Research on an Interpretable Grey Wolf Optimization-Based Ensemble Machine Learning Model for Identifying Heterogeneity of Bladder Cancer Based on Immunological Microenvironment

Research on an Interpretable Grey Wolf Optimization-Based Ensemble Machine Learning Model for Identifying Heterogeneity of Bladder Cancer Based on Immunological Microenvironment | 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 Research on an Interpretable Grey Wolf Optimization-Based Ensemble Machine Learning Model for Identifying Heterogeneity of Bladder Cancer Based on Immunological Microenvironment Honglin Guo, Qiuyue Song, Chengcheng Gao, Ke Chen, Yunhao Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8260991/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Bladder urothelial carcinoma (BLCA) exhibits marked heterogeneity, leading to variable treatment responses and prognoses across subtypes. Current molecular classification systems lack emphasis on immune-related genes, limiting their utility for guiding immunotherapy. Using TCGA transcriptome data, we identified 490 immune-related differentially expressed genes. The top 20% most representative genes were selected for subtype delineation via Non-negative Matrix Factorization (NMF), yielding 2 optimal subtypes. We then constructed an Exploration-Enhanced Grey Wolf Optimization-based Soft Voting (EGWO-SV) model, integrating Logistic Regression, XGBoost, and Random Forest as base learners. This model outperformed 9 classical machine learning methods (AUC 97.11%, Accuracy 90.00%, F1 88.24%). SHAP visualization highlighted CLEC2B and SULT1A1 as key genes for BLCA prognosis. Subtype analysis revealed significant survival disparities, with the high-risk group linked to advanced stages. EGWO-SV enables efficient BLCA subtyping, supporting precise diagnosis, personalized immunotherapy, and improved understanding of tumor heterogeneity. Immune-related Bladder cancer Ensemble learning Subtype identification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Jan, 2026 Reviewers agreed at journal 30 Dec, 2025 Reviewers agreed at journal 30 Dec, 2025 Reviewers agreed at journal 25 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 08 Dec, 2025 First submitted to journal 02 Dec, 2025 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8260991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559036024,"identity":"7d32eeba-a409-432c-b5ed-bd538fa8a9f4","order_by":0,"name":"Honglin Guo","email":"","orcid":"","institution":"Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Honglin","middleName":"","lastName":"Guo","suffix":""},{"id":559036028,"identity":"8d7c7284-bb5b-49ac-8329-0e42515ce730","order_by":1,"name":"Qiuyue Song","email":"","orcid":"","institution":"Army Medical 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