Identification of SASP-associated biomarkers and regulatory mechanisms in diabetic foot ulcers based on transcriptomics and experimental validation

BackgroundDiabetic foot ulcers (DFU) constitute a major complication arising from diabetes mellitus. Emerging research findings have underscored the pivotal contribution of cellular senescence to the pathophysiological development of wounds exhibiting delayed healing; nevertheless, the molecular regulatory networks governing this process in DFU pathogenesis remain incompletely elucidated MethodsDFU-related transcriptomic datasets were acquired from public databases. Biomarkers were identified through machine learning algorithms combined with expression validation. To evaluate the diagnostic predictive capacity of identified biomarkers for DFU, a nomogram prediction model was developed and validated. Further analyses included Gene Set Enrichment Analysis (GSEA), molecular network regulation, immune infiltration profiling, and drug prediction. Subsequently, the expression profiles of these biomarkers were quantitatively assessed through reverse transcription quantitative PCR (RT-qPCR) methodology. ResultsFour feature genes were identified using machine learning. Among them, CXCR2 and SIGLEC7 passed expression validation and were significantly up-regulated in DFU groups across two datasets (p 0, AUC = 0.99). GSEA indicated that CXCR2 and SIGLEC7 were both enriched in the cytokine-cytokine receptor interaction pathway as well as other functionally associated pathways. Molecular regulatory network analysis indicated that both CXCR2 and SIGLEC7 were predicted to be regulated by transcription factors (TFs) such as SOX2. Immune infiltration analysis showed a significant strong positive correlation between CXCR2 and activated mast cells (correlation coefficient (cor) = 0.72, p < 0.001), and between SIGLEC7 and macrophages M0 (cor = 0.71, p < 0.001). Drug prediction identified 12 drugs targeting CXCR2 (e.g., Indoprofen) and one drug targeting SIGLEC7 (N-acetyl-alpha-D-glucosamine). Validation through Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) revealed that CXCR2 and SIGLEC7 expression levels were markedly increased in the DFU group, showing statistically significant differences (p<0.05), which aligned with the bioinformatics analysis outcomes ConclusionThis study identified CXCR2 and SIGLEC7 as SASPRG-related biomarkers in DFU through bioinformatic analysis, providing new theoretical support and a basis for early diagnosis and treatment of DFU.