Frontiers in cell and developmental biology · 2026
An exploratory analysis of disulfidptosis-related gene signatures in minimal change disease identifies metabolic and immune associations.
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Li Jiahui, Li Quhuan, Shen Yue, Nie Chaohong, Li Jiao, Zhang Fengxia
Аннотация
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 FLNC, GYS1, INF2, and MYH11 demonstrated exploratory discriminatory performance (AUC = 0.839). Consensus clustering based on the four hub genes suggested two potential molecular groups (C1 and C2), and their differences in pathway enrichment and immune characteristics were explored. MYH11 expression was markedly higher in the C2 group and correlated with cytoskeletal remodeling and immune modulation. Experimental validation confirmed significant upregulation of MYH11 in MCD.
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.
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 FLNC, GYS1, INF2, and MYH11 demonstrated exploratory discriminatory performance (AUC = 0.839). Consensus clustering based on the four hub genes suggested two potential molecular groups (C1 and C2), and their differences in pathway enrichment and immune characteristics were explored. MYH11 expression was markedly higher in the C2 group and correlated with cytoskeletal remodeling and immune modulation. Experimental validation confirmed significant upregulation of MYH11 in MCD.
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.