A Novel Cuproptosis-Related Two-Gene Signature for Prognostic Prediction in Bladder Cancer

A Novel Cuproptosis-Related Two-Gene Signature for Prognostic Prediction in Bladder Cancer | 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 A Novel Cuproptosis-Related Two-Gene Signature for Prognostic Prediction in Bladder Cancer Yuhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9562963/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 Bladder cancer (BLCA) is a major urological malignancy with limited prognostic biomarkers. Cuproptosis, a copper-dependent programmed cell death pathway, has attracted interest in cancer biology, yet its prognostic relevance in BLCA remains underexplored. Methods Transcriptomic and clinical data for 402 BLCA patients were obtained from TCGA via UCSC Xena. Ten cuproptosis-related genes were screened by univariate Cox regression, followed by LASSO-penalized Cox regression to construct a risk score model. The signature was externally validated in GSE32894 (n = 224). Co-expressed genes were subjected to GO and KEGG enrichment analyses. Model performance was evaluated by time-dependent ROC curves, and multivariate Cox regression assessed independence from clinical covariates. Results LIPT1 (HR = 0.527, P = 5.11 × 10⁻⁶) and DLAT (HR = 1.467, P = 8.99 × 10⁻³) were significantly associated with overall survival (OS) and retained in the LASSO model (λ = 0.00344). The risk score was: Risk Score = (− 0.607 × LIPT1) + (0.283 × DLAT). High-risk patients showed significantly shorter OS in the TCGA cohort (P = 0.00013), confirmed by external validation (P = 0.00037). AUC values for 1-, 3-, and 5-year OS were 0.640, 0.611, and 0.620. Enrichment analyses linked co-expressed genes to the TCA cycle, lipoic acid metabolism, and oxidative phosphorylation. The risk score remained an independent predictor after adjusting for age (HR = 2.175, 95% CI: 1.638–2.889, P = 8.31 × 10⁻⁸). Conclusions A concise two-gene cuproptosis-related signature (LIPT1 and DLAT) was developed and independently validated, demonstrating robust prognostic performance in BLCA. Bioinformatics bladder cancer cuproptosis LIPT1 DLAT prognostic signature TCGA external validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Simple Summary Bladder cancer remains one of the most prevalent urological malignancies, yet reliable and practical prognostic biomarkers are still lacking. Cuproptosis is a newly identified form of copper-dependent programmed cell death that differs mechanistically from apoptosis, ferroptosis, and necroptosis. In this study, we investigated whether genes essential to the cuproptosis pathway could serve as prognostic indicators in bladder cancer. Using publicly available gene expression and clinical data from 402 patients, we constructed a two-gene risk score model based on LIPT1 and DLAT. This signature successfully separated patients into high-risk and low-risk groups with substantially different survival outcomes, and the finding was corroborated in an independent external cohort. The results suggest that a concise, biology-grounded gene signature may offer useful prognostic information for bladder cancer patients. 1. Introduction Bladder cancer (BLCA) ranks as the seventh most commonly diagnosed malignancy among men globally, with approximately 75% of new cases presenting as non-muscle-invasive disease ( 1 , 2 ). Although surgical techniques and adjuvant therapies have advanced substantially, patients continue to face considerable risks of recurrence and metastasis, and long-term survival has not improved commensurately ( 3 , 4 ). A persistent challenge is the scarcity of easily accessible biomarkers that can reliably identify individuals at high risk of adverse outcomes. Cuproptosis is a copper-dependent mode of programmed cell death that was formally characterized in 2022 by Tsvetkov and colleagues. Unlike apoptosis, necroptosis, or ferroptosis, cuproptosis proceeds through a biochemically distinct route that depends on the lipoylation of mitochondrial enzymes in the tricarboxylic acid (TCA) cycle ( 4 ). Intracellular copper overload triggers the aggregation of these lipoylated proteins, ultimately leading to cell death. The discovery of this pathway has prompted several groups to investigate the roles of cuproptosis-related genes (CRGs) across a range of malignancies, including hepatocellular carcinoma, lung adenocarcinoma, and melanoma ( 5 ). In bladder cancer, preliminary multi-omics analyses have suggested that CRGs may contribute to remodeling of the tumor microenvironment and to immune cell infiltration patterns, raising the possibility that these genes could carry prognostic information ( 8 ). A small number of studies have already explored cuproptosis-based prognostic models in BLCA. Hao et al. devised a combined ferroptosis/cuproptosis-related gene signature for muscle-invasive bladder cancer that performed favorably in both training and external validation cohorts ( 6 ). Ye et al. independently reported a cuproptosis-focused risk model that was linked not only to prognosis but also to immune infiltration characteristics ( 7 ). While these studies establish the feasibility of the approach, they often employed relatively large gene panels, which may reduce translational practicality. In the present study, we set out to determine whether a deliberately parsimonious signature—limited to the ten core CRGs originally identified as essential for cuproptosis—could achieve comparable prognostic stratification in BLCA. We integrated univariate Cox regression with LASSO-penalized multivariate modeling to select the most informative genes, validated the resulting model in an independent external cohort, and performed enrichment analyses to gain insight into the underlying biology. 2. Materials and Methods 2.1 Data Acquisition and Processing Gene expression data (RNA-seq, Illumina HiSeq 2000 platform) and corresponding clinical annotations for BLCA patients were downloaded from the UCSC Xena platform ( https://xenabrowser.net/ ). The expression matrix comprised 20,530 genes assayed across 426 samples, with values reported as log2(RSEM + 1) normalized counts. The clinical dataset contained 130 variables for 436 patients. Samples lacking survival information or with a follow-up time of zero were removed, resulting in a final analytical cohort of 402 patients. 2.2 Cuproptosis-Related Gene Set Ten genes that have been shown to be integral to the cuproptosis pathway were selected from the published literature ( 5 ): FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A. All ten were present in the TCGA-BLCA expression matrix. 2.3 Statistical Analysis for Model Construction All statistical analyses were performed in R (version 4.5.3). Univariate Cox proportional hazards regression was first applied to each of the ten CRGs to evaluate their individual associations with overall survival (OS). Genes meeting a relaxed significance threshold of P < 0.1 were taken forward as candidates. LASSO-penalized Cox regression with 10-fold cross-validation was then performed using the glmnet package to select the most informative gene combination. The penalty parameter lambda was chosen at the value that minimized the cross-validation error. Genes with non-zero coefficients at the optimal lambda were retained for the final multivariate Cox model. For each patient, a risk score was calculated as a linear combination of gene expression values weighted by the corresponding regression coefficients. Patients were stratified into high-risk and low-risk groups based on the median risk score, and Kaplan-Meier survival curves were compared using the log-rank test. 2.4 External Validation The derived risk model was externally validated using the GSE32894 dataset downloaded from the Gene Expression Omnibus (GEO). This dataset contains gene expression profiles and survival data for 224 bladder cancer patients. Risk scores were computed using the same formula and coefficients obtained from the TCGA training cohort. Patients in GSE32894 were likewise stratified by the median risk score, and Kaplan-Meier analysis was conducted. 2.5 Functional Enrichment Analysis Genes co-expressed with either LIPT1 or DLAT were identified from the TCGA expression matrix (|Pearson correlation coefficient| > 0.4, P < 0.05). These co-expressed genes were subjected to Gene Ontology (GO) Biological Process enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment using the clusterProfiler package. The Benjamini–Hochberg procedure was applied to control the false discovery rate (q-value < 0.05). 2.6 Model Evaluation and Independence Testing Time-dependent ROC curves were generated to assess the predictive accuracy of the risk score for 1-, 3-, and 5-year OS using the timeROC package. To determine whether the risk score was an independent prognostic factor, a multivariate Cox model was fitted that included the risk score and age at initial pathologic diagnosis as covariates. 3. Results 3.1 Patient Characteristics The TCGA training cohort consisted of 402 BLCA patients, among whom 177 deaths (44.0%) were recorded and 225 patients (56.0%) were alive at last follow-up. The external validation cohort (GSE32894) included 224 patients, with 25 deaths (11.2%) and 199 patients (88.8%) censored. 3.2 Univariate Associations Between CRGs and Overall Survival Of the ten CRGs examined, LIPT1 (HR = 0.527, 95% CI: 0.400–0.694, P = 5.11 × 10⁻⁶) and DLAT (HR = 1.467, 95% CI: 1.100–1.955, P = 8.99 × 10⁻³) were significantly associated with OS in univariate Cox regression (Table 1 ). The remaining eight genes did not reach the pre-specified threshold of P < 0.1. Table 1 Univariate Cox regression analysis of ten cuproptosis-related genes in BLCA. Gene HR Lower 95% CI Upper 95% CI P-value Significance FDX1 0.946 0.768 1.166 0.606 ns LIAS 0.958 0.749 1.226 0.734 ns LIPT1 0.527 0.400 0.694 5.11×10⁻⁶ *** DLD 0.992 0.755 1.305 0.955 ns DLAT 1.467 1.100 1.955 8.99×10⁻³ ** PDHA1 0.986 0.745 1.306 0.924 ns PDHB 1.163 0.846 1.598 0.351 ns MTF1 1.121 0.893 1.408 0.326 ns GLS 0.992 0.833 1.181 0.925 ns CDKN2A 0.989 0.948 1.032 0.600 ns *HR: Hazard Ratio; CI: Confidence Interval; ns: not significant; ** P < 0.01; *** P < 0.001.* 3.3 Construction of the Prognostic Gene Signature Both LIPT1 and DLAT were taken forward to LASSO regression. The 10-fold cross-validation procedure identified an optimal lambda of 0.00344, at which point both genes retained non-zero coefficients. The resulting risk score formula was: Risk Score = (− 0.607 × LIPT1 expression) + (0.283 × DLAT expression) The negative coefficient for LIPT1 is concordant with its protective effect in the univariate analysis, while the positive coefficient for DLAT is concordant with its role as a risk-associated factor. 3.4 Survival Stratification in the TCGA Cohort When the 402 TCGA patients were divided according to the median risk score, the high-risk group (n = 201) showed significantly shorter OS than the low-risk group (n = 201; log-rank P = 0.00013; Fig. 1 ). 3.5 External Validation in GSE32894 Application of the same risk score formula to the independent GSE32894 cohort likewise produced a significant separation between high-risk and low-risk groups (log-rank P = 0.00037; Fig. 2 ). 3.6 Functional Enrichment of Co-expressed Genes A total of 328 genes were found to be co-expressed with LIPT1 or DLAT. GO Biological Process enrichment revealed that these genes were predominantly involved in nucleocytoplasmic transport, RNA splicing, and protein localization to the nucleus. KEGG pathway analysis further identified 228 significantly enriched pathways, among which the most relevant to cuproptosis biology were the citrate cycle (TCA cycle), lipoic acid metabolism, pyruvate metabolism, and oxidative phosphorylation (Fig. 3 ). 3.7 Time-Dependent ROC Analysis The predictive accuracy of the risk score was assessed using time-dependent ROC curves. The AUC values for 1-, 3-, and 5-year OS were 0.640, 0.611, and 0.620, respectively (Fig. 4 ). 3.8 Independent Prognostic Value In a multivariate Cox model adjusting for age at diagnosis, the risk score remained a strong independent predictor of OS (HR = 2.175, 95% CI: 1.638–2.889, P = 8.31 × 10⁻⁸; Table 2 ). Age was also independently associated with survival (HR = 1.027, 95% CI: 1.012–1.042, P = 3.50 × 10⁻⁴). Table 2 Multivariate Cox analysis incorporating the risk score and age. Variable HR Lower 95% CI Upper 95% CI P-value Risk Score 2.175 1.638 2.889 8.31×10⁻⁸ Age 1.027 1.012 1.042 3.50×10⁻⁴ 4. Discussion In this study, we constructed and independently validated a parsimonious two-gene prognostic signature derived from the core cuproptosis machinery. The signature successfully discriminated between patients with favorable and adverse survival outcomes in both the TCGA training cohort and an external GEO dataset. The enrichment of co-expressed genes in the TCA cycle, lipoic acid metabolism, and oxidative phosphorylation pathways lends biological plausibility to the model and is consistent with the known biochemical mechanism of cuproptosis ( 5 ). The time-dependent ROC analysis indicated reasonable predictive accuracy, and the risk score remained prognostic after adjustment for age, supporting its potential clinical utility. Several limitations should be acknowledged. Both the training and external validation datasets are retrospective in nature, and despite the consistency of the results across two independent cohorts, the GSE32894 dataset has a relatively low event rate (11.2%), which may affect the precision of the survival estimates. Additionally, the study relied entirely on publicly available transcriptomic data, and no wet-laboratory experiments were performed to confirm the expression levels or functional roles of LIPT1 and DLAT in bladder cancer cell lines. Future work incorporating prospective cohorts, additional molecular characterizations such as mutation and immune infiltration data, and functional validation experiments would further strengthen the evidence base for this signature. 5. Conclusions We developed a novel cuproptosis-related two-gene signature consisting of LIPT1 and DLAT that effectively stratifies BLCA patients into risk groups with significantly different overall survival. The signature was externally validated and provides prognostic information independent of age. This concise model may offer a practical framework for individualized survival assessment in bladder cancer. Declarations Ethics approval and consent to participate Not applicable. Patient consent for publication Not applicable. Competing interests The author declares that he has no competing interests. Use of artificial intelligence tools During the preparation of this manuscript, the author used an AI-assisted language model to improve the readability and language. The author subsequently revised and edited the content and takes full responsibility for the final version. Funding No funding was received. Authors' contributions YHZ conceived and designed the study, performed all data analysis, interpreted the results, and wrote the manuscript. The author confirms the authenticity of all the raw data. Acknowledgements The author thanks the TCGA Research Network, the UCSC Xena team, and the GEO database for making the data publicly available. Availability of data and materials The data that support the findings of this study are openly available in TCGA (via UCSC Xena, https://xenabrowser.net/ ) and GEO (accession: GSE32894). The complete reproducible R code is provided as Supplementary File S1. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249 Babjuk M, Burger M, Compérat E, Gontero P, Mostafid AH, Palou J, van Rhijn BWG, Rouprêt M, Shariat SF, Sylvester R (2019) European Association of Urology guidelines on non-muscle-invasive bladder cancer (TaT1 and carcinoma in situ) – 2019 update. Eur Urol 76:639–657 Alfred Witjes J, Lebret T, Compérat EM, Cowan NC, De Santis M, Bruins HM, Hernández V, Espinós EL, Dunn J, Rouanne M et al (2017) Updated 2016 EAU guidelines on muscle-invasive and metastatic bladder cancer. Eur Urol 71:462–475 Tsvetkov P, Coy S, Petrova B, Dreishpoon M, Verma A, Abdusamad M, Rossen J, Joesch-Cohen L, Humeidi R, Spangler RD et al (2022) Copper induces cell death by targeting lipoylated TCA cycle proteins. Science 375:1254–1261 Li SR, Bu LL, Cai L (2022) Cuproptosis: lipoylated TCA cycle proteins-mediated novel cell death pathway. Signal Transduct Target Ther 7:158 Hao S, Zhang J, Li T, Wang X, Liu B, Chen Z, Zheng J, Sun Y, Zhu X (2024) Development of prognostic model incorporating a ferroptosis/cuproptosis-related signature and mutational landscape analysis in muscle-invasive bladder cancer. BMC Cancer 24:938 Sheng H, Gu J, Huang Y, Kołat D, Shi G, Yan L, Ye D (2024) Cuproptosis-related signature predicts prognosis and indicates tumor immune infiltration in bladder cancer. Transl Androl Urol 13:2280–2293 Yang CY, A RN, Liu JM (2024) Prognostic signature and immune/drug therapy analysis of cuproptosis-related immune checkpoint genes in bladder cancer. J Hainan Med Univ 30:262–272 Additional Declarations The authors declare no competing interests. Supplementary Files BLCACuproptosisCompleteCode.r BLCA_Cuproptosis_Complete_Code Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9562963","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631613823,"identity":"ca91e314-18ae-4400-bb29-82f5896463f4","order_by":0,"name":"Yuhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDACZjBpkwCmEgqI15KWwMAG0mJAvF2HIVoYiNHCd5z32Iefbefz+OW7Ez88MGCQ5xc7gF+L5GG+5Jm9bbeLJdt4N0sAHWY4c3YCfi0Gh3mMGXjbbiduOMa7AaQlweA2EVoY/7adA2nZ/INoLcy8bQdAWrYRZ4skSIvMueTEmW252ywSDCQI+4Xv/Bljxjdldon9zGc33/xRYSPPL01AC8MBIGZkg3MlCCiHaWH4Q4TCUTAKRsEoGLkAAFgaQcOKAuWXAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-2769-467X","institution":"Guangling College, Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Yuhan","suffix":""}],"badges":[],"createdAt":"2026-04-29 08:42:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9562963/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9562963/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108409730,"identity":"127c9074-be23-4d84-ac0e-68d46ac50fa4","added_by":"auto","created_at":"2026-05-04 10:02:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99534,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‑Meier curves comparing overall survival between the high‑risk and low‑risk groups in the TCGA‑BLCA training cohort (P = 0.00013, log‑rank test).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9562963/v1/0bd063d42c5871d196c11e75.png"},{"id":108493178,"identity":"63abba5c-e207-4a0e-ab9d-3d06438ff44e","added_by":"auto","created_at":"2026-05-05 09:59:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97790,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‑Meier curves comparing overall survival between the high‑risk and low‑risk groups in the GSE32894 validation cohort (P = 0.00037, log‑rank test).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9562963/v1/cce370a680d57a04af4be8ec.png"},{"id":108492941,"identity":"afadae5e-2347-4864-a735-e8f41db8d2ee","added_by":"auto","created_at":"2026-05-05 09:59:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":689447,"visible":true,"origin":"","legend":"\u003cp\u003e(A) GO Biological Process enrichment of genes co‑expressed with LIPT1 or DLAT. (B) KEGG pathway enrichment of the same gene set, highlighting metabolism‑related pathways.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9562963/v1/217fc1c9a944e110027f4b4b.jpeg"},{"id":108409732,"identity":"69de7552-dafc-46fc-a86b-a6d27f77be36","added_by":"auto","created_at":"2026-05-04 10:02:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44090,"visible":true,"origin":"","legend":"\u003cp\u003eTime‑dependent ROC curves evaluating the predictive performance of the risk score for 1‑, 3‑, and 5‑year overall survival.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9562963/v1/f71640f5fc5e986bca803c02.png"},{"id":108803920,"identity":"23f1d82f-a56c-4de5-97c6-6995843e2329","added_by":"auto","created_at":"2026-05-08 15:10:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1188431,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9562963/v1/5fb575ad-913c-4176-b50d-38c2532d0339.pdf"},{"id":108493305,"identity":"ed666c1b-2b2a-4acf-8a4a-a251cab03b69","added_by":"auto","created_at":"2026-05-05 09:59:52","extension":"r","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9585,"visible":true,"origin":"","legend":"\u003cp\u003eBLCA_Cuproptosis_Complete_Code\u003c/p\u003e","description":"","filename":"BLCACuproptosisCompleteCode.r","url":"https://assets-eu.researchsquare.com/files/rs-9562963/v1/9b853077a2fd9224985cd574.r"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA Novel Cuproptosis-Related Two-Gene Signature for Prognostic Prediction in Bladder Cancer\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Simple Summary","content":"\u003cp\u003eBladder cancer remains one of the most prevalent urological malignancies, yet reliable and practical prognostic biomarkers are still lacking. Cuproptosis is a newly identified form of copper-dependent programmed cell death that differs mechanistically from apoptosis, ferroptosis, and necroptosis. In this study, we investigated whether genes essential to the cuproptosis pathway could serve as prognostic indicators in bladder cancer. Using publicly available gene expression and clinical data from 402 patients, we constructed a two-gene risk score model based on LIPT1 and DLAT. This signature successfully separated patients into high-risk and low-risk groups with substantially different survival outcomes, and the finding was corroborated in an independent external cohort. The results suggest that a concise, biology-grounded gene signature may offer useful prognostic information for bladder cancer patients.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eBladder cancer (BLCA) ranks as the seventh most commonly diagnosed malignancy among men globally, with approximately 75% of new cases presenting as non-muscle-invasive disease (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although surgical techniques and adjuvant therapies have advanced substantially, patients continue to face considerable risks of recurrence and metastasis, and long-term survival has not improved commensurately (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). A persistent challenge is the scarcity of easily accessible biomarkers that can reliably identify individuals at high risk of adverse outcomes.\u003c/p\u003e \u003cp\u003eCuproptosis is a copper-dependent mode of programmed cell death that was formally characterized in 2022 by Tsvetkov and colleagues. Unlike apoptosis, necroptosis, or ferroptosis, cuproptosis proceeds through a biochemically distinct route that depends on the lipoylation of mitochondrial enzymes in the tricarboxylic acid (TCA) cycle (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Intracellular copper overload triggers the aggregation of these lipoylated proteins, ultimately leading to cell death. The discovery of this pathway has prompted several groups to investigate the roles of cuproptosis-related genes (CRGs) across a range of malignancies, including hepatocellular carcinoma, lung adenocarcinoma, and melanoma (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In bladder cancer, preliminary multi-omics analyses have suggested that CRGs may contribute to remodeling of the tumor microenvironment and to immune cell infiltration patterns, raising the possibility that these genes could carry prognostic information (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA small number of studies have already explored cuproptosis-based prognostic models in BLCA. Hao et al. devised a combined ferroptosis/cuproptosis-related gene signature for muscle-invasive bladder cancer that performed favorably in both training and external validation cohorts (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Ye et al. independently reported a cuproptosis-focused risk model that was linked not only to prognosis but also to immune infiltration characteristics (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). While these studies establish the feasibility of the approach, they often employed relatively large gene panels, which may reduce translational practicality.\u003c/p\u003e \u003cp\u003eIn the present study, we set out to determine whether a deliberately parsimonious signature\u0026mdash;limited to the ten core CRGs originally identified as essential for cuproptosis\u0026mdash;could achieve comparable prognostic stratification in BLCA. We integrated univariate Cox regression with LASSO-penalized multivariate modeling to select the most informative genes, validated the resulting model in an independent external cohort, and performed enrichment analyses to gain insight into the underlying biology.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Acquisition and Processing\u003c/h2\u003e \u003cp\u003eGene expression data (RNA-seq, Illumina HiSeq 2000 platform) and corresponding clinical annotations for BLCA patients were downloaded from the UCSC Xena platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The expression matrix comprised 20,530 genes assayed across 426 samples, with values reported as log2(RSEM\u0026thinsp;+\u0026thinsp;1) normalized counts. The clinical dataset contained 130 variables for 436 patients. Samples lacking survival information or with a follow-up time of zero were removed, resulting in a final analytical cohort of 402 patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cuproptosis-Related Gene Set\u003c/h2\u003e \u003cp\u003eTen genes that have been shown to be integral to the cuproptosis pathway were selected from the published literature (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e): FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A. All ten were present in the TCGA-BLCA expression matrix.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis for Model Construction\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed in R (version 4.5.3). Univariate Cox proportional hazards regression was first applied to each of the ten CRGs to evaluate their individual associations with overall survival (OS). Genes meeting a relaxed significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were taken forward as candidates. LASSO-penalized Cox regression with 10-fold cross-validation was then performed using the glmnet package to select the most informative gene combination. The penalty parameter lambda was chosen at the value that minimized the cross-validation error. Genes with non-zero coefficients at the optimal lambda were retained for the final multivariate Cox model. For each patient, a risk score was calculated as a linear combination of gene expression values weighted by the corresponding regression coefficients. Patients were stratified into high-risk and low-risk groups based on the median risk score, and Kaplan-Meier survival curves were compared using the log-rank test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 External Validation\u003c/h2\u003e \u003cp\u003eThe derived risk model was externally validated using the GSE32894 dataset downloaded from the Gene Expression Omnibus (GEO). This dataset contains gene expression profiles and survival data for 224 bladder cancer patients. Risk scores were computed using the same formula and coefficients obtained from the TCGA training cohort. Patients in GSE32894 were likewise stratified by the median risk score, and Kaplan-Meier analysis was conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGenes co-expressed with either LIPT1 or DLAT were identified from the TCGA expression matrix (|Pearson correlation coefficient| \u0026gt; 0.4, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These co-expressed genes were subjected to Gene Ontology (GO) Biological Process enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment using the clusterProfiler package. The Benjamini\u0026ndash;Hochberg procedure was applied to control the false discovery rate (q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Model Evaluation and Independence Testing\u003c/h2\u003e \u003cp\u003eTime-dependent ROC curves were generated to assess the predictive accuracy of the risk score for 1-, 3-, and 5-year OS using the timeROC package. To determine whether the risk score was an independent prognostic factor, a multivariate Cox model was fitted that included the risk score and age at initial pathologic diagnosis as covariates.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient Characteristics\u003c/h2\u003e \u003cp\u003eThe TCGA training cohort consisted of 402 BLCA patients, among whom 177 deaths (44.0%) were recorded and 225 patients (56.0%) were alive at last follow-up. The external validation cohort (GSE32894) included 224 patients, with 25 deaths (11.2%) and 199 patients (88.8%) censored.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate Associations Between CRGs and Overall Survival\u003c/h2\u003e \u003cp\u003eOf the ten CRGs examined, LIPT1 (HR\u0026thinsp;=\u0026thinsp;0.527, 95% CI: 0.400\u0026ndash;0.694, P\u0026thinsp;=\u0026thinsp;5.11 \u0026times; 10⁻⁶) and DLAT (HR\u0026thinsp;=\u0026thinsp;1.467, 95% CI: 1.100\u0026ndash;1.955, P\u0026thinsp;=\u0026thinsp;8.99 \u0026times; 10⁻\u0026sup3;) were significantly associated with OS in univariate Cox regression (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The remaining eight genes did not reach the pre-specified threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Cox regression analysis of ten cuproptosis-related genes in BLCA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIPT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.11\u0026times;10⁻⁶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.99\u0026times;10⁻\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDHA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDKN2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*HR: Hazard Ratio; CI: Confidence Interval; ns: not significant; ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.*\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Construction of the Prognostic Gene Signature\u003c/h2\u003e \u003cp\u003eBoth LIPT1 and DLAT were taken forward to LASSO regression. The 10-fold cross-validation procedure identified an optimal lambda of 0.00344, at which point both genes retained non-zero coefficients. The resulting risk score formula was:\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk Score = (\u0026minus;\u0026thinsp;0.607 \u0026times; LIPT1 expression) + (0.283 \u0026times; DLAT expression)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe negative coefficient for LIPT1 is concordant with its protective effect in the univariate analysis, while the positive coefficient for DLAT is concordant with its role as a risk-associated factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Survival Stratification in the TCGA Cohort\u003c/h2\u003e \u003cp\u003eWhen the 402 TCGA patients were divided according to the median risk score, the high-risk group (n\u0026thinsp;=\u0026thinsp;201) showed significantly shorter OS than the low-risk group (n\u0026thinsp;=\u0026thinsp;201; log-rank P\u0026thinsp;=\u0026thinsp;0.00013; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 External Validation in GSE32894\u003c/h2\u003e \u003cp\u003eApplication of the same risk score formula to the independent GSE32894 cohort likewise produced a significant separation between high-risk and low-risk groups (log-rank P\u0026thinsp;=\u0026thinsp;0.00037; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Functional Enrichment of Co-expressed Genes\u003c/h2\u003e \u003cp\u003eA total of 328 genes were found to be co-expressed with LIPT1 or DLAT. GO Biological Process enrichment revealed that these genes were predominantly involved in nucleocytoplasmic transport, RNA splicing, and protein localization to the nucleus. KEGG pathway analysis further identified 228 significantly enriched pathways, among which the most relevant to cuproptosis biology were the citrate cycle (TCA cycle), lipoic acid metabolism, pyruvate metabolism, and oxidative phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Time-Dependent ROC Analysis\u003c/h2\u003e \u003cp\u003eThe predictive accuracy of the risk score was assessed using time-dependent ROC curves. The AUC values for 1-, 3-, and 5-year OS were 0.640, 0.611, and 0.620, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Independent Prognostic Value\u003c/h2\u003e \u003cp\u003eIn a multivariate Cox model adjusting for age at diagnosis, the risk score remained a strong independent predictor of OS (HR\u0026thinsp;=\u0026thinsp;2.175, 95% CI: 1.638\u0026ndash;2.889, P\u0026thinsp;=\u0026thinsp;8.31 \u0026times; 10⁻⁸; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Age was also independently associated with survival (HR\u0026thinsp;=\u0026thinsp;1.027, 95% CI: 1.012\u0026ndash;1.042, P\u0026thinsp;=\u0026thinsp;3.50 \u0026times; 10⁻⁴).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox analysis incorporating the risk score and age.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisk Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.175\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.638\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.889\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.31\u0026times;10⁻⁸\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.50\u0026times;10⁻⁴\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we constructed and independently validated a parsimonious two-gene prognostic signature derived from the core cuproptosis machinery. The signature successfully discriminated between patients with favorable and adverse survival outcomes in both the TCGA training cohort and an external GEO dataset. The enrichment of co-expressed genes in the TCA cycle, lipoic acid metabolism, and oxidative phosphorylation pathways lends biological plausibility to the model and is consistent with the known biochemical mechanism of cuproptosis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The time-dependent ROC analysis indicated reasonable predictive accuracy, and the risk score remained prognostic after adjustment for age, supporting its potential clinical utility.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. Both the training and external validation datasets are retrospective in nature, and despite the consistency of the results across two independent cohorts, the GSE32894 dataset has a relatively low event rate (11.2%), which may affect the precision of the survival estimates. Additionally, the study relied entirely on publicly available transcriptomic data, and no wet-laboratory experiments were performed to confirm the expression levels or functional roles of LIPT1 and DLAT in bladder cancer cell lines. Future work incorporating prospective cohorts, additional molecular characterizations such as mutation and immune infiltration data, and functional validation experiments would further strengthen the evidence base for this signature.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWe developed a novel cuproptosis-related two-gene signature consisting of LIPT1 and DLAT that effectively stratifies BLCA patients into risk groups with significantly different overall survival. The signature was externally validated and provides prognostic information independent of age. This concise model may offer a practical framework for individualized survival assessment in bladder cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003ePatient consent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe author declares that he has no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eUse of artificial intelligence tools\u003c/h2\u003e \u003cp\u003eDuring the preparation of this manuscript, the author used an AI-assisted language model to improve the readability and language. The author subsequently revised and edited the content and takes full responsibility for the final version.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received.\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e \u003cp\u003eYHZ conceived and designed the study, performed all data analysis, interpreted the results, and wrote the manuscript. The author confirms the authenticity of all the raw data.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe author thanks the TCGA Research Network, the UCSC Xena team, and the GEO database for making the data publicly available.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are openly available in TCGA (via UCSC Xena, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GEO (accession: GSE32894). The complete reproducible R code is provided as Supplementary File S1.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209\u0026ndash;249\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabjuk M, Burger M, Comp\u0026eacute;rat E, Gontero P, Mostafid AH, Palou J, van Rhijn BWG, Roupr\u0026ecirc;t M, Shariat SF, Sylvester R (2019) European Association of Urology guidelines on non-muscle-invasive bladder cancer (TaT1 and carcinoma in situ) \u0026ndash;\u0026thinsp;2019 update. Eur Urol 76:639\u0026ndash;657\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlfred Witjes J, Lebret T, Comp\u0026eacute;rat EM, Cowan NC, De Santis M, Bruins HM, Hern\u0026aacute;ndez V, Espin\u0026oacute;s EL, Dunn J, Rouanne M et al (2017) Updated 2016 EAU guidelines on muscle-invasive and metastatic bladder cancer. Eur Urol 71:462\u0026ndash;475\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsvetkov P, Coy S, Petrova B, Dreishpoon M, Verma A, Abdusamad M, Rossen J, Joesch-Cohen L, Humeidi R, Spangler RD et al (2022) Copper induces cell death by targeting lipoylated TCA cycle proteins. Science 375:1254\u0026ndash;1261\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi SR, Bu LL, Cai L (2022) Cuproptosis: lipoylated TCA cycle proteins-mediated novel cell death pathway. Signal Transduct Target Ther 7:158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao S, Zhang J, Li T, Wang X, Liu B, Chen Z, Zheng J, Sun Y, Zhu X (2024) Development of prognostic model incorporating a ferroptosis/cuproptosis-related signature and mutational landscape analysis in muscle-invasive bladder cancer. BMC Cancer 24:938\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng H, Gu J, Huang Y, Kołat D, Shi G, Yan L, Ye D (2024) Cuproptosis-related signature predicts prognosis and indicates tumor immune infiltration in bladder cancer. Transl Androl Urol 13:2280\u0026ndash;2293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang CY, A RN, Liu JM (2024) Prognostic signature and immune/drug therapy analysis of cuproptosis-related immune checkpoint genes in bladder cancer. J Hainan Med Univ 30:262\u0026ndash;272\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Guangling College, Yangzhou University","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":"bladder cancer, cuproptosis, LIPT1, DLAT, prognostic signature, TCGA, external validation","lastPublishedDoi":"10.21203/rs.3.rs-9562963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9562963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBladder cancer (BLCA) is a major urological malignancy with limited prognostic biomarkers. Cuproptosis, a copper-dependent programmed cell death pathway, has attracted interest in cancer biology, yet its prognostic relevance in BLCA remains underexplored.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003e Transcriptomic and clinical data for 402 BLCA patients were obtained from TCGA via UCSC Xena. Ten cuproptosis-related genes were screened by univariate Cox regression, followed by LASSO-penalized Cox regression to construct a risk score model. The signature was externally validated in GSE32894 (n\u0026thinsp;=\u0026thinsp;224). Co-expressed genes were subjected to GO and KEGG enrichment analyses. Model performance was evaluated by time-dependent ROC curves, and multivariate Cox regression assessed independence from clinical covariates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLIPT1 (HR\u0026thinsp;=\u0026thinsp;0.527, P\u0026thinsp;=\u0026thinsp;5.11 \u0026times; 10⁻⁶) and DLAT (HR\u0026thinsp;=\u0026thinsp;1.467, P\u0026thinsp;=\u0026thinsp;8.99 \u0026times; 10⁻\u0026sup3;) were significantly associated with overall survival (OS) and retained in the LASSO model (λ\u0026thinsp;=\u0026thinsp;0.00344). The risk score was: Risk Score = (\u0026minus;\u0026thinsp;0.607 \u0026times; LIPT1) + (0.283 \u0026times; DLAT). High-risk patients showed significantly shorter OS in the TCGA cohort (P\u0026thinsp;=\u0026thinsp;0.00013), confirmed by external validation (P\u0026thinsp;=\u0026thinsp;0.00037). AUC values for 1-, 3-, and 5-year OS were 0.640, 0.611, and 0.620. Enrichment analyses linked co-expressed genes to the TCA cycle, lipoic acid metabolism, and oxidative phosphorylation. The risk score remained an independent predictor after adjusting for age (HR\u0026thinsp;=\u0026thinsp;2.175, 95% CI: 1.638\u0026ndash;2.889, P\u0026thinsp;=\u0026thinsp;8.31 \u0026times; 10⁻⁸).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA concise two-gene cuproptosis-related signature (LIPT1 and DLAT) was developed and independently validated, demonstrating robust prognostic performance in BLCA.\u003c/p\u003e","manuscriptTitle":"A Novel Cuproptosis-Related Two-Gene Signature for Prognostic Prediction in Bladder Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 10:02:20","doi":"10.21203/rs.3.rs-9562963/v1","editorialEvents":[{"type":"communityComments","content":1}],"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":"ed27b09a-91db-49a5-beb3-049aae961cec","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67228667,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2026-05-04T10:02:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 10:02:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9562963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9562963","identity":"rs-9562963","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}