Changhong Xu,1,* Yun Deng,1,* Xinyi Gong,2,* Huabin Wang,1 Jiangwei Man,1 Hailong Wang,1 Kun Cheng,1 Huiming Gui,1 Shengjun Fu,1 Shenghu Wei,3 Xiaoling Zheng,4 Tuanjie Che,4 Liyun Ding,3 Li Yang1 

1Department of Urology, Institute of Urology, Gansu Province Clinical Research Center for Urinary System Disease, Lanzhou University Second Hospital, Lanzhou, Gansu, 730030, People’s Republic of China; 2The Second Clinical Medical College of Lanzhou University, Lanzhou, Gansu, 730030, People’s Republic of China; 3School of Physical Science and Technology, Lanzhou University, Lanzhou, 730000, People’s Republic of China; 4Innovation Center of Functional Genomics and Molecular Diagnostics Technology of Gansu Province, Lanzhou, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Li Yang; Liyun Ding, Email ery_yangli@lzu.edu.cn; dingly@lzu.edu.cn

Background: Renal ischemia-reperfusion injury (RIRI) is a significant cause of acute kidney injury, complicating clinical interventions such as kidney transplants and partial nephrectomy. Recent research has indicated the role of cuproptosis, a copper-dependent cell death pathway, in various pathologies, but its specific involvement in RIRI remains insufficiently understood. This study aims to investigate the role of cuproptosis-related genes in RIRI and establish robust diagnostic models.
Methods: We analyzed transcriptomic data from 203 RIRI and 188 control samples using bioinformatics tools to identify cuproptosis-related differentially expressed genes (CRDEGs). The relationship between CRDEGs and immune cells was explored using immune infiltration analysis and correlation analysis. Gene Set Enrichment Analysis (GSEA) was conducted to identify pathways associated with CRDEGs. Machine learning models, including Least Absolute Shrinkage and Selection Operator(LASSO) logistic regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), Clustering analysis, and weighted gene co-expression network analysis (WGCNA), were used to construct diagnostic gene models. The models were validated using independent datasets. Experimental validation was conducted in vivo using a mouse bilateral RIRI model and in vitro using an HK-2 cell hypoxia-reoxygenation (HR) model with copper chelation intervention. HE, PAS, and TUNEL staining, along with plasma creatinine and blood urea nitrogen (BUN) measurements, were used to evaluate the protective effect of the copper chelator D-Penicillamine (D-PCA) on RIRI in mice. JC-1 and TUNEL staining were employed to assess apoptosis in HK-2 cells under hypoxia-reoxygenation conditions. Immunofluorescence and Western blot (WB) techniques were used to verify the expression levels of the SDHB and NDUFB6 genes.
Results: A total of 18 CRDEGs were identified, many of which were significantly correlated with immune cell infiltration. GSEA revealed that these genes were involved in pathways related to oxidative phosphorylation and immune response regulation. Four key cuproptosis marker genes (LIPA, LIPT1, SDHB, and NDUFB6) were incorporated into a Cuproptosis Marker Gene Model(CMGM), achieving an area under the curve (AUC) of 0.741– 0.834 in validation datasets. In addition, a five-hub-gene SVM model (MOAP1, PPP2CA, SYL2, ZZZ3, and SFRS2) was developed, demonstrating promising diagnostic performance. Clustering analysis revealed two RIRI subtypes (C1 and C2) with distinct molecular profiles and pathway activities, particularly in oxidative phosphorylation and immune responses. Experimental results showed that copper chelation alleviated renal damage and cuproptosis in both in vivo and in vitro models.
Conclusion: Our study reveals that cuproptosis-related genes are significantly involved in RIRI, particularly influencing mitochondrial dysfunction and immune responses. The diagnostic models developed showed promising predictive performance across independent datasets. Copper chelation demonstrated potential therapeutic effects, suggesting that cuproptosis regulation may be a viable therapeutic strategy for RIRI. This work provides a foundation for further exploration of copper metabolism in renal injury contexts.

Keywords: cuproptosis, renal ischemia-reperfusion injury, RIRI, gene diagnostic models, copper chelators, bioinformatics analysis