Neutrophil Extracellular Trap-Related Genes and Immune Infiltration in Diabetic Foot Ulcers by Bioinformatics Analysis and Experimental Verification

Abstract

The study aims to investigate the correlation between diabetic foot ulcer (DFU) and neutrophil extracellular traps (NETs), as well as to identify potential biomarkers. Gene microarray data from the Gene Expression Omnibus (GEO) was employed in this research. The GSE134431 and GSE80178 datasets were obtained for bioinformatics analysis to identify differentially expressed genes, which were then cross-referenced with a curated database of genes associated with neutrophil extracellular traps (NETs). Next, the specificity of these potential genes for the disease was determined via Receiver Operating Characteristic (ROC) analysis. Subsequently, the protein-protein interaction (PPI) networks and the Least Absolute Shrinkage and Selection Operator (Lasso) regression were used to identify potential key genes. Additionally, the MCPcounter algorithm was utilized to evaluate the immune infiltration and analyze the relationship between core genes and immune cell infiltration. Furthermore, the levels of expression of these candidate genes were verified by RT-qPCR. Four core genes (CXCL8, S100A12, CXCL12, S100A9) were identified through ROC and Lasso regression analyses. Moreover, individuals suffering from diabetic foot disease exhibited decreased expression levels of T cells, CD8 T cells, cytotoxic lymphocytes, NK cells, fibroblasts, and myeloid dendritic cells; In contrast, monocytes and neutrophils exhibited elevated expression levels. The expression levels of CXCL12 and CXCL8 were positively linked to a wide range of immune and endothelial cells, whereas S100A12 and S100A9 showed distinct correlation patterns with specific immune cell types. qPCR analysis showed increased expression of CXCL8 and CXCL12 as the levels of glucose increased in vitro experimental hyperglycemia. In conclusion, the intricate interplay among genes and immune cell types suggests that DFU and NETs hold potential as valuable biomarkers for the diagnosis and treatment of these conditions, thereby enhancing their clinical meanings.