The RNA expression data, which included 4 IRI groups and 4 Sham groups, were collected from GEO (http://www.ncbi.nlm.nih.gov/geo/) database with series numbers GSE148420. The GSE21374, GSE58438 and GSE126805 datasets were also obtained from the GEO database. The dataset GSE58438 contains 19 samples from 4 experimental groups, including 5 Sham group samples, five IRI 3h samples, four IRI 24h samples, and five IRI 120h samples. The dataset GSE126805 includes samples from 42 kidney transplant recipients at 4 different time points, namely before transplant, after transplant, 3 months after transplant, and 1 year after transplant. The dataset GSE21374 includes gene expression data and graft survival information from renal transplant patients, comprising 51 cases of allograft loss and 231 cases of allograft survival. These datasets cover various stages of ischemia-reperfusion injury, but unfortunately, they come from different species. Ferroptosis-related genes(FRGs), including drive, suppress, or mark gene, were obtained from the FerrDb (http://www.zhounan.org/ferrdb) database. A flowchart of this study is shown in Figure 1 .

Eighteen SPF-grade Balb/c mice were selected, aged 6-8 weeks, weighing 20-25g (purchased from Hunan SJA Laboratory Animal Co., Ltd), and were randomly divided into Sham group, IR-AKI group and IR+Anti-CD44 group. The IR+Anti-CD44 group was injected with CD44 monoclonal antibody (100ug) 30 minutes before ischemia procedure. The surgical incision was made approximately 0.5 cm to the left or right of the midline on the back of the mouse to expose the kidney. The right kidney was removed from all groups, the Sham group exposed the left kidney without clamping, and the IR group exposed the left kidney and rapidly clamped the left renal pedicle with an atraumatic microvascular clamp. Successful clamping was indicated by a rapid change in kidney color from dark red to dark purple. After 45 minutes of ischemia, releasing the clamp demonstrated a change in kidney color from purple-black back to red, indicating successful reperfusion. The reperfusion time was 24 hours. This study has been approved by the Animal Ethics Committee of Guangxi University of Chinese Medicine (Approval No.: DW20220529-216).

Hematoxylin and eosin (HE) staining and PAS staining was used to detect pathological damage in the kidney tissue. All steps were performed according to manufacturer’s instructions. The injury scoring was recorded with blind assessment method, with the following scores: 0, normal; 1, mild; 2, moderate; 3, severe.

Differentially-expressed genes (DEGs) between IRI-AKI and Sham group were acquired by the “Limma” package in R software. The absolute value of Log2Fold > 1.0 and adj.P<0.05 were used as significance indicators. DEGs were visualized by volcanic maps using the “ggplot2” package in R software. The top 50 DEGs were shown by heat map based on the “pheatmap” package in R software. The intersection of DEGs and FRGs obtained in the FerrDb Database is defined as differentially expressed ferroptosis-related genes (DEFRGs). The number of DEFRGs was shown in a Venn diagram using the online tool “jvenn” (https://jvenn.toulouse.inrae.fr) (7).

To reveal the underlying biological functions and underlying mechanisms of DEFRGs, we used the Sangerbox (http://sangerbox.com/) online analysis tool for gene ontology (GO) and Kyoto Encyclopedia of Gene and Genes (KEGG) enrichment analysis of DEFRGs. GO enrichment analysis includes biological processes (BPs), cellular components (CCs) and molecular functions (MFs); adj. P<0.05 is the threshold for screening the main enrichment functions and pathways of DEFRGs.

To further explore the Protein–Protein interactions(PPI) among DEFRGs, we constructed a PPI network of DEFGRs using the online tool “String” (http://www.string-db.org/) (Version 11.5)database. We set 0.4 (medium confidence) as minimum interaction score. We identified key genes in the PPI network of DEFGRs using four algorithms from the Cytohubba plug-in. The four algorithms are Degree, Density of Maximum Neighborhood Component(DMNC), Edge Percolated component (EPC) and Maximum neighborhood component (MNC). Top 10 key genes were defined as hubgenes. Then the Hubgenes obtained from the four algorithms was intersected with each other, and the intersection genes were defined as the most valuable ferroptosis-related Hub genes (FRHGs) for renal IRI.

To further validate the expression level of FRHGs in GSE148420, the external datasets GSE58438, GSE126805 were used as external validation sets. we performed a statistical analysis on the expression of the FRHGs between each group in the external validation set, and P<0.05 was considered statistically significant. Secondly, Proteomics and Western blot is used to validate the expression levels of key proteins. Proteomic sequencing is performed by Novogene Co. Ltd. Bioinformatics analysis was done with R software. An appropriate amount of liver tissue is taken and added to 500 μL of protein lysis buffer, then ground using a fully automatic cryogenic grinder. The resulting mixture is then centrifuged at low temperature and high speed (4°C,12,000 r/min) for 10 minutes, and the supernatant is collected to obtain the extracted tissue protein solution. After quantifying the protein using the BCA method, the protein is mixed with protein loading buffer at a 4:1 ratio and boiled at 100°C for 10 minutes. 60 μg of the sample is loaded per lane, and the protein extracts are separated by polyacrylamide gel electrophoresis (PAGE) in the electrophoresis solution. The proteins are then transferred to a PVDF membrane using the “wet transfer method.” The membrane is blocked with 5% skim milk at room temperature for 1 hour, followed by respective incubation with primary and secondary antibodies, and then visualized and analyzed using Imaging J.

R package “Cibersort” was used to calculate the proportion of 22 different immune cells type in GSE148420 datasets (8). Spearman’s correlation analysis between infiltrating immune cells and FRHGs was calculated using “Corrplot” in R software. Correlation analysis result was shown in the dot plot.

We further investigated the effects of FRHGs on long-term allograft survival through the utilization of the GSE21374 dataset. The Sangerbox online tool was used to plot survival curves.

We use the drug gene interactions database (DGIdb, www.dgidb.org) to predict drugs and molecular compounds that may interact with FRHGs. The Cytoscape software is used to visualize the drug-gene interaction network.

Renal function was evaluated through the measurement of serum creatinine(Scr) and blood urea nitrogen(BUN)levels, conducted in accordance with the manufacturer’s protocol (Jiancheng, Nanjing, China).

For the Malondialdehyde (MDA) assay, an appropriate quantity of tissue sample was weighed, thoroughly lysed, and centrifuged to yield 100 μL of supernatant. The assay was subsequently performed following the provided kit instructions (Solarbio, China), with absorbance recorded at 532 nm and 600 nm using a microplate reader. The assessment of Glutathione (GSH) was conducted in accordance with the guidance of the GSH detection kit (Solarbio, China), measuring absorbance at 412 nm. All procedures were conducted on ice to preserve sample integrity.

Tissue samples were fixed in 4% paraformaldehyde and rehydrated through a graded series of ethanol solutions. To block non-specific binding, sections were incubated at room temperature for 30 minutes in 10% goat serum. Subsequently, the sections were exposed to primary antibodies—mouse monoclonal antibodies against CD11b (EPR1344, Abcam), CD86 (EPR28721, Abcam), and GPX4 (67763-1-Ig, Proteintech)-and incubated overnight at 4°C. Following this, the samples were incubated with a secondary antibody for 30 minutes at room temperature. For immunohistochemistry (IHC), HRP-conjugated secondary antibodies were employed, while fluorescently labeled secondary antibodies were utilized for immunofluorescence (IF). Microscopic examination of the samples was conducted to assess staining.

Apoptotic cells were identified using a TUNEL Apoptosis Assay Kit (K1123, APExBIO), following the manufacturer’s instructions. The average number of TUNEL-positive cells was quantified from five randomly selected fields under a fluorescence microscope (BX51, OLYMPUS, Japan) and expressed as a percentage of the total cell nuclei.

Small pieces of renal cortex were excised and fixed in 2.5% glutaraldehyde. Following fixation, the samples were dehydrated and embedded. Ultra-thin sections were stained and subsequently examined using a transmission electron microscope (HITACHI HT7800, Japan).

All bioinformatics analyses were performed using R software; Statistical analyses of experimental data were processed by Prism software 9.0 (GraphPad software, La Jolla, CA). **** represents P<0.0001, *** represents P<0.001, ** represents P<0.01, * represents P<0.05.

In order to study the differentially expressed FRGs in IRI-AKI, 484 FRGs were extracted from FerrDb. FerrDb is a database of markers and diseases related to ferroptosis, which includes ferroptosis markers, drivers, and suppressors. Differential expression analysis of the GSE148420 dataset showed that 2,468 genes were significantly differentially expressed in IRI-AKI group compared to sham group, with a threshold of |log2FC|≥ 1 and adj.P < 0.05 ( Figure 2A ). After taking the intersection of DEGs and FRGs, a total of 60 differentially expressed FRGs were defined as DEFRGs ( Figure 2B ), which were presented in a Venn diagram.

We investigated the potential biological function and pathways of DEFRGs based on GO and KEGG pathway analysis. The results were presented in a dot plot. GO-BP analysis revealed that the DEFRGs were mainly enriched in “small molecule metabolic process”,”response to lipid”,” response to endogenous stimulus”[biological process, (BP)] ( Figure 3A ). GO-MF analysis revealed that the DEFRGs were mainly enriched in “oxidoreductase activity”, “oxidoreductase activity acting on NADP(H)”,”iron ion binding” ( Figure 3B ). In the GO-CC, DEFRGs were mostly enriched in the “chromatin”,” apical part of cell”, “apical plasma membrane”,” basolateral plasma membrane”,” basal part of cell”,” tertiary granule membrane” “chromatin silencing complex” “NADPH oxidase complex “, “perinuclear endoplasmic reticulum” [cellular component, (CC)] ( Figure 3C ). According to KEGG enrichment analysis, these DEFRGs were mainly enriched in the “HIF-1 signaling pathway”, “ Toll-like receptor signaling pathway”, “NOD-like receptor signaling pathway” and “Ferroptosis” ( Figure 3D ).

To explore the interactions between each DEFRG, all DEFRGs were submitted to the STING database to construct a PPI network. The DEFRGs PPI network included 59 nodes and 106 edges ( Figure 4A ). Subsequently, the PPI data was imported into Cytoscape, and four algorithms, namely Degree ( Figure 4B ), DMNC ( Figure 4C ), MNC ( Figure 4D ), and EPC ( Figure 4E ), were applied in Cytohubba to analyze hubgenes. The top 10 hubgenes obtained from the four algorithms were intersected, resulting in six ferroptosis-related hubgenes (FRHGs), including ATF3, EGR1, PPARA, CDKN1A, CD44, and EZH2 ( Figure 4F ).

Through the above analysis process, we identified as EZH2, CDKN1A, PPARA, EGR1, ATF3, and CD44 as FRHGs. The datasets GSE58438 and GSE126805 were used to further confirm the expression levels of the FRHGs at different time points. In GSE58438, ATF3, EGR1, CDKN1A, and CD44 were significantly upregulated as early as 3 hours after renal IRI, where ATF3 and CD44 continued to increase for up to 120 hours. On the other hand, PPARA showed a decreasing trend at 3 hours after renal IRI, which continued to decrease for up to 120 hours. The increase in EZH2 expression was observed at around 120 hours ( Figure 5A ). In GSE126805, a similar trend was observed for ATF3, EGR1, and CDKN1A, whereas CD44 and EZH2 continued to increase even up to 1 year after renal IRI ( Figure 5B ). Interestingly, PPARA showed an increasing trend at 3 months after IR, which continued to increase for up to 1 year. To further understand the role of FRHGs in IRI-AKI, GSE21374 was utilized to analyze the relationship between FRHGs and renal allograft survival. The results showed that high expression levels of EZH2, CDKN1A, EGR1, ATF3, and CD44 were all associated with poor prognosis, while low expression of PPARA was associated with poor prognosis ( Figure 5C ). These experimental results indicate that EZH2, ATF3, EGR1, CDKN1A, and CD44 can be upregulated early after IR and their sustained high expression levels are associated with poor prognosis, while PPARA initially decreases in expression but can gradually recover, and its increased expression levels are associated with good prognosis.

In the GO and KEGG enrichment analysis of the DEFRGs, immune cells appear to play a crucial role in the process of renal IRI. To further confirm the role of immune cells, we conducted immune infiltration analysis and found significant differences (P<0.05) in seven immune cells between the IRI and Sham groups, namely: Dendritic cells activated, Dendritic cells resting, Macrophages M0, Macrophages M1, Macrophages M2, Monocytes, and Plasma. cells ( Figures 6A, B ). To better understand the role of FRHGs in immune infiltration, we used Spearman correlation analysis to determine their association with immune cell infiltration. The correlation analysis showed that EZH2, CDKN1A, PPARA, EGR1, ATF3, and CD44 were strongly correlated with all seven immune cells. Among them, EZH2 showed the strongest negative correlation with Macrophages.M0; CD44 showed strong positive correlation with Macrophages M1 and Dendritic cells resting; CDKN1A showed strong positive correlation with Macrophages M2; PPARA showed strong positive correlation with Plasma cells; and EGR1 and ATF3 showed strong positive correlation with Monocytes ( Figure 6C ).

To further understand the interaction between FRHGs and drugs, we predicted drugs or molecular compounds that may interact with FRHGs based on the DGIDB database. A total of 87 drugs or molecular compounds that may have regulatory relationships with FRHGs were screened, of which the largest number of drugs interacting with PPARA was identified, with a total of 57 related drugs ( Figure 7 ).

As we all know that protein is the primary executor of gene function. In this study, Proteomics and Western blot were further used to confirm the protein expression of FRHGs. Hematoxylin and eosin (HE) staining and creatinine determination confirmed successful modeling ( Figures 8A, B ). Differentially expressed proteins (DEPs) between IRI-AKI and Sham groups were analyzed in R software. The results showed 88 proteins exhibiting an upregulation trend, and 198 proteins exhibiting a downregulation trend ( Figure 8C ). GO analysis showed that “ion transport [BP],” “membrane [CC],” “ion channel activity [MF],” and “iron ion binding [MF]” were the most significantly enriched biological processes ( Figure 8D ). In KEGG, “Arachidonic acid metabolism” was the primarily enriched pathway ( Figure 8E ). Among these, the iron ion binding pathway was the commonly enriched biological process between DEFRGs and DEPs. By overlapping DEFRGs with differentially expressed proteins, we identified three intersecting proteins, among which CD44 was the only FRGH with elevated expression at both the gene and protein levels ( Figure 8F ). Subsequently, we further validated the protein expression levels of CD44 using Western blot ( Figure 8G ).

To further elucidate the critical role of CD44 in IRI-AKI, we established a mouse model of IRI-AKI and administered CD44-blocking antibodies (Anti-CD44). Compared to the Sham group, the IRI-AKI group exhibited tissue damage characterized by tubular necrosis, epithelial cell shedding, and inflammatory cell infiltration ( Figure 9A ). Renal function assessments revealed that Scr and BUN levels were increased rapidly; however, pre-treatment with Anti-CD44 significantly mitigated histological injury and functional impairment in the kidneys ( Figures 9A–C ). We also investigated the role of CD44 in ferroptosis. TUNEL staining indicated high death rate in renal tissues following IRI, accompanied by significant declines in ferroptosis-related indicators such as glutathione (GSH) and GPX4, and a marked increase in MDA levels ( Figures 9D–H ). Transmission electron microscopy demonstrated diminished, degenerated, or absent mitochondrial cristae in the IRI-AKI group ( Figure 9I ). Pre-treatment with CD44 monoclonal antibodies effectively reduced ferroptosis, evidenced by a decrease in TUNEL positive cells, an increase in GSH levels, elevated GPX4 expression, and a reduction in MDA, as well as lessened mitochondrial structural damage ( Figures 9D–I ). These findings suggest that CD44 plays a protective role in inhibiting ferroptosis during renal IRI. Furthermore, immunohistochemical analyses indicated a significant increase in M1 macrophage accumulation during IRI, which was effectively reduced by Anti-CD44 treatment( Figure 9J ). The relationship between reduced M1 macrophage accumulation and the inhibition of ferroptosis warrants further investigation.