Abstract
Renal ischemia-reperfusion injury (IRI) is an inevitable process in kidney transplantation, leading to acute kidney injury, delayed graft function (DGF), and even graft loss. Ferroptosis is an iron-dependent regulated cell death in various diseases including IRI. We aimed to identify subtypes of renal IRI and construct a robust DGF predictive signature based on ferroptosis-related genes (FRGs). A consensus clustering analysis was applied to identify ferroptosis-associated subtypes of 203 renal IRI samples in the GSE43974 dataset. The FRG-associated DGF predictive signature was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO), and its robustness was further verified in the validation set GSE37838. The present study revealed two ferroptosis-related patient clusters (pBECN1 and pNF2 cluster) in renal IRI samples based on distinct expression patterns of BECN1 and NF2 gene clusters. Cluster pBECN1 was metabolically active and closely correlated with less DGF, while pNF2 was regarded as the metabolic exhausted subtype with higher incidence of DGF. Additionally, a six-gene (ATF3, SLC2A3, CXCL2, DDIT3, and ZFP36) ferroptosis-associated signature was constructed to predict occurrence of DGF in renal IRI patients and exhibited robust efficacy in both the training and validation sets. High-risk patients tended to have more infiltration of dendritic cells, macrophages, and T cells, and they had significantly enriched chemokine-related pathway, WNT/β-catenin signaling pathway, and allograft rejection. Patients with low risks of DGF were associated with ferroptosis-related pathways such as glutathione and fatty acid metabolism pathways. In conclusion, patient stratification with distinct metabolic activities based on ferroptosis may help distinguish patients who may respond to metabolic therapeutics. Moreover, the DGF predictive signature based on FRGs may guide advanced strategies toward prevention of DGF in the early stage.
Highlights
Kidney transplantation is the major treatment for patients with endstage renal disease
The gene expression profiles of kidney tissues obtained after ischemia-reperfusion (IRI group) in kidney transplantation and kidney tissues obtained before retrieval were analyzed using the GSE43974 dataset from the Gene Expression Omnibus (GEO) database
To identify differentially expressed genes (DEG) in renal ischemia-reperfusion injury (IRI), the GSE43974 geneset, which consisted of 203 kidney tissues obtained after reperfusion (IRI group) and 188 kidney tissues obtained before organ retrieval, was used to perform the differential expression analysis
Summary
Kidney transplantation is the major treatment for patients with endstage renal disease. Ischemiareperfusion injury (IRI) is an inevitable process in kidney transplantation, during which the cold ischemia, warm ischemia, and subsequent reperfusion result in dysfunction of renal allografts (Smith et al, 2019). The major damage caused by IRI is the loss of function in tubular epithelial cells, which contributes to acute kidney injury (AKI), delayed graft function (DGF), and loss of allografts (Saat et al, 2016). Irish et al (2010) proposed a nomogram integrating donor’s and recipient’s risk factors to predict DGF. It is far from satisfactory, and better predictive tools are needed
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