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Background <p>Although the precise regulatory mechanisms underlying diabetic foot (DF) remain incompletely understood, increasing evidence suggests that mitochondrial metabolism may be involved in DF progression. The study aimed to identify and validate candidate biomarkers associated with mitochondrial metabolism in DF.</p> Methods <p>Transcriptome sequencing data from 12 DF and 18 control tissue samples were analyzed together with 1,234 mitochondrial metabolism-related genes (MMRGs), which were obtained from MSigDB by integrating curated gene sets directly associated with mitochondrial metabolic processes. First, overlapping genes between MMRGs and DEGs between DF and control samples were used as candidate genes. Then, candidate biomarkers were identified using PPI network construction, two machine learning algorithms, and ROC curve analysis. Next, based on the candidate biomarkers, functional enrichment, immune infiltration, regulatory network, compound prediction, as well as molecular docking analyses were undertaken. Finally, to explore the expression of candidate biomarkers in clinical samples, the reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed.</p> Results <p>GPAT3 and PTGS2 were identified as mitochondrial metabolism-associated candidate biomarkers for DF, with area under the curve values of 0.963 and 0.958, respectively. Functional enrichment analysis showed that multiple pathways were significantly enriched by both GPAT3 and PTGS2, such as ribosome and Leishmania infection. GPAT3 had the strongest positive and negative correlations with neutrophils and resting CD4 memory T cells, respectively. In contrast, PTGS2 exhibited the strongest negative connection with M1 macrophages and the strongest positive correlation with neutrophils. Subsequently, multiple transcription factors (TFs) were found to co-target GPAT3 and PTGS2, such as BRD4 and NR0B1. Moreover, compound prediction analysis identified several compounds potentially associated with these biomarkers, such as lipopolysaccharides and cisplatin. The binding free energy between PTGS2 and tetradecanoylphorbol acetate was − 8.2&#xa0;kcal/mol, while the binding free energy between GPAT3 and cyclosporine was − 10.4&#xa0;kcal/mol, according to molecular docking data. Finally, the expression levels of GPAT3 and PTGS2 were significantly different in clinical samples.</p> Conclusion <p>GPAT3 and PTGS2 were identified as candidate biomarkers associated with mitochondrial metabolism in DF, providing molecular clues for further investigation of DF pathogenesis and potential intervention strategies.</p>

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Identification and validation of candidate biomarkers associated with mitochondrial metabolism in diabetic foot

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • Haifeng Li,
  • Yijie Ning,
  • Chuanlong Lu,
  • Yuhang Zhang,
  • Sheng Yan,
  • Wenkai Chang,
  • Haijiang Jin,
  • Qinqin Tian,
  • Yongbin Shi,
  • Maolin Qiao,
  • Yudan Zhang,
  • Honglin Dong

摘要

Background

Although the precise regulatory mechanisms underlying diabetic foot (DF) remain incompletely understood, increasing evidence suggests that mitochondrial metabolism may be involved in DF progression. The study aimed to identify and validate candidate biomarkers associated with mitochondrial metabolism in DF.

Methods

Transcriptome sequencing data from 12 DF and 18 control tissue samples were analyzed together with 1,234 mitochondrial metabolism-related genes (MMRGs), which were obtained from MSigDB by integrating curated gene sets directly associated with mitochondrial metabolic processes. First, overlapping genes between MMRGs and DEGs between DF and control samples were used as candidate genes. Then, candidate biomarkers were identified using PPI network construction, two machine learning algorithms, and ROC curve analysis. Next, based on the candidate biomarkers, functional enrichment, immune infiltration, regulatory network, compound prediction, as well as molecular docking analyses were undertaken. Finally, to explore the expression of candidate biomarkers in clinical samples, the reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed.

Results

GPAT3 and PTGS2 were identified as mitochondrial metabolism-associated candidate biomarkers for DF, with area under the curve values of 0.963 and 0.958, respectively. Functional enrichment analysis showed that multiple pathways were significantly enriched by both GPAT3 and PTGS2, such as ribosome and Leishmania infection. GPAT3 had the strongest positive and negative correlations with neutrophils and resting CD4 memory T cells, respectively. In contrast, PTGS2 exhibited the strongest negative connection with M1 macrophages and the strongest positive correlation with neutrophils. Subsequently, multiple transcription factors (TFs) were found to co-target GPAT3 and PTGS2, such as BRD4 and NR0B1. Moreover, compound prediction analysis identified several compounds potentially associated with these biomarkers, such as lipopolysaccharides and cisplatin. The binding free energy between PTGS2 and tetradecanoylphorbol acetate was − 8.2 kcal/mol, while the binding free energy between GPAT3 and cyclosporine was − 10.4 kcal/mol, according to molecular docking data. Finally, the expression levels of GPAT3 and PTGS2 were significantly different in clinical samples.

Conclusion

GPAT3 and PTGS2 were identified as candidate biomarkers associated with mitochondrial metabolism in DF, providing molecular clues for further investigation of DF pathogenesis and potential intervention strategies.