Machine Learning-Based Identification and Experimental Validation of Hub Ferroptosis-Related Cuproptosis Genes in Lupus Nephritis

Background: The role of Ferroptosis and Cuproptosis in lupus nephritis (LN) is unclear. The aim of this study was to explore the expression and effects of ferroptosis-related Cuproptosis genes (FRCGs) in LN using bioinformatics and experimental validation.

Methods: The LN-related datasets GSE112943 and GSE32591 were downloaded from the GEO database. We collected 834 ferroptosis-related genes and 1046 cuproptosis-related genes. Weighted gene co-expression network analysis (WGCNA) and machine learning algorithms identified hub FRCGs in the LN. We then analyzed the relationship of hub FRCGs with immune infiltration and clinical traits. Finally, we validated the expression of the hub FRCGs in vivo and in vitro.

Results: A total of 31 differentially expressed FRCGs (DE-FRCGs) in the LN were screened, which were mainly involved in the response to metal ions and oxidative stress. And they were engaged in autophagy-animal signaling pathway. Machine learning identified two hub FRCGs (JUN and ZFP36). Public datasets, clinical samples, in vivo and in vitro experiments confirmed that hub FRCGs expression was reduced in the LN group compared to control group. Immune infiltration analysis displayed that monocytes and macrophages were the major infiltrating cells in LN kidneys. In LN, hub genes were negatively correlated with macrophages and monocytes.

Conclusion: This study elucidates the contribution of ferroptosis-related Cuproptosis genes to the onset and progression of LN. JUN and ZFP36 were potentially effective biomarkers of LN and may be potential targets for LN therapy in the future.

cuproptosis; ferroptosis; immune infiltration; lupus nephritis; machine learning.