Journal of diabetes and its complications · 2025
Elaboration and verification of immune-based diagnostic biomarker panel for diabetic foot ulcer.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Gao Hengkun, Chen Sibing, Li Jiannan, Liu Yanxi
Аннотация
BACKGROUND: Diabetic foot ulcer (DFU) constitutes a major complication in diabetes management. This study aimed to develop and validate an immune-related diagnostic model for DFU by identifying key genes and analyzing their functional enrichment.
METHODS: We utilized the datasets GSE199939, GSE134431, and GSE80178 from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify gene modules associated with DFU. Differentially expressed genes (DEGs) were pinpointed using the "limma" package, and functional enrichment was executed using "clusterProfiler". A risk score for diagnosing DFU was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) model. The CIBERSORT algorithm was utilized to assess immune cell infiltration. The diagnostic effectiveness of the risk score was gauged through the receiver operating characteristic (ROC) curve, and drug target prediction was performed using the DGIdb database.
RESULTS: WGCNA identified a DFU-related gene module containing 2184 genes. Functional enrichment analysis revealed important pathways, including proteasome and cell cycle. Nine DEGs were recognized as immune-related candidates for DFU, predominantly involved in signaling cascades like cytokine-cytokine receptor interaction. The LASSO model selected four key genes (APOD, ULBP2, TGFBR3, TNFRSF12A) to construct a risk score, which showed high diagnostic accuracy in datasets GSE134431, GSE199939 and GSE80178 (AUC = 0.990, 1.000, and 0.926, respectively). Pronounced disparities in infiltrating immune cells were observed among DFU patient groups with disparate risk factors. Drug prediction analyses identified potential therapeutic targets for the key genes.
CONCLUSION: This study developed a powerful immune-related diagnostic model for DFU, highlighting the key genes and pathways involved in its pathogenesis. The risk score provides a valuable tool for DFU diagnosis, while the identified drug targets provide avenues for potential therapeutic intervention.
METHODS: We utilized the datasets GSE199939, GSE134431, and GSE80178 from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify gene modules associated with DFU. Differentially expressed genes (DEGs) were pinpointed using the "limma" package, and functional enrichment was executed using "clusterProfiler". A risk score for diagnosing DFU was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) model. The CIBERSORT algorithm was utilized to assess immune cell infiltration. The diagnostic effectiveness of the risk score was gauged through the receiver operating characteristic (ROC) curve, and drug target prediction was performed using the DGIdb database.
RESULTS: WGCNA identified a DFU-related gene module containing 2184 genes. Functional enrichment analysis revealed important pathways, including proteasome and cell cycle. Nine DEGs were recognized as immune-related candidates for DFU, predominantly involved in signaling cascades like cytokine-cytokine receptor interaction. The LASSO model selected four key genes (APOD, ULBP2, TGFBR3, TNFRSF12A) to construct a risk score, which showed high diagnostic accuracy in datasets GSE134431, GSE199939 and GSE80178 (AUC = 0.990, 1.000, and 0.926, respectively). Pronounced disparities in infiltrating immune cells were observed among DFU patient groups with disparate risk factors. Drug prediction analyses identified potential therapeutic targets for the key genes.
CONCLUSION: This study developed a powerful immune-related diagnostic model for DFU, highlighting the key genes and pathways involved in its pathogenesis. The risk score provides a valuable tool for DFU diagnosis, while the identified drug targets provide avenues for potential therapeutic intervention.