 57 ]. To investigate the role of immune/inflammatory cells in DFU wound deterioration, we performed a comprehensive assessment of the immune microenvironment of DFUs and presented different cellular components in the immune microenvironment between DFUs and Controls.…”

“…It has been previously shown that macrophages play a role in hemostasis and that pro-inflammatory macrophages infiltrate and remove dead cells and bacteria from the wound site after injury [ 60 ]. During the wound healing phase, macrophage polarization stimulates the movement and growth of fibroblasts, endothelial cells, and keratinocytes, which accelerates repair of the epidermis, dermis, and vascular system [ 57 ]. Additionally, it has been shown that decreased innate immunoregulatory function exacerbates macrophage polarization imbalance as well as decreased wound healing [ 61 ], which is also consistent with the results of the immune infiltration analyses mentioned above, suggesting that a significant down-regulation of the abundance of dominant immune cells in DFUs, including B and T cells, leads to a decrease in wound healing capacity.…”

The role of machine learning in advancing diabetic foot: a review

“…This discovery, validated through external datasets and immunohistochemistry, underscores GSTM5’s potential influence on essential signaling pathways and its association with T cells, paving the way for targeted immune therapies ( 61 ). Equally important is that another pivotal study sheds light on glutamine metabolism’s role in DFU pathogenesis ( 62 ). By analyzing microarray datasets from the GEO database, this research unveiled differential expressions of glutamine metabolism-related genes and their correlation with immune cell infiltration in DFU patients.…”

“…By analyzing microarray datasets from the GEO database, this research unveiled differential expressions of glutamine metabolism-related genes and their correlation with immune cell infiltration in DFU patients. The employment of a SVM model, based on 5 critical genes, demonstrated remarkable predictive accuracy (AUC = 0.929) on external validation datasets ( 62 ). Furthermore, investigations into angiogenesis-related genes using machine learning algorithms underscored the vital function of angiogenesis in DFU’s wound healing process ( 63 ).…”

“…GEO allows researchers to submit, retrieve, and explore data sets in an effort to understand complex biological phenomena. This includes the examination of gene expression changes associated with diseases such as DFUs, where understanding the genetic underpinnings can lead to breakthroughs in diagnosis, treatment, and prevention strategies (61)(62)(63). The database supports the use of machine learning techniques to analyze and interpret the vast amounts of genomic data it contains, enabling the identification of disease biomarkers and the elucidation of molecular pathways involved in the progression of complex conditions like DFUs (61)(62)(63).…”