An exploratory analysis of disulfidptosis-related gene signatures in minimal change disease identifies metabolic and immune associations.

Aim: We aimed to investigate the association between genes related to disulfidptosis-a form of cell death caused by aberrant disulfide stress and cytoskeletal collapse-and the molecular features of minimal change disease (MCD), the leading cause of primary nephrotic syndrome. Methods: Data from the GeneCards and Gene Expression Omnibus (GEO) databases were integrated to systematically analyze disulfidptosis-related genes in MCD. Machine learning approaches-including generalized linear model (GLM), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)-pinpointed four hub genes. These genes were used to construct a diagnostic nomogram. Molecular groups were defined by consensus clustering, and pathway alterations were explored through gene set variation analysis and gene set enrichment analyses. Gene expression was validated by immunohistochemistry (IHC) and immunofluorescence (IF). Results: The diagnostic model based on Conclusion: This study indicates a potential link between disulfidptosis-related genes and MCD, and presents an exploratory diagnostic and molecular classification system that requires further validation in larger cohorts.