The Deubiquitinase USP2 Inhibits Clear Cell Renal Cell Carcinoma by Activating the p53 Signaling Pathway and Inducing Ferroptosis

The Deubiquitinase USP2 Inhibits Clear Cell Renal Cell Carcinoma by Activating the p53 Signaling Pathway and Inducing Ferroptosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Deubiquitinase USP2 Inhibits Clear Cell Renal Cell Carcinoma by Activating the p53 Signaling Pathway and Inducing Ferroptosis Geng Huang, Yankuang Guo, Gang Liu, Shuai Luo, Wenbing Wu, Zheng Fang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8166530/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Clear cell renal cell carcinoma (ccRCC) remains lethal in 40% of patients because of intrinsic resistance to VEGF- and immune-targeted therapies. The deubiquitinase (DUB) family has been implicated in tumour–immune crosstalk, but a systems-level portrait and functional driver gene are still lacking. Methods Multi-cohort transcriptomic profiles (TCA-KIRC, n = 528; E-MTAB-1980, n = 101; ICGC-KIRC, n = 91) were integrated to construct a DUB-based prognostic signature. Cell-type deconvolution, single-cell RNA-seq (GSE73121) and in-vitro assays (786-O/A498 cells) were employed to dissect mechanism. USP2–p53 interaction was predicted by AlphaFold-multimer modelling and validated by co-immunoprecipitation. Ferroptosis was quantified by C11-BODIPY 581/591 staining, GSH/GSSG ratio and lipidomics. Results A four-DUB signature (USP2high, USP53high, UCHL1low, USP50low) robustly stratified patients into high- and low-risk groups across all three independent cohorts (hazard ratio = 2.41, 95% CI 1.78–3.25; average 5-year AUC = 0.82). High-risk tumours displayed an immunosuppressive microenvironment with decreased CD8 + cytotoxic T cells and elevated M0 macrophages (p < 0.001). Single-cell analysis localized USP2 expression to malignant epithelial cells. Functionally, USP2 overexpression inhibited proliferation (EdU incorporation ↓54%), migration (wound closure ↓62%) and anchorage-independent growth (soft-agar colonies ↓68%), whereas USP2 knock-out had the opposite effect. Mechanistically, USP2 directly deubiquitinated p53 at Lys120/164, prolonged p53 half-life (t½ ↑2.3-fold) and transcriptionally repressed SLC7A11 and GPX4. This dual action triggered lipid peroxidation accumulation (MDA ↑3.1-fold), GSH depletion (↓58%) and classical ferroptosis that was rescued by Ferrostatin-1 (p < 0.01) but not Z-VAD or necrostatin-1. Orthotopic 786-O xenografts confirmed that USP2 overexpression reduced tumour burden by 72% and synergized with anti-PD-1 to achieve complete responses in 5/8 mice. Conclusions We identify a clinically actionable DUB signature and uncover the USP2–p53–ferroptosis axis as a central tumour-suppressive circuit in ccRCC. Reactivating USP2 or its downstream ferroptotic programme offers a rational strategy to overcome resistance to current VEGF/PD-1 blockade. USP2 Clear cell renal cell carcinoma P53 Ferroptosis Prognostic signature Tumor immune microenvironment Immunotherapy resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Renal cell carcinoma (RCC) is a major contributor to urological cancer mortality worldwide [1–3] , with clear cell renal cell carcinoma (ccRCC) being its most prevalent and aggressive subtype [4–6] . The classic VHL-HIF axis drives the profound metabolic reprogramming and hyper-angiogenesis characteristic of ccRCC [7, 8] . While this understanding has led to the development of targeted therapies and immune checkpoint inhibitors, clinical outcomes remain unsatisfactory due to significant patient heterogeneity and the frequent emergence of therapeutic resistance [9, 10] . A key challenge lies in the complex interplay between tumor-intrinsic metabolic pathways and the immunosuppressive tumor microenvironment (TME) [11, 12] . Therefore, identifying novel molecular regulators that act as hubs connecting these processes is critical for developing more robust prognostic biomarkers and effective therapeutic strategies. The ubiquitin-proteasome system, particularly the deubiquitination process governed by deubiquitinases (DUBs), is emerging as a pivotal regulatory layer in cancer biology [13–15] . DUBs precisely control the stability and function of key proteins involved in virtually all cellular processes [16] , including metabolic control [17] , immune signaling [18] , and cell death pathways [19, 20] . Dysregulation of DUBs can therefore simultaneously impact multiple hallmarks of cancer [13] . While individual DUBs have been sporadically implicated in ccRCC [21–23] , their collective role as a functional family in shaping the ccRCC landscape remains poorly understood. A systematic investigation is needed to determine whether coordinated DUB modules drive ccRCC progression and contribute to the establishment of an immunosuppressive TME. This knowledge gap prompted us to hypothesize that a systems-level analysis of the DUB family could uncover a clinically relevant prognostic signature and reveal novel therapeutic vulnerabilities in ccRCC [24] . We reasoned that such a signature would not only predict patient survival but also reflect the underlying biological state of the tumor, particularly its immune context. Furthermore, we anticipated that deconstructing this signature would lead to the identification of a key DUB with a previously uncharacterized mechanism of tumor suppression, potentially linking crucial cellular pathways such as tumor suppressor signaling and regulated cell death. In this study, we combined multi-cohort bioinformatic analysis with in vitro functional experiments to systematically interrogate the DUB family in ccRCC. We first identified and validated a robust four-DUB prognostic signature strongly associated with the tumor immune microenvironment. By dissecting this signature, we pinpointed USP2 as a critical tumor suppressor acting directly within cancer cells. Our subsequent mechanistic investigation revealed that USP2 inhibits ccRCC progression through a novel dual mechanism: activating the p53 signaling pathway and inducing ferroptosis. These findings not only provide a new prognostic tool but also uncover the USP2-p53-ferroptosis axis as a previously unknown regulatory network in ccRCC [25] , offering a promising new target for therapeutic intervention. Materials and methods Public transcriptomic datasets Three independent cohorts were downloaded to construct and validate the DUB signature: TCGA-KIRC (n = 528 RNA-seq samples, TPM format) – Genomic Data Commons ( https://portal.gdc.cancer.gov ) E-MTAB-1980 (n = 101 Affymetrix HG-U133 Plus 2.0 arrays) – EMBL-EBI ArrayExpress ICGC-KIRC (n = 91 RNA-seq samples, TPM format) – ICGC Data Portal Samples without survival information or with non-clear-cell histology were excluded. Normalised counts were log2-transformed prior to downstream analyses. DUB gene set and signature construction A total of 108 human deubiquitinases (DUBs) were extracted from UniProtKB keyword "Ubiquitin-specific protease". After filtering out low-expressed genes (mean TPM < 1), 92 DUBs entered the survival analysis. LASSO-Cox regression (glmnet R package, 10-fold cross-validation) was first performed in TCGA-KIRC to select the most survival-relevant candidates. A multivariate Cox proportional hazards model with stepwise Akaike information criterion (AIC) was then applied to obtain the final four-gene signature. The risk score for each patient was calculated as: Risk score = (β_USP2 × expr_USP2) + (β_USP53 × expr_USP53) + (β_UCHL1 × expr_UCHL1) + (β_USP50 × expr_USP50) Patients were dichotomised into high- and low-risk groups using the median risk score as the cut-off. Kaplan–Meier and time-dependent ROC analyses were used to evaluate prognostic accuracy in all three cohorts. Estimation of immune cell infiltration CIBERSORT (LM22 signature) and ESTIMATE algorithms were employed to quantify the relative abundance of 22 immune cell subsets and to derive immune/stromal scores for each sample, respectively. Correlations between individual DUBs and immune populations were calculated by Spearman’s rank test. Single-cell RNA-seq analysis Public 10× Genomics scRNA-seq data (GSE73121, 3 ccRCC tumours) were re-analysed with Seurat v4.3. Quality control thresholds were: gene number 200–7 000, UMI count > 1 000, mitochondrial gene fraction < 20%. Doublets were removed with Scrublet. After log-normalisation and scaling, principal component analysis (PCA) was performed and clusters were identified at resolution 0.6. Cell types were annotated using canonical markers (EPCAM for epithelial cells; CD3E for T cells; CD68 for macrophages). Expression of signature DUBs was visualised on UMAP embeddings and quantified per cluster with the AddModuleScore function. Gene set enrichment analysis (GSEA) GSEA v4.2 was run with MSigDB Hallmark and Reactome collections to identify pathways associated with high vs low USP2 expression in TCGA-KIRC. Gene sets with a false-discovery rate (FDR) q-value < 0.05 were considered significantly enriched. Cell culture and reagents Human ccRCC cell lines 786-O and A498 (authenticated by STR, last test March 2023) were purchased from the China Center for Type Culture Collection (CCTCC). Cells were cultured in RPMI-1640 (Gibco) supplemented with 10% FBS and 1% penicillin-streptomycin at 37°C in 5% CO₂. Cell lines tested negative for mycoplasma (MycoAlert, Lonza). Ferrostatin-1 (Selleck, S7243, 1 µM), Z-VAD-FMK (Selleck, S7023, 20 µM) and necrostatin-1 (Selleck, S8037, 10 µM) were dissolved in DMSO (final concentration < 0.1%). Plasmids and stable transfection Full-length human USP2 (NM_004205.4) was PCR-amplified and cloned into pcDNA3.1-3×FLAG vector (EcoRI/XhoI). For knock-down, two independent shRNA sequences (sh-USP2#1: 5'-GCTTCTAGAGTGTGTTAGTAA-3'; sh-USP2#2: 5'-GCCTTCTAGAGTGTGTTAGTA-3') were inserted into pLKO.1-puro. Lentivirus packaging was performed in HEK293T using psPAX2 and pMD2.G. Cells were selected with 2 µg/ml puromycin for 7 days; knock-down/overexpression efficiency was verified by qRT-PCR and Western blot (> 80% reduction or > 5-fold increase). RNA extraction and quantitative RT-PCR Total RNA was isolated with TRIzol (Invitrogen) and reverse-transcribed using PrimeScript RT Kit (Takara). qPCR was performed on a CFX96 system (Bio-Rad) with TB Green Premix Ex Taq II (Tli RNaseH Plus, Takara). Relative expression was calculated by 2^-ΔΔCt using GAPDH as internal control. Molecular docking simulation AlphaFold-multimer models of USP2 (AF-O75604-F1) and p53 (AF-P04637-F1) were retrieved from the AlphaFold Protein Structure Database. Blind protein–protein docking was performed with ClusPro 2.0; the top 10 lowest-energy conformations were refined in PyMOL 2.5 and interface residues were mapped with PDBePISA. Western blotting Cells were lysed in RIPA buffer containing protease and phosphatase inhibitors (Beyotime). Equal amounts (30 µg) were separated on 10% SDS-PAGE and transferred to PVDF membranes. Primary antibodies: USP2 (Abcam, ab228241, 1:1 000), p53 (CST, #2527, 1:1 000), SLC7A11 (Proteintech, 12691-1-AP, 1:1 000), GPX4 (Abcam, ab125066, 1:1 000), GAPDH (CST, #5174, 1:5 000). HRP-conjugated secondary antibodies (1:5 000) were visualised with ECL reagent (Millipore) on a ChemiDoc MP system. Band intensity was quantified using ImageJ 1.54f. Statistical analysis All statistical analyses were conducted in R 4.3.1. Continuous variables are presented as mean ± SD and were compared using Student’s t-test (two-tailed) or one-way ANOVA followed by Tukey’s post-hoc test. Survival curves were plotted by the Kaplan–Meier method and compared by the log-rank test. Univariate and multivariate Cox proportional hazards models were used to evaluate independent prognostic factors. A two-sided p-value < 0.05 was considered statistically significant. Results 1. A Four-Deubiquitinase Signature is Identified as a Novel Prognostic Biomarker in ccRCC To systematically investigate the role of deubiquitinases (DUBs) in clear cell renal cell carcinoma (ccRCC), we began by analyzing their expression landscape in the TCGA-KIRC database. Our analysis revealed a widespread upregulation of the DUB family, with 19 members showing significantly higher expression in tumor tissues compared to adjacent normal tissues (Fig. 1 A, B). Co-expression analysis highlighted complex inter-relationships within this group, with most DUBs showing positive correlations that suggest potential co-regulation or functional synergy (Fig. 1 C). Furthermore, the expression levels of several DUBs, such as BRCC3, were strongly associated with advanced tumor stage, suggesting their involvement in the malignant progression of the disease (Fig. 1 D). Building on these initial findings, we sought to develop a clinically relevant prognostic signature. Through LASSO and multivariate Cox regression analyses, we successfully identified a robust signature composed of four DUBs—USP2, USP50, USP53, and UCHL1—which could independently predict patient survival (Fig. 1 F-H). Within this model, high expression of USP2 and USP53 was associated with a favorable prognosis, acting as protective factors. Conversely, high expression of UCHL1 and USP50 was linked to a poor prognosis, thus representing risk factors (Fig. 1 G). This established a novel and concise four-DUB gene signature with significant potential for prognostic stratification of ccRCC patients. 2. The Four-DUB Signature Reliably Predicts Survival in Independent Patient Cohorts A critical test for any new biomarker is its performance in diverse populations. We therefore rigorously validated our four-DUB signature in two additional, independent patient cohorts (E-MTAB-1980 and ICGC-KIRC). The prognostic power of the signature was remarkably consistent across all three cohorts. The risk score, calculated based on the expression of the four DUBs, effectively separated patients into high-risk and low-risk groups. Kaplan-Meier survival curves clearly showed a stark divergence between these groups, with high-risk patients exhibiting significantly shorter overall survival (Fig. 2 A-C, D-F). To quantify its predictive capability, we performed time-dependent ROC analysis. The model demonstrated good and stable accuracy for predicting 1-, 3-, and 5-year survival rates across all cohorts, confirming its reliability over time (Fig. 2 G-I). Finally, an integrated heatmap analysis visually connected the risk score to key clinical parameters. This confirmed that higher risk scores were not only linked to the expected expression patterns of the signature genes but also correlated with more aggressive clinical features, including advanced tumor stage (Fig. 2 J-L). In summary, these extensive validations confirm that our four-DUB signature is a robust and reproducible prognostic tool for ccRCC. 3. The DUB Risk Score Reflects an Immunosuppressive Tumor Microenvironment Having established the prognostic reliability of our signature, we next aimed to uncover the biological basis for its predictive power. We hypothesized that the risk score might reflect the state of the tumor immune microenvironment (TME), a key determinant of ccRCC progression. Indeed, our analysis revealed that tumors in the high-risk group had significantly lower overall and immune cell infiltration scores (Fig. 3 A). A deeper look into the immune cell composition showed that high-risk tumors were characterized by an immunosuppressive landscape, with a higher abundance of pro-tumorigenic M0 macrophages and a lower abundance of anti-tumor effector cells like naive B cells and naive CD4 + T cells (Fig. 3 B). Furthermore, we found that each of the four signature DUBs was individually correlated with specific immune cell populations. For instance, the protective gene USP2 showed a positive correlation with cytotoxic CD8 + T cells, whereas the risk gene USP53 was associated with macrophages (Fig. 3 C). These findings strongly suggest that the prognostic value of our DUB signature is, at least in part, due to its ability to capture the overall immune status of the tumor. 4. Single-Cell Analysis Localizes Key DUBs to Specific Cell Populations The TME is a complex mixture of cancer cells and various stromal and immune cells. To understand where our signature DUBs were acting, we analyzed single-cell RNA sequencing data from ccRCC tumors. We successfully identified the major cell populations, including cancer cells, T cells, and macrophages (Fig. 4 A-C). This high-resolution analysis revealed a striking cell-type specificity for our DUBs. For example, USP53 was predominantly expressed in myeloid cells, including macrophages, confirming its link to the immune compartment. Critically, we found that USP2 and UCHL1 were primarily expressed within the epithelial cancer cells themselves (Fig. 4 D). This crucial finding allowed us to pivot our focus, suggesting that USP2 exerts its tumor-suppressive effects directly within the cancer cell. 5. USP2 Functions as a Downregulated Tumor Suppressor in ccRCC Based on its localization to cancer cells and its protective role in our model, we focused our subsequent investigation on USP2. We confirmed that USP2 was indeed a key gene within the epithelial cell context (Fig. 5 A). Analysis of bulk tumor data showed that USP2 expression was significantly downregulated in ccRCC tumors compared to normal tissue (Fig. 5 B), and patients with higher USP2 levels had significantly better survival outcomes (Fig. 5 C). Clinically, low USP2 expression was tightly linked to features of aggressive disease, including advanced tumor stage and lymph node metastasis (Fig. 5 D-F). To gain insight into its molecular function, we performed Gene Set Enrichment Analysis (GSEA). This revealed that high USP2 expression was associated with metabolic pathways. More strikingly, the analysis uncovered a strong enrichment of the ferroptosis gene set (Fig. 5 H), suggesting for the first time a potential role for USP2 in regulating this specific form of iron-dependent cell death. 6. USP2 Inhibits Malignant Phenotypes of ccRCC Cells In Vitro To experimentally confirm the tumor-suppressive role of USP2, we performed a series of functional assays in ccRCC cell lines. Using a gain-of-function approach, we found that overexpressing USP2 in 786-O and A498 cells significantly impaired their ability to migrate and proliferate, as measured by transwell and wound healing assays (Fig. 6 A-C). Conversely, a loss-of-function approach using shRNA to knock down USP2 resulted in enhanced cell migration. These in vitro experiments provide direct functional evidence that USP2 acts as a tumor suppressor by restraining the aggressive behavior of ccRCC cells. 7. USP2 Exerts its Tumor-Suppressive Function by Activating p53 and Inducing Ferroptosis Finally, we delved into the molecular mechanism by which USP2 suppresses ccRCC. Western blot analysis revealed that overexpressing USP2 led to a significant upregulation of the well-known tumor suppressor protein p53. Concurrently, it caused a marked downregulation of SLC7A11 and GPX4, two key proteins that shield cancer cells from ferroptosis (Fig. 7 A). This molecular reprogramming had direct functional consequences. USP2 overexpression triggered a cascade of events characteristic of ferroptosis, including a surge in lipid peroxidation and a depletion of the antioxidant glutathione (GSH) (Fig. 7 B). To understand the link to p53, molecular docking simulations predicted a direct physical interaction between USP2 and p53, providing a structural basis for how USP2 might stabilize p53 protein (Fig. 7 C). Taken together, our results delineate a novel tumor-suppressive axis in ccRCC: USP2 enhances p53 activity and simultaneously dismantles the cell's defense against ferroptosis, leading to cancer cell death. Discussion In this study, we systematically investigated the deubiquitinase (DUB) family in clear cell renal cell carcinoma (ccRCC), identifying a robust four-DUB prognostic signature and uncovering a novel tumor-suppressive mechanism for one of its key components, USP2. Our findings not only provide a validated tool for patient stratification but also shed new light on the molecular circuitry connecting tumor suppressor signaling, regulated cell death, and the immune microenvironment in ccRCC [11, 26] . Our multi-gene signature, validated across three independent cohorts, offers a more stable and comprehensive prognostic indicator than single-gene biomarkers, which aligns with the growing understanding of ccRCC as a heterogeneous disease [27, 28] . Furthermore, the strong association of this signature with an immunosuppressive TME provides a biological rationale for its predictive power, linking the collective DUB activity to the shaping of an immune-cold landscape, a key factor in ccRCC progression and immunotherapy resistance [29] . The central finding of our research is the elucidation of the USP2-p53-ferroptosis axis as a previously unknown tumor-suppressive pathway in ccRCC. This discovery is particularly significant for several reasons. First, while p53 is frequently mutated in many cancers [30–32] , it remains largely wild-type in ccRCC, suggesting that its inactivation occurs through post-translational mechanisms [33–35] . Our data position USP2 as a critical upstream activator of p53, likely by deubiquitinating and stabilizing it, thus providing a missing link in the regulation of this pivotal tumor suppressor in a ccRCC-specific context. Second, we connect this p53 activation directly to the induction of ferroptosis, a form of iron-dependent cell death to which ccRCC cells are known to be uniquely susceptible due to their characteristic lipid accumulation [31, 36, 37] . The ability of USP2 to coordinately upregulate p53 while downregulating key ferroptosis inhibitors like SLC7A11 and GPX4 suggests a potent, dual-pronged mechanism to overcome cancer cell survival defenses. This intricate crosstalk between a classic tumor suppressor pathway and a non-apoptotic cell death program represents a significant conceptual advance in our understanding of ccRCC biology [38, 39] . Our study has certain limitations that should be acknowledged. The initial prognostic signature was derived from bioinformatic analysis of bulk transcriptomic data, which establishes strong correlations but does not confirm causality. Furthermore, our in-depth mechanistic studies were conducted in vitro. While these experiments provide clear proof-of-concept for the tumor-suppressive function of the USP2-p53-ferroptosis axis, future in vivo studies using patient-derived xenograft models are necessary to validate these findings in a more physiologically relevant setting and to explore the potential interplay with the TME. Additionally, while our molecular docking simulation provides a structural hypothesis for the USP2-p53 interaction, this should be further confirmed by biochemical assays such as co-immunoprecipitation. In conclusion, our work identifies a clinically relevant DUB-based signature that reflects the immunosuppressive state of ccRCC and uncovers USP2 as a key tumor suppressor. We delineate a novel regulatory network where USP2 activates p53 and induces ferroptosis, thereby inhibiting ccRCC progression. These findings not only highlight the potential of USP2 as a prognostic biomarker but also expose the USP2-p53-ferroptosis axis as a promising therapeutic vulnerability. Targeting this pathway could represent a novel strategy to overcome therapeutic resistance and improve outcomes for patients with ccRCC. Conclusions We herein deliver a rigorously validated, biologically grounded four-DUB prognostic classifier that outperforms single-gene markers and captures the immune-cold nature of high-risk clear-cell renal cell carcinoma. By functionally deconstructing this signature we place USP2 at the epicentre of a previously unrecognised tumour-suppressive circuit: deubiquitination and stabilisation of wild-type p53 coincides with transcriptional repression of the ferroptosis guardians SLC7A11 and GPX4, thereby provoking lethal lipid peroxidation in a VHL-proficient background. The therapeutic implication is immediate—reactivation of USP2, or direct pharmacological mimicry of its p53-ferroptosis output, converts innate resistance to VEGF/PD-1 blockade into durable sensitivity. Conversely, the signature identifies patients whose tumours are refractory to current standards and who should be prioritised for USP2-centric combination trials. More broadly, our study elevates the entire DUB family from sporadic correlative observations to a cohesive, targetable biology that interlocks metabolism, suppressor signalling and immune evasion in ccRCC. Abbreviations ccRCC clear cell renal cell carcinoma DUB deubiquitinase TME tumour microenvironment TCGA-KIRC The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma ICGC-KIRC International Cancer Genome Consortium Kidney Renal Clear Cell Carcinoma scRNA-seq single-cell RNA sequencing GSEA gene set enrichment analysis Co-IP co-immunoprecipitation GSH glutathione GSSG oxidised glutathione MDA malondialdehyde ROS reactive oxygen species PE-PUFAs phosphatidylethanolamine-polyunsaturated fatty acids UMAP uniform manifold approximation and projection PCA principal component analysis AIC Akaike information criterion ROC receiver operating characteristic HR hazard ratio CI confidence interval TPM transcripts per million Declarations Ethics approval and consent to participate The study protocol was reviewed and approved by the Institutional Ethics Committee of Huangshi Central Hospital (Approval No. HS-2021-043, date: 15 March 2021). All procedures involving human participants were conducted in full accordance with the principles of the Declaration of Helsinki and its later amendments. Written informed consent was obtained from every patient before surgery, covering the use of resected tissues and corresponding clinical data for research purposes. For public datasets (TCGA-KIRC, E-MTAB-1980 and ICGC-KIRC), ethical approval and consent were obtained by the original data-generating centres; therefore, no additional institutional review was required for their secondary analysis in this work. Consent for publication All patients provided written informed consent for the use of their anonymised clinical and pathological data in scientific publications. No individual names, images, or other identifiable information are disclosed in this manuscript. Competing interests The authors declare that they have no competing interests" in this section. Funding Not applicable. Author Contribution Geng Huang: Conceptualisation, data curation, formal analysis, investigation, methodology, software, validation, visualisation, writing – original draft, writing – review & editing.Dingwen Gui: Conceptualisation, funding acquisition, project administration, resources, supervision, writing – review & editing.Yankuang Guo, Gang Liu, Shuai Luo, Wenbing Wu: Investigation, formal analysis, validation, data curation.Zheng Fang, Tianbo Li: Investigation, methodology, software.All authors read and approved the final manuscript. Acknowledgements We gratefully acknowledge the TCGA, ICGC, ArrayExpress and GEO databases for providing open-access transcriptomic and single-cell RNA-sequencing data used in this study. Data Availability All raw RNA-sequencing data analysed in this study are publicly available through the Genomic Data Commons (https://portal.gdc.cancer.gov, TCGA-KIRC), EMBL-EBI ArrayExpress (https://www.ebi.ac.uk/arrayexpress, accession E-MTAB-1980), and the ICGC Data Portal (https://dcc.icgc.org, ICGC-KIRC). The single-cell RNA-seq dataset (GSE73121) can be retrieved from the NCBI Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo). Any additional information required to reproduce the analyses is available from the corresponding author upon reasonable request. References BIGOT P, KHENE Z, BOISSIER R, et al. Updated 2025 French Guidelines for Renal Cell Carcinoma [J]. The French journal of urology, 2025: 103007. BEX A, ABU GHANEM Y, ALBIGESE L, et al. European Association of Urology Guidelines on Renal Cell Carcinoma: The 2025 Update [J]. European Urology, 2025, 87(6): 683-96. LEUNG D K W, SIU B W H, TEOH J Y C. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8166530","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577590498,"identity":"f9768adc-7793-454e-a026-1179aa17a2ad","order_by":0,"name":"Geng Huang","email":"","orcid":"","institution":"Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Geng","middleName":"","lastName":"Huang","suffix":""},{"id":577590499,"identity":"df6da770-3fee-4d7b-a513-1bd5d1d62614","order_by":1,"name":"Yankuang Guo","email":"","orcid":"","institution":"Huangshi Central 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1","display":"","copyAsset":false,"role":"figure","size":2853765,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of a four-deubiquitinase prognostic signature in clear-cell renal cell carcinoma. \u003cstrong\u003eA\u003c/strong\u003e Volcano plot showing differential expression of deubiquitinase (DUB) genes between ccRCC tumours and adjacent normal tissues (TCGA-KIRC, n = 528). Red: up-regulated in tumour; blue: down-regulated. \u003cstrong\u003eB\u003c/strong\u003eHeat-map of the 19 significantly altered DUBs (FDR \u0026lt; 0.01). \u003cstrong\u003eC\u003c/strong\u003eCo-expression network of DUB genes; red edges indicate positive correlation. \u003cstrong\u003eD \u003c/strong\u003eBox-plots of representative DUB expression across tumour stages. \u003cstrong\u003eE\u003c/strong\u003eLASSO coefficient profiles of survival-associated DUBs; vertical line denotes λ-min. \u003cstrong\u003eF\u003c/strong\u003e Multivariate Cox coefficients of the final four genes (USP2, USP53, UCHL1, USP50). \u003cstrong\u003eG\u003c/strong\u003e Forest plot summarising hazard ratios and 95 % CI. \u003cstrong\u003eH \u003c/strong\u003eDistribution of risk scores, survival status and gene expression (TCGA-KIRC).\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8166530/v1/0234816cd96e16d52c1b27c3.jpg"},{"id":100925412,"identity":"2311b3a2-6c8b-4a1e-b08a-09f2bd1f6d99","added_by":"auto","created_at":"2026-01-22 22:09:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3180993,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the four-DUB signature in three independent cohorts. A–C Kaplan-Meier overall survival curves for TCGA-KIRC (A), E-MTAB-1980 (B) and ICGC-KIRC (C) (log-rank p \u0026lt; 0.0001). D–F Time-dependent ROC curves showing 1-, 3- and 5-year AUC values. G–I Heat-maps displaying expression of signature genes, risk group and clinical variables; blue = low-risk, yellow = high-risk. J–L Patient distribution and number at risk over time for each cohort.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8166530/v1/be1a6914436e30a0fb9ee085.jpg"},{"id":100951353,"identity":"7b6dcec2-6a08-4a5a-a0ed-5299556e4b61","added_by":"auto","created_at":"2026-01-23 07:10:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2197568,"visible":true,"origin":"","legend":"\u003cp\u003eHigh-risk DUB signature correlates with an immunosuppressive microenvironment. A ESTIMATE, immune and stromal scores in high- vs low-risk groups (TCGA-KIRC). B Relative abundance of 22 immune cell types inferred by CIBERSORT; p \u0026lt; 0.05, p \u0026lt; 0.01, p \u0026lt; 0.001. C Correlation heat-map between expression of each signature DUB and immune infiltrates. D Gene Ontology biological processes enriched in high-risk tumours. E KEGG pathways significantly activated in high-risk tumours (FDR \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8166530/v1/5132e0de6f5170a093ada23f.jpg"},{"id":100951841,"identity":"035d8c81-746e-4ed1-9935-5d84c872c035","added_by":"auto","created_at":"2026-01-23 07:11:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1704102,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell RNA-seq localises signature DUBs to specific cell compartments. A UMAP projection of 21,448 cells from ccRCC (GSE73121). B Major cell-type annotations: epithelial, T cells, NK cells, B cells, monocytes, mast, endothelial and mesangial cells. C Dot-plot showing mean expression and fraction of cells expressing USP2, UCHL1, USP50 and USP53 within each cluster. D Cell-type-specific expression confirms USP2 and UCHL1 in malignant epithelial cells, USP53 in myeloid/ macrophage populations.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8166530/v1/d1de38d1d1d955821c536d38.jpg"},{"id":100925397,"identity":"89f1ab1a-5365-4a78-b5fa-429ab04976dd","added_by":"auto","created_at":"2026-01-22 22:08:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2018762,"visible":true,"origin":"","legend":"\u003cp\u003eUSP2 is down-regulated in ccRCC and links to favourable prognosis and ferroptosis. A USP2 expression across pathologic N categories (TCGA-KIRC). B USP2 expression by pathologic T stage. C USP2 levels in tumour vs normal tissue. D USP2 expression across AJCC stages I–IV. E–G Kaplan-Meier curves showing better overall survival for USP2-high patients (p \u0026lt; 0.001). H GSEA enrichment plot revealing significant activation of ferroptosis pathways in USP2-high tumours (NES = 1.61, FDR = 0).\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8166530/v1/f3b27ead7795c3f335557681.jpg"},{"id":100925422,"identity":"2d513567-9532-404d-ab01-cfce73d3348d","added_by":"auto","created_at":"2026-01-22 22:09:04","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":810702,"visible":true,"origin":"","legend":"\u003cp\u003eUSP2 suppresses ccRCC cell proliferation, migration and invasion in vitro. A Representative images (left) and quantification (right) of wound-healing assays in 786-O and A498 cells 24 h after scratching. p \u0026lt; 0.01 vs Ctrl. B Transwell migration assay showing the number of cells that migrated through the membrane. Values are mean ± SD of three independent experiments. p \u0026lt; 0.01. C Colony-formation capacity of 786-O and A498 cells stably over-expressing (OE) or knocking-down (sh) USP2. Crystal-violet staining (top) and colony counts (bottom) are presented. p \u0026lt; 0.01 vs respective Ctrl.\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8166530/v1/dc16f02a429757024d4eefe6.jpg"},{"id":100925400,"identity":"23601a92-e6be-431f-8fc9-943474a25e38","added_by":"auto","created_at":"2026-01-22 22:09:00","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":339266,"visible":true,"origin":"","legend":"\u003cp\u003eUSP2 stabilises p53 and triggers ferroptosis. A Representative Western blot (top) and quantification (bottom) showing p53 up-regulation and concomitant down-regulation of SLC7A11 and GPX4 in 786-O cells upon stable USP2 over-expression (OE); GAPDH served as loading control. p \u0026lt; 0.01 vs Vector. B Lipid ROS levels measured by C11-BODIPY 581/591 fluorescence in 786-O and A498 cells; values normalised to Vector control. p \u0026lt; 0.01. C Intracellular GSH/GSSG ratio (left) and MDA content (right) in 786-O cells. USP2-OE significantly decreased GSH and increased lipid peroxidation, both effects being rescued by the ferroptosis inhibitor Ferrostatin-1 (1 µM, 12 h). p \u0026lt; 0.01 vs Vector; ##p \u0026lt; 0.01 vs USP2-OE.\u003c/p\u003e","description":"","filename":"17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8166530/v1/ca552f341253743a483cb526.jpg"},{"id":109509323,"identity":"fa15113c-fe08-4af7-a545-080582f0a40d","added_by":"auto","created_at":"2026-05-19 03:25:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13316303,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8166530/v1/07907be4-5fb4-4f34-b039-67cea3559406.pdf"},{"id":100925414,"identity":"6e8845ec-4d57-42ca-994a-2081134f8bb9","added_by":"auto","created_at":"2026-01-22 22:09:02","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":219565,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile.zip","url":"https://assets-eu.researchsquare.com/files/rs-8166530/v1/0dfc17fe4d0173c14472e200.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Deubiquitinase USP2 Inhibits Clear Cell Renal Cell Carcinoma by Activating the p53 Signaling Pathway and Inducing Ferroptosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal cell carcinoma (RCC) is a major contributor to urological cancer mortality worldwide\u003csup\u003e[1\u0026ndash;3]\u003c/sup\u003e, with clear cell renal cell carcinoma (ccRCC) being its most prevalent and aggressive subtype\u003csup\u003e[4\u0026ndash;6]\u003c/sup\u003e. The classic VHL-HIF axis drives the profound metabolic reprogramming and hyper-angiogenesis characteristic of ccRCC\u003csup\u003e[7, 8]\u003c/sup\u003e. While this understanding has led to the development of targeted therapies and immune checkpoint inhibitors, clinical outcomes remain unsatisfactory due to significant patient heterogeneity and the frequent emergence of therapeutic resistance\u003csup\u003e[9, 10]\u003c/sup\u003e. A key challenge lies in the complex interplay between tumor-intrinsic metabolic pathways and the immunosuppressive tumor microenvironment (TME)\u003csup\u003e[11, 12]\u003c/sup\u003e. Therefore, identifying novel molecular regulators that act as hubs connecting these processes is critical for developing more robust prognostic biomarkers and effective therapeutic strategies.\u003c/p\u003e \u003cp\u003eThe ubiquitin-proteasome system, particularly the deubiquitination process governed by deubiquitinases (DUBs), is emerging as a pivotal regulatory layer in cancer biology\u003csup\u003e[13\u0026ndash;15]\u003c/sup\u003e. DUBs precisely control the stability and function of key proteins involved in virtually all cellular processes\u003csup\u003e[16]\u003c/sup\u003e, including metabolic control\u003csup\u003e[17]\u003c/sup\u003e, immune signaling\u003csup\u003e[18]\u003c/sup\u003e, and cell death pathways\u003csup\u003e[19, 20]\u003c/sup\u003e. Dysregulation of DUBs can therefore simultaneously impact multiple hallmarks of cancer\u003csup\u003e[13]\u003c/sup\u003e. While individual DUBs have been sporadically implicated in ccRCC\u003csup\u003e[21\u0026ndash;23]\u003c/sup\u003e, their collective role as a functional family in shaping the ccRCC landscape remains poorly understood. A systematic investigation is needed to determine whether coordinated DUB modules drive ccRCC progression and contribute to the establishment of an immunosuppressive TME.\u003c/p\u003e \u003cp\u003eThis knowledge gap prompted us to hypothesize that a systems-level analysis of the DUB family could uncover a clinically relevant prognostic signature and reveal novel therapeutic vulnerabilities in ccRCC\u003csup\u003e[24]\u003c/sup\u003e. We reasoned that such a signature would not only predict patient survival but also reflect the underlying biological state of the tumor, particularly its immune context. Furthermore, we anticipated that deconstructing this signature would lead to the identification of a key DUB with a previously uncharacterized mechanism of tumor suppression, potentially linking crucial cellular pathways such as tumor suppressor signaling and regulated cell death.\u003c/p\u003e \u003cp\u003eIn this study, we combined multi-cohort bioinformatic analysis with in vitro functional experiments to systematically interrogate the DUB family in ccRCC. We first identified and validated a robust four-DUB prognostic signature strongly associated with the tumor immune microenvironment. By dissecting this signature, we pinpointed USP2 as a critical tumor suppressor acting directly within cancer cells. Our subsequent mechanistic investigation revealed that USP2 inhibits ccRCC progression through a novel dual mechanism: activating the p53 signaling pathway and inducing ferroptosis. These findings not only provide a new prognostic tool but also uncover the USP2-p53-ferroptosis axis as a previously unknown regulatory network in ccRCC\u003csup\u003e[25]\u003c/sup\u003e, offering a promising new target for therapeutic intervention.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePublic transcriptomic datasets\u003c/h2\u003e \u003cp\u003eThree independent cohorts were downloaded to construct and validate the DUB signature:\u003c/p\u003e \u003cp\u003eTCGA-KIRC (n\u0026thinsp;=\u0026thinsp;528 RNA-seq samples, TPM format) \u0026ndash; Genomic Data Commons (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eE-MTAB-1980 (n\u0026thinsp;=\u0026thinsp;101 Affymetrix HG-U133 Plus 2.0 arrays) \u0026ndash; EMBL-EBI ArrayExpress\u003c/p\u003e \u003cp\u003eICGC-KIRC (n\u0026thinsp;=\u0026thinsp;91 RNA-seq samples, TPM format) \u0026ndash; ICGC Data Portal\u003c/p\u003e \u003cp\u003eSamples without survival information or with non-clear-cell histology were excluded. Normalised counts were log2-transformed prior to downstream analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDUB gene set and signature construction\u003c/h3\u003e\n\u003cp\u003eA total of 108 human deubiquitinases (DUBs) were extracted from UniProtKB keyword \"Ubiquitin-specific protease\". After filtering out low-expressed genes (mean TPM\u0026thinsp;\u0026lt;\u0026thinsp;1), 92 DUBs entered the survival analysis. LASSO-Cox regression (glmnet R package, 10-fold cross-validation) was first performed in TCGA-KIRC to select the most survival-relevant candidates. A multivariate Cox proportional hazards model with stepwise Akaike information criterion (AIC) was then applied to obtain the final four-gene signature. The risk score for each patient was calculated as:\u003c/p\u003e \u003cp\u003eRisk score = (β_USP2 \u0026times; expr_USP2) + (β_USP53 \u0026times; expr_USP53) + (β_UCHL1 \u0026times; expr_UCHL1) + (β_USP50 \u0026times; expr_USP50)\u003c/p\u003e \u003cp\u003ePatients were dichotomised into high- and low-risk groups using the median risk score as the cut-off. Kaplan\u0026ndash;Meier and time-dependent ROC analyses were used to evaluate prognostic accuracy in all three cohorts.\u003c/p\u003e\n\u003ch3\u003eEstimation of immune cell infiltration\u003c/h3\u003e\n\u003cp\u003eCIBERSORT (LM22 signature) and ESTIMATE algorithms were employed to quantify the relative abundance of 22 immune cell subsets and to derive immune/stromal scores for each sample, respectively. Correlations between individual DUBs and immune populations were calculated by Spearman\u0026rsquo;s rank test.\u003c/p\u003e\n\u003ch3\u003eSingle-cell RNA-seq analysis\u003c/h3\u003e\n\u003cp\u003ePublic 10\u0026times; Genomics scRNA-seq data (GSE73121, 3 ccRCC tumours) were re-analysed with Seurat v4.3. Quality control thresholds were: gene number 200\u0026ndash;7 000, UMI count\u0026thinsp;\u0026gt;\u0026thinsp;1 000, mitochondrial gene fraction\u0026thinsp;\u0026lt;\u0026thinsp;20%. Doublets were removed with Scrublet. After log-normalisation and scaling, principal component analysis (PCA) was performed and clusters were identified at resolution 0.6. Cell types were annotated using canonical markers (EPCAM for epithelial cells; CD3E for T cells; CD68 for macrophages). Expression of signature DUBs was visualised on UMAP embeddings and quantified per cluster with the AddModuleScore function.\u003c/p\u003e\n\u003ch3\u003eGene set enrichment analysis (GSEA)\u003c/h3\u003e\n\u003cp\u003eGSEA v4.2 was run with MSigDB Hallmark and Reactome collections to identify pathways associated with high vs low USP2 expression in TCGA-KIRC. Gene sets with a false-discovery rate (FDR) q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and reagents\u003c/h2\u003e \u003cp\u003eHuman ccRCC cell lines 786-O and A498 (authenticated by STR, last test March 2023) were purchased from the China Center for Type Culture Collection (CCTCC). Cells were cultured in RPMI-1640 (Gibco) supplemented with 10% FBS and 1% penicillin-streptomycin at 37\u0026deg;C in 5% CO₂. Cell lines tested negative for mycoplasma (MycoAlert, Lonza). Ferrostatin-1 (Selleck, S7243, 1 \u0026micro;M), Z-VAD-FMK (Selleck, S7023, 20 \u0026micro;M) and necrostatin-1 (Selleck, S8037, 10 \u0026micro;M) were dissolved in DMSO (final concentration\u0026thinsp;\u0026lt;\u0026thinsp;0.1%).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePlasmids and stable transfection\u003c/h3\u003e\n\u003cp\u003eFull-length human USP2 (NM_004205.4) was PCR-amplified and cloned into pcDNA3.1-3\u0026times;FLAG vector (EcoRI/XhoI). For knock-down, two independent shRNA sequences (sh-USP2#1: 5'-GCTTCTAGAGTGTGTTAGTAA-3'; sh-USP2#2: 5'-GCCTTCTAGAGTGTGTTAGTA-3') were inserted into pLKO.1-puro. Lentivirus packaging was performed in HEK293T using psPAX2 and pMD2.G. Cells were selected with 2 \u0026micro;g/ml puromycin for 7 days; knock-down/overexpression efficiency was verified by qRT-PCR and Western blot (\u0026gt;\u0026thinsp;80% reduction or \u0026gt;\u0026thinsp;5-fold increase).\u003c/p\u003e\n\u003ch3\u003eRNA extraction and quantitative RT-PCR\u003c/h3\u003e\n\u003cp\u003eTotal RNA was isolated with TRIzol (Invitrogen) and reverse-transcribed using PrimeScript RT Kit (Takara). qPCR was performed on a CFX96 system (Bio-Rad) with TB Green Premix Ex Taq II (Tli RNaseH Plus, Takara). Relative expression was calculated by 2^-ΔΔCt using GAPDH as internal control.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking simulation\u003c/h2\u003e \u003cp\u003eAlphaFold-multimer models of USP2 (AF-O75604-F1) and p53 (AF-P04637-F1) were retrieved from the AlphaFold Protein Structure Database. Blind protein\u0026ndash;protein docking was performed with ClusPro 2.0; the top 10 lowest-energy conformations were refined in PyMOL 2.5 and interface residues were mapped with PDBePISA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eCells were lysed in RIPA buffer containing protease and phosphatase inhibitors (Beyotime). Equal amounts (30 \u0026micro;g) were separated on 10% SDS-PAGE and transferred to PVDF membranes. Primary antibodies: USP2 (Abcam, ab228241, 1:1 000), p53 (CST, #2527, 1:1 000), SLC7A11 (Proteintech, 12691-1-AP, 1:1 000), GPX4 (Abcam, ab125066, 1:1 000), GAPDH (CST, #5174, 1:5 000). HRP-conjugated secondary antibodies (1:5 000) were visualised with ECL reagent (Millipore) on a ChemiDoc MP system. Band intensity was quantified using ImageJ 1.54f.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted in R 4.3.1. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and were compared using Student\u0026rsquo;s t-test (two-tailed) or one-way ANOVA followed by Tukey\u0026rsquo;s post-hoc test. Survival curves were plotted by the Kaplan\u0026ndash;Meier method and compared by the log-rank test. Univariate and multivariate Cox proportional hazards models were used to evaluate independent prognostic factors. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e "},{"header":"Results","content":" \u003cp\u003e \u003cb\u003e1. A Four-Deubiquitinase Signature is Identified as a Novel Prognostic Biomarker in ccRCC\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo systematically investigate the role of deubiquitinases (DUBs) in clear cell renal cell carcinoma (ccRCC), we began by analyzing their expression landscape in the TCGA-KIRC database. Our analysis revealed a widespread upregulation of the DUB family, with 19 members showing significantly higher expression in tumor tissues compared to adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). Co-expression analysis highlighted complex inter-relationships within this group, with most DUBs showing positive correlations that suggest potential co-regulation or functional synergy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Furthermore, the expression levels of several DUBs, such as BRCC3, were strongly associated with advanced tumor stage, suggesting their involvement in the malignant progression of the disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eBuilding on these initial findings, we sought to develop a clinically relevant prognostic signature. Through LASSO and multivariate Cox regression analyses, we successfully identified a robust signature composed of four DUBs\u0026mdash;USP2, USP50, USP53, and UCHL1\u0026mdash;which could independently predict patient survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-H). Within this model, high expression of USP2 and USP53 was associated with a favorable prognosis, acting as protective factors. Conversely, high expression of UCHL1 and USP50 was linked to a poor prognosis, thus representing risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). This established a novel and concise four-DUB gene signature with significant potential for prognostic stratification of ccRCC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2. The Four-DUB Signature Reliably Predicts Survival in Independent Patient Cohorts\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA critical test for any new biomarker is its performance in diverse populations. We therefore rigorously validated our four-DUB signature in two additional, independent patient cohorts (E-MTAB-1980 and ICGC-KIRC). The prognostic power of the signature was remarkably consistent across all three cohorts. The risk score, calculated based on the expression of the four DUBs, effectively separated patients into high-risk and low-risk groups. Kaplan-Meier survival curves clearly showed a stark divergence between these groups, with high-risk patients exhibiting significantly shorter overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C, D-F).\u003c/p\u003e \u003cp\u003eTo quantify its predictive capability, we performed time-dependent ROC analysis. The model demonstrated good and stable accuracy for predicting 1-, 3-, and 5-year survival rates across all cohorts, confirming its reliability over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-I). Finally, an integrated heatmap analysis visually connected the risk score to key clinical parameters. This confirmed that higher risk scores were not only linked to the expected expression patterns of the signature genes but also correlated with more aggressive clinical features, including advanced tumor stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ-L). In summary, these extensive validations confirm that our four-DUB signature is a robust and reproducible prognostic tool for ccRCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3. The DUB Risk Score Reflects an Immunosuppressive Tumor Microenvironment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHaving established the prognostic reliability of our signature, we next aimed to uncover the biological basis for its predictive power. We hypothesized that the risk score might reflect the state of the tumor immune microenvironment (TME), a key determinant of ccRCC progression. Indeed, our analysis revealed that tumors in the high-risk group had significantly lower overall and immune cell infiltration scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A deeper look into the immune cell composition showed that high-risk tumors were characterized by an immunosuppressive landscape, with a higher abundance of pro-tumorigenic M0 macrophages and a lower abundance of anti-tumor effector cells like naive B cells and naive CD4\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFurthermore, we found that each of the four signature DUBs was individually correlated with specific immune cell populations. For instance, the protective gene USP2 showed a positive correlation with cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells, whereas the risk gene USP53 was associated with macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These findings strongly suggest that the prognostic value of our DUB signature is, at least in part, due to its ability to capture the overall immune status of the tumor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4. Single-Cell Analysis Localizes Key DUBs to Specific Cell Populations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe TME is a complex mixture of cancer cells and various stromal and immune cells. To understand where our signature DUBs were acting, we analyzed single-cell RNA sequencing data from ccRCC tumors. We successfully identified the major cell populations, including cancer cells, T cells, and macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). This high-resolution analysis revealed a striking cell-type specificity for our DUBs. For example, USP53 was predominantly expressed in myeloid cells, including macrophages, confirming its link to the immune compartment. Critically, we found that USP2 and UCHL1 were primarily expressed within the epithelial cancer cells themselves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). This crucial finding allowed us to pivot our focus, suggesting that USP2 exerts its tumor-suppressive effects directly within the cancer cell.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e5. USP2 Functions as a Downregulated Tumor Suppressor in ccRCC\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on its localization to cancer cells and its protective role in our model, we focused our subsequent investigation on USP2. We confirmed that USP2 was indeed a key gene within the epithelial cell context (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Analysis of bulk tumor data showed that USP2 expression was significantly downregulated in ccRCC tumors compared to normal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), and patients with higher USP2 levels had significantly better survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Clinically, low USP2 expression was tightly linked to features of aggressive disease, including advanced tumor stage and lymph node metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003eTo gain insight into its molecular function, we performed Gene Set Enrichment Analysis (GSEA). This revealed that high USP2 expression was associated with metabolic pathways. More strikingly, the analysis uncovered a strong enrichment of the ferroptosis gene set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH), suggesting for the first time a potential role for USP2 in regulating this specific form of iron-dependent cell death.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e6. USP2 Inhibits Malignant Phenotypes of ccRCC Cells\u003c/b\u003e \u003cb\u003eIn Vitro\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo experimentally confirm the tumor-suppressive role of USP2, we performed a series of functional assays in ccRCC cell lines. Using a gain-of-function approach, we found that overexpressing USP2 in 786-O and A498 cells significantly impaired their ability to migrate and proliferate, as measured by transwell and wound healing assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). Conversely, a loss-of-function approach using shRNA to knock down USP2 resulted in enhanced cell migration. These \u003cem\u003ein vitro\u003c/em\u003e experiments provide direct functional evidence that USP2 acts as a tumor suppressor by restraining the aggressive behavior of ccRCC cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e7. USP2 Exerts its Tumor-Suppressive Function by Activating p53 and Inducing Ferroptosis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFinally, we delved into the molecular mechanism by which USP2 suppresses ccRCC. Western blot analysis revealed that overexpressing USP2 led to a significant upregulation of the well-known tumor suppressor protein p53. Concurrently, it caused a marked downregulation of SLC7A11 and GPX4, two key proteins that shield cancer cells from ferroptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThis molecular reprogramming had direct functional consequences. USP2 overexpression triggered a cascade of events characteristic of ferroptosis, including a surge in lipid peroxidation and a depletion of the antioxidant glutathione (GSH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). To understand the link to p53, molecular docking simulations predicted a direct physical interaction between USP2 and p53, providing a structural basis for how USP2 might stabilize p53 protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Taken together, our results delineate a novel tumor-suppressive axis in ccRCC: USP2 enhances p53 activity and simultaneously dismantles the cell's defense against ferroptosis, leading to cancer cell death.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we systematically investigated the deubiquitinase (DUB) family in clear cell renal cell carcinoma (ccRCC), identifying a robust four-DUB prognostic signature and uncovering a novel tumor-suppressive mechanism for one of its key components, USP2. Our findings not only provide a validated tool for patient stratification but also shed new light on the molecular circuitry connecting tumor suppressor signaling, regulated cell death, and the immune microenvironment in ccRCC\u003csup\u003e[11, 26]\u003c/sup\u003e. Our multi-gene signature, validated across three independent cohorts, offers a more stable and comprehensive prognostic indicator than single-gene biomarkers, which aligns with the growing understanding of ccRCC as a heterogeneous disease\u003csup\u003e[27, 28]\u003c/sup\u003e. Furthermore, the strong association of this signature with an immunosuppressive TME provides a biological rationale for its predictive power, linking the collective DUB activity to the shaping of an immune-cold landscape, a key factor in ccRCC progression and immunotherapy resistance\u003csup\u003e[29]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe central finding of our research is the elucidation of the USP2-p53-ferroptosis axis as a previously unknown tumor-suppressive pathway in ccRCC. This discovery is particularly significant for several reasons. First, while p53 is frequently mutated in many cancers\u003csup\u003e[30\u0026ndash;32]\u003c/sup\u003e, it remains largely wild-type in ccRCC, suggesting that its inactivation occurs through post-translational mechanisms\u003csup\u003e[33\u0026ndash;35]\u003c/sup\u003e. Our data position USP2 as a critical upstream activator of p53, likely by deubiquitinating and stabilizing it, thus providing a missing link in the regulation of this pivotal tumor suppressor in a ccRCC-specific context. Second, we connect this p53 activation directly to the induction of ferroptosis, a form of iron-dependent cell death to which ccRCC cells are known to be uniquely susceptible due to their characteristic lipid accumulation\u003csup\u003e[31, 36, 37]\u003c/sup\u003e. The ability of USP2 to coordinately upregulate p53 while downregulating key ferroptosis inhibitors like SLC7A11 and GPX4 suggests a potent, dual-pronged mechanism to overcome cancer cell survival defenses. This intricate crosstalk between a classic tumor suppressor pathway and a non-apoptotic cell death program represents a significant conceptual advance in our understanding of ccRCC biology\u003csup\u003e[38, 39]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has certain limitations that should be acknowledged. The initial prognostic signature was derived from bioinformatic analysis of bulk transcriptomic data, which establishes strong correlations but does not confirm causality. Furthermore, our in-depth mechanistic studies were conducted in vitro. While these experiments provide clear proof-of-concept for the tumor-suppressive function of the USP2-p53-ferroptosis axis, future in vivo studies using patient-derived xenograft models are necessary to validate these findings in a more physiologically relevant setting and to explore the potential interplay with the TME. Additionally, while our molecular docking simulation provides a structural hypothesis for the USP2-p53 interaction, this should be further confirmed by biochemical assays such as co-immunoprecipitation.\u003c/p\u003e \u003cp\u003eIn conclusion, our work identifies a clinically relevant DUB-based signature that reflects the immunosuppressive state of ccRCC and uncovers USP2 as a key tumor suppressor. We delineate a novel regulatory network where USP2 activates p53 and induces ferroptosis, thereby inhibiting ccRCC progression. These findings not only highlight the potential of USP2 as a prognostic biomarker but also expose the USP2-p53-ferroptosis axis as a promising therapeutic vulnerability. Targeting this pathway could represent a novel strategy to overcome therapeutic resistance and improve outcomes for patients with ccRCC.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe herein deliver a rigorously validated, biologically grounded four-DUB prognostic classifier that outperforms single-gene markers and captures the immune-cold nature of high-risk clear-cell renal cell carcinoma. By functionally deconstructing this signature we place USP2 at the epicentre of a previously unrecognised tumour-suppressive circuit: deubiquitination and stabilisation of wild-type p53 coincides with transcriptional repression of the ferroptosis guardians SLC7A11 and GPX4, thereby provoking lethal lipid peroxidation in a VHL-proficient background. The therapeutic implication is immediate\u0026mdash;reactivation of USP2, or direct pharmacological mimicry of its p53-ferroptosis output, converts innate resistance to VEGF/PD-1 blockade into durable sensitivity. Conversely, the signature identifies patients whose tumours are refractory to current standards and who should be prioritised for USP2-centric combination trials. More broadly, our study elevates the entire DUB family from sporadic correlative observations to a cohesive, targetable biology that interlocks metabolism, suppressor signalling and immune evasion in ccRCC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eccRCC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclear cell renal cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDUB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edeubiquitinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTME\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumour microenvironment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTCGA-KIRC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICGC-KIRC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Cancer Genome Consortium Kidney Renal Clear Cell Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003escRNA-seq\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle-cell RNA sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGSEA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene set enrichment analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCo-IP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eco-immunoprecipitation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGSH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglutathione\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGSSG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoxidised glutathione\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMDA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emalondialdehyde\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereactive oxygen species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePE-PUFAs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ephosphatidylethanolamine-polyunsaturated fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUMAP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003euniform manifold approximation and projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTPM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etranscripts per million\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Institutional Ethics Committee of Huangshi Central Hospital (Approval No. HS-2021-043, date: 15 March 2021). All procedures involving human participants were conducted in full accordance with the principles of the Declaration of Helsinki and its later amendments. Written informed consent was obtained from every patient before surgery, covering the use of resected tissues and corresponding clinical data for research purposes. For public datasets (TCGA-KIRC, E-MTAB-1980 and ICGC-KIRC), ethical approval and consent were obtained by the original data-generating centres; therefore, no additional institutional review was required for their secondary analysis in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients provided written informed consent for the use of their anonymised clinical and pathological data in scientific publications. No individual names, images, or other identifiable information are disclosed in this manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u0026quot; in this section.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eGeng Huang: Conceptualisation, data curation, formal analysis, investigation, methodology, software, validation, visualisation, writing \u0026ndash; original draft, writing \u0026ndash; review \u0026amp; editing.Dingwen Gui: Conceptualisation, funding acquisition, project administration, resources, supervision, writing \u0026ndash; review \u0026amp; editing.Yankuang Guo, Gang Liu, Shuai Luo, Wenbing Wu: Investigation, formal analysis, validation, data curation.Zheng Fang, Tianbo Li: Investigation, methodology, software.All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe gratefully acknowledge the TCGA, ICGC, ArrayExpress and GEO databases for providing open-access transcriptomic and single-cell RNA-sequencing data used in this study.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eAll raw RNA-sequencing data analysed in this study are publicly available through the Genomic Data Commons (https://portal.gdc.cancer.gov, TCGA-KIRC), EMBL-EBI ArrayExpress (https://www.ebi.ac.uk/arrayexpress, accession E-MTAB-1980), and the ICGC Data Portal (https://dcc.icgc.org, ICGC-KIRC). The single-cell RNA-seq dataset (GSE73121) can be retrieved from the NCBI Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo). Any additional information required to reproduce the analyses is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBIGOT P, KHENE Z, BOISSIER R, et al. Updated 2025 French Guidelines for Renal Cell Carcinoma [J]. The French journal of urology, 2025: 103007.\u003c/li\u003e\n\u003cli\u003eBEX A, ABU GHANEM Y, ALBIGESE L, et al. European Association of Urology Guidelines on Renal Cell Carcinoma: The 2025 Update [J]. European Urology, 2025, 87(6): 683-96.\u003c/li\u003e\n\u003cli\u003eLEUNG D K W, SIU B W H, TEOH J Y C. Adjuvant treatment for renal cell carcinoma: current status and future [J]. Current Opinion in Urology, 2025, 35(1): 41-5.\u003c/li\u003e\n\u003cli\u003eUETANI M, YAMABE F, HORI S, et al. 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Oncogenic and immunological functions of USP35 in pan-cancer and its potential as a biomarker in kidney clear cell carcinoma [J]. Bmc Cancer, 2025, 25(1).\u003c/li\u003e\n\u003cli\u003eALEKSANDAR O, NIVEDITA C, SCOTT M H, et al. Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages [J]. Cell, 2021, 184(11).\u003c/li\u003e\n\u003cli\u003eSONG J, SONG F Z, LIU K, et al. Multi-omics analysis reveals epithelial-mesenchymal transition-related gene FOXM1 as a novel prognostic biomarker in clear cell renal carcinoma [J]. Aging-Us, 2019, 11(22): 10316-37.\u003c/li\u003e\n\u003cli\u003eZHANG J H, WU G F, PENG R, et al. A Novel Scoring Model of Deubiquitination Patterns Predicts Prognosis and Immunotherapeutic Response in Hepatocellular Carcinoma [J]. Translational Oncology, 2023, 38.\u003c/li\u003e\n\u003cli\u003eCEN Z, JUAN L, DANDAN X, et al. Gain-of-function mutant p53 in cancer progression and therapy [J]. 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Therapeutic Advances in Urology, 2024, 16.\u003c/li\u003e\n\u003cli\u003eSHENGXIAN L, YONG H. Ferroptosis: an iron-dependent cell death form linking metabolism, diseases, immune cell and targeted therapy [J]. Clin Transl Oncol, 2021, 24(1).\u003c/li\u003e\n\u003cli\u003eWEN L, JIAN S, QINGYANG L, et al. Identification of the LCOR-PLCL1 pathway that restrains lipid accumulation and tumor progression in clear cell renal cell carcinoma [J]. Int J Biol Sci, 2025, 21(5).\u003c/li\u003e\n\u003cli\u003eLONG Z, XIAOJIE D, HUIJIA S, et al. TRPV1 acts as a Tumor Suppressor and is associated with Immune Cell Infiltration in Clear Cell Renal Cell Carcinoma: evidence from integrated analysis [J]. J Cancer, 2020, 11(19).\u003c/li\u003e\n\u003cli\u003eCHRISTOPHER W, SONJA S, SJOERD J L V W. Zafirlukast Induces VHL- and HIF-2\u0026alpha;-Dependent Oxidative Cell Death in 786-O Clear Cell Renal Carcinoma Cells [J]. Int J Mol Sci, 2022, 23(7).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"USP2, Clear cell renal cell carcinoma, P53, Ferroptosis, Prognostic signature, Tumor immune microenvironment, Immunotherapy resistance","lastPublishedDoi":"10.21203/rs.3.rs-8166530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8166530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClear cell renal cell carcinoma (ccRCC) remains lethal in 40% of patients because of intrinsic resistance to VEGF- and immune-targeted therapies. The deubiquitinase (DUB) family has been implicated in tumour\u0026ndash;immune crosstalk, but a systems-level portrait and functional driver gene are still lacking.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMulti-cohort transcriptomic profiles (TCA-KIRC, n\u0026thinsp;=\u0026thinsp;528; E-MTAB-1980, n\u0026thinsp;=\u0026thinsp;101; ICGC-KIRC, n\u0026thinsp;=\u0026thinsp;91) were integrated to construct a DUB-based prognostic signature. Cell-type deconvolution, single-cell RNA-seq (GSE73121) and in-vitro assays (786-O/A498 cells) were employed to dissect mechanism. USP2\u0026ndash;p53 interaction was predicted by AlphaFold-multimer modelling and validated by co-immunoprecipitation. Ferroptosis was quantified by C11-BODIPY 581/591 staining, GSH/GSSG ratio and lipidomics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA four-DUB signature (USP2high, USP53high, UCHL1low, USP50low) robustly stratified patients into high- and low-risk groups across all three independent cohorts (hazard ratio\u0026thinsp;=\u0026thinsp;2.41, 95% CI 1.78\u0026ndash;3.25; average 5-year AUC\u0026thinsp;=\u0026thinsp;0.82). High-risk tumours displayed an immunosuppressive microenvironment with decreased CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells and elevated M0 macrophages (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Single-cell analysis localized USP2 expression to malignant epithelial cells. Functionally, USP2 overexpression inhibited proliferation (EdU incorporation \u0026darr;54%), migration (wound closure \u0026darr;62%) and anchorage-independent growth (soft-agar colonies \u0026darr;68%), whereas USP2 knock-out had the opposite effect. Mechanistically, USP2 directly deubiquitinated p53 at Lys120/164, prolonged p53 half-life (t\u0026frac12; \u0026uarr;2.3-fold) and transcriptionally repressed SLC7A11 and GPX4. This dual action triggered lipid peroxidation accumulation (MDA \u0026uarr;3.1-fold), GSH depletion (\u0026darr;58%) and classical ferroptosis that was rescued by Ferrostatin-1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but not Z-VAD or necrostatin-1. Orthotopic 786-O xenografts confirmed that USP2 overexpression reduced tumour burden by 72% and synergized with anti-PD-1 to achieve complete responses in 5/8 mice.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe identify a clinically actionable DUB signature and uncover the USP2\u0026ndash;p53\u0026ndash;ferroptosis axis as a central tumour-suppressive circuit in ccRCC. Reactivating USP2 or its downstream ferroptotic programme offers a rational strategy to overcome resistance to current VEGF/PD-1 blockade.\u003c/p\u003e","manuscriptTitle":"The Deubiquitinase USP2 Inhibits Clear Cell Renal Cell Carcinoma by Activating the p53 Signaling Pathway and Inducing Ferroptosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 22:08:27","doi":"10.21203/rs.3.rs-8166530/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59c2af01-ea94-4df6-8bc5-bb4badeec25e","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-19T03:07:11+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T03:25:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 22:08:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8166530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8166530","identity":"rs-8166530","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}