PPARG-centered regulatory network of ferroptosis in lupus nephritis: insights by integrated comprehensive bioinformatics analysis and machine learning.

Abstract

Introduction: Ferroptosis has garnered attention as a mechanism of cell death contributing to lupus and lupus nephritis pathogenesis. However, the precise locations of occurrence, mechanisms triggering disease progression, and critical targets remain unclear.

Materials and methods: Differentially expressed genes were identified using the "limma" package in R. Weighted gene co-expression network analysis was applied to explore gene modules associated with LN. Ferroptosis-related genes were obtained from FerrDb V2 and intersected with DEGs and WGCNA modules to identify candidate genes. Hub genes were selected using LASSO and Random Forest algorithms, followed by ROC curve validation. Immune cell infiltration was analyzed using the CIBERSORT algorithm, and correlations with hub gene expression were assessed. A protein-protein interaction network was constructed via STRING. Finally, RT-qPCR was performed to validate the expression of selected genes in kidney tissues from MRL/lpr and C57BL/6 mice.

Results: Differential expression gene analysis and weighted gene co-expression network analysis identified 688 LN-related genes in PBMC, 625 in the renal tubulointerstitium, and 1428 in renal glomeruli. The LASSO and Random Forest algorithms selected hub genes associated with ferroptosis and were validated through ROC analysis. Immunocyte infiltration analysis revealed differential patterns in different tissues, with most hub genes highly correlated with immune cell infiltrations. PPI analysis and RT-qPCR validation identified a PPARG-centered regulatory network (including PPARG, CDKN1A, NR4A1, ATF3, DUSP1 and PDK4) that may be crucial for the regulation of ferroptosis in lupus nephritis.

Conclusion: This study reveals, for the first time, the mechanisms and regulatory hub genes of ferroptosis in different LN tissues. The regulatory network centered around PPARG may play a crucial role in ferroptosis in LN, providing a new perspective for in-depth investigation into LN pathogenesis and targeted therapy development. Key Points • Identified tissue-specific ferroptosis biomarkers in lupus nephritis using multiple machine learning methods. • The diagnostic efficacy of the PPARG regulatory network was validated through both internal and external validation. • Discovered the regulatory network of ferroptosis in lupus nephritis by constructing the PPARG regulatory network.