Journal of diabetes investigation · 2025
Identification and validation of novel inflammatory response-related diagnostic biomarkers in diabetic foot ulcers.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Cai Sen, Xu Lingtao, Wan Shaole, Hu Zengyong, Shen Yonghui
Аннотация
BACKGROUND: Diabetic foot ulcers (DFU) have become a global health issue. Chronic inflammation is a key pathological feature of DFU; however, its role in the diagnosis of DFU remains unclear.
METHODS: DFU sample data were acquired from public databases, and weighted gene co-expression network analysis (WGCNA) was employed to identify key genes associated with DFU traits. A protein-protein interaction (PPI) network was constructed to screen for diagnostic biomarkers, and receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic efficacy of these key genes. The non-negative matrix factorization algorithm was applied to classify DFU samples. To further investigate the potential molecular mechanisms of these diagnostic genes, single-gene functional enrichment analysis, single-sample gene set enrichment analysis (ssGSEA) for immune infiltration, and construction of upstream transcription factor regulatory networks were conducted.
RESULTS: Four feature genes (FOSL1, HBEGF, S100A12, and TICAM1) demonstrating diagnostic potential were identified. ROC curve analysis validated these genes as potential diagnostic biomarkers for DFU. Functional enrichment analysis indicated that these genes are predominantly involved in biological processes related to keratinization and differentiation of epidermal skin cells. Furthermore, the expression levels of these feature genes exhibited a significant negative correlation with the infiltration levels of most immune cells. The regulatory network of upstream transcription factors highlighted the interconnectedness of these feature genes.
CONCLUSION: This study identified DFU diagnostic genes linked to the inflammatory response, revealing their distinct transcriptional profiles and functional enrichment. These discoveries advance understanding of DFU pathology and provide a theoretical foundation for early diagnosis and treatment.
METHODS: DFU sample data were acquired from public databases, and weighted gene co-expression network analysis (WGCNA) was employed to identify key genes associated with DFU traits. A protein-protein interaction (PPI) network was constructed to screen for diagnostic biomarkers, and receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic efficacy of these key genes. The non-negative matrix factorization algorithm was applied to classify DFU samples. To further investigate the potential molecular mechanisms of these diagnostic genes, single-gene functional enrichment analysis, single-sample gene set enrichment analysis (ssGSEA) for immune infiltration, and construction of upstream transcription factor regulatory networks were conducted.
RESULTS: Four feature genes (FOSL1, HBEGF, S100A12, and TICAM1) demonstrating diagnostic potential were identified. ROC curve analysis validated these genes as potential diagnostic biomarkers for DFU. Functional enrichment analysis indicated that these genes are predominantly involved in biological processes related to keratinization and differentiation of epidermal skin cells. Furthermore, the expression levels of these feature genes exhibited a significant negative correlation with the infiltration levels of most immune cells. The regulatory network of upstream transcription factors highlighted the interconnectedness of these feature genes.
CONCLUSION: This study identified DFU diagnostic genes linked to the inflammatory response, revealing their distinct transcriptional profiles and functional enrichment. These discoveries advance understanding of DFU pathology and provide a theoretical foundation for early diagnosis and treatment.