Introduction:
Diabetic foot ulcers (DFUs) are among the most severe and debilitating diabetic complications, often leading to extremely high morbidity and mortality. Recently, increasing evidence has highlighted the role of necroptosis, a distinct type of programmed cell death distinct from apoptosis, in the progression and severity of DFUs. Understanding necroptosis-associated genes in DFUs could open new therapeutic avenues aimed at modulating this form of cell death, potentially improving outcomes for patients suffering from this serious diabetic complication.

Material and methods:
This study focuses on discovering and confirming potential necroptosis biomarkers linked to DFU through the application of machine learning and bioinformatics approaches. We obtained three microarray datasets associated with DFU individuals from the Gene Expression Omnibus (GEO) database: GSE68183, GSE134431, and GSE80178.

Results:
In GSE134431, we identified necroptosis-associated genes (NRGs) with differential expression between DFU patients and healthy controls, totaling 37 NRGs. Additionally, we observed an activated immune response in both groups. Moreover, clustering analysis revealed two distinct clusters within the DFU samples, showcasing immune heterogeneity. Subsequently, we constructed a Random Forest (RF) model utilizing 5 genes (CENPB, TRIM56, ZNF768, PLIN4, and ATP1A1). Notably, this model demonstrated outstanding performance on the external validation datasets GSE134431, GSE68183 (AUC = 1.000). The study has pinpointed five genes linked to necroptosis in the context of DFU, unveiling new potential biomarkers and targets for DFU therapy.

Conclusions:
Bioinformatics analysis elucidated that CENPB, TRIM56, ZNF768, PLIN4, and ATP1A1 could serve as potential biomarkers for future DFU research.