Identification of disulfidptosis-related genes in immunity and immunotherapy in diabetic foot ulcer.

Background: Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Disulfidptosis, a recently identified form of programmed cell death, might be involved in the development of diabetic complications. This study aims to identify and validate potential disulfidptosis biomarkers associated with DFU through bioinformatics and machine learning analysis. Methods: We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism-related genes (deDRGs) between DFU and normal controls. We analyzed the correlation between deDRGs and immune cell infiltration status. We also explored the relationship between DRG molecular clusters and immune cell infiltration status. Notably, we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify differentially expressed genes within specific clusters. We used Gene Set Variation Analysis (GSVA) to explore which pathways might be related to the DRGs. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis, and the GSE80178 and GSE68183 datasets. Results: In both the DFU and normal control groups, we confirmed the presence of deDRGs and an activated immune response. From the GSE134431 dataset, we obtained 33 deDRGs, including MYH10, MYL6, UBASH3B, SLC7A11, DSTN, CD2AP, ME1, OXSM, NDUFC1, GYS1, SCO2, NLN, HNRNPH2, MRPS17, SART3, SAFB2, SAFB, HNRNPU, HNRNPM, MYH14, GTF2I, MYH3, CNOT1, PCBP2, GLUD1, MYH11, TLN2, CHD4, SQSTM1, NDUFB11, NDUFS2, SAMM50, and PPIH. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a support vector machine model based on five genes (RALY, R3HCC1, CES1, TCEAL3, and F13A1), which exhibited excellent performance on the external validation datasets GSE80178 and GSE68183 (AUC = 1). Conclusion: This study has identified five disulfidptosis genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.