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. 2025 Aug 18:18:11355-11372.
doi: 10.2147/JIR.S531204. eCollection 2025.

Identification of Neutrophil Extracellular Trap-Related Biomarkers in Diabetic Foot Ulcers Based on Bioinformatics

Kang Liu  1 , Lei Lei  1 , Xin-Lei Yang  1 , Xin-He Zhang  1
Affiliations

Identification of Neutrophil Extracellular Trap-Related Biomarkers in Diabetic Foot Ulcers Based on Bioinformatics

Kang Liu et al. J Inflamm Res. .

Abstract

Background: Neutrophil extracellular traps (NETs) play an important role in the development of diabetic foot ulcers (DFUs), and improving its progression by targeting the activation and regulation of NETs-related genes (NETRGs) might be an important therapeutic target and deserve further exploration.

Methods: Differentially expressed NETRGs (DENETRGs) were obtained by intersecting the NETRGs and the differentially expressed genes (DEGs) between DFUs and healthy control (HC) samples. The nomogram was constructed with the biomarkers identified by machine learning algorithms. In addition, the immune infiltration was conducted to further analyze the pathogenesis of DFUs. We used quantitative real-time polymerase chain reaction (qRT-PCR) to verify the expression of the biomarkers.

Results: S100A12 and HPSE were defined as the biomarkers of DFUs, and both were significantly highly expressed in DFUs groups. Moreover, the diagnostic model with two biomarker was constructed. Besides, the proportion of the type 17 T helper cell and neutrophil were significantly increased, and the proportion of activated B cell was significantly decreased in the DFUs groups.

Conclusion: This study revealed the potential molecular mechanisms of NETRGs in DFUs, which could provide novel insights for the clinical diagnosis and treatment of DFUs.

Keywords: diabetic foot ulcers; diagnosis; function; immune infiltration; mechanism; neutrophil extracellular traps-related genes.

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Conflict of interest statement

The authors declare no competing interests in this work.

Figures

Figure 1
Figure 1
Identification of differentially expressed neutrophil extracellular traps-related genes (DENETRGs) from the GSE134431 dataset. (A) The volcano map and heat map of differentially expressed genes (DEGs) between diabetic foot ulcers (DFUs) and healthy control (HC) samples. (B) The Venn diagram of DENETRGs obtained by intersecting DEGs and NETRGs. (C) The expression discrepancies of DENETRGs between DFU and HC groups. ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. (D) The chromosomal localization analysis of DENETRGs. (E) The pie chart illustrating the relevance between DENETRGs. (F) The GO terms and KEGG pathways enriched in DENETRGs. (G) The PPI network of DENETRGs.
Figure 2
Figure 2
Screening of candidate biomarkers by machine learning algorithms. (A) Tuning parameter selection via 10-fold crossvalidation with minimum criteria in the LASSO model; LASSO coefficient profiles of genes. (B) The SVM-RFE cross-validation curve. (C) The line chart depicting the temporal evolution of variable importance scores during the execution of the Boruta algorithm, along with a box plot illustrating the distribution of variable importance. (D) The Venn graph of candidate gene.
Figure 3
Figure 3
Identification and validation of biomarkers for DFU. (A and B) Comparison of the expression levels of candidate genes between the DFU and HC groups in the training (A) and validation (B) sets. not significant; ***P < 0.001; ****P < 0.0001. (C and D) Receiver operating characteristic (ROC) curves biomarkers in the training (C) and validation (D) sets. (E) The expression of biomarkers in clinical samples. *P < 0.05; ***P < 0.001.
Figure 4
Figure 4
Construction of the nomogram. (A) The nomogram constructed based on two biomarkers (S100A12 and HPSE). (B) The calibration curve, decision curve analysis (DCA) curve, and ROC curve of nomogram.
Figure 5
Figure 5
GSEA enrichment analysis of biomarkers. (A) S100A12; (B) HPSE.
Figure 6
Figure 6
Immune infiltration analysis. (A) The stacked bar graph depicting the immune cell scores of 28 samples from DFU and HC groups. (B) Comparison of estimated proportion of immune cells between DFU and HC groups. not significant; *P < 0.05; **P < 0.01. (C) Correlation heat map of differential immune cells and biomarkers.
Figure 7
Figure 7
The ceRNA regulatory network of biomarkers. The yellow square represents mRNA, the red circle represents miRNA, the blue triangle represents lncRNA, and the lines represent the interaction between the two molecules.
Figure 8
Figure 8
Prediction of targeted drugs for DFU. (A) The drug-biomarker network. The yellow graph represents biomarkers and the blue represents targeted drugs. (B) The secondary structure of drugs.
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