Identification of a ubiquitin related gene signature for predicting prognosis and immune infiltration in breast cancer based on single cell and bulk RNA sequencing analysis

Identification of a ubiquitin related gene signature for predicting prognosis and immune infiltration in breast cancer based on single cell and bulk RNA sequencing analysis | 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 Identification of a ubiquitin related gene signature for predicting prognosis and immune infiltration in breast cancer based on single cell and bulk RNA sequencing analysis Zhengbo Fang, Jiaqi Li, Man Li, Fei Ma, Yansong Liu, Weilun Cheng, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8886340/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 Ubiquitination drives breast cancer (BC) progression, yet the clinical value of ubiquitin-related genes (UbRGs) remains underexplored. This study aimed to develop a UbRG-based prognostic model and investigate its associations with the tumor immune microenvironment (TIME) and therapeutic responses. Methods Transcriptomic and clinical data from the TCGA-BRCA cohort were analyzed to identify differentially expressed UbRGs. A ubiquitin-related risk score (URS) was constructed using LASSO and Cox regression. Single-cell RNA sequencing (scRNA-seq) data (GSE161529) were integrated via the Scissor algorithm to identify risk-associated cell populations. Furthermore, weighted gene co-expression network analysis (WGCNA) combined with machine learning approaches was applied to identify a core hub gene, which was subsequently validated using immunohistochemistry (IHC) in a clinical cohort. Results A six-gene URS independently stratified patients into high- and low-risk groups with significantly different overall survival. High-risk tumors exhibited an immunosuppressive TIME, featuring decreased lymphocyte infiltration and M2 macrophage enrichment, which was corroborated at the single-cell level. Conversely, low-risk patients demonstrated predicted sensitivity to targeted therapies, including CDK4/6 and PARP inhibitors. Through multi-omics and machine learning integration, SORBS1 was identified as the critical hub gene. IHC validation confirmed that SORBS1 protein expression was significantly downregulated in BC tissues and correlated with improved survival. Conclusions The scRNA-seq and machine learning-integrated URS provides robust prognostic stratification and therapeutic guidance in BC. Our findings link ubiquitination to immune exclusion and establish SORBS1 as a clinically validated, protective biomarker. breast cancer ubiquitination prognostic signature immune microenvironment biomarker SORBS1 Full Text Additional Declarations No competing interests reported. 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. 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-8886340","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612637831,"identity":"c9d1a12f-e5fb-4641-a0b2-a7e430d24a12","order_by":0,"name":"Zhengbo Fang","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhengbo","middleName":"","lastName":"Fang","suffix":""},{"id":612637832,"identity":"ce0105dd-dcb1-47eb-bc28-89662e032798","order_by":1,"name":"Jiaqi Li","email":"","orcid":"","institution":"Second Affiliated Hospital of Harbin 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Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yiyun","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-15 13:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8886340/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8886340/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108603969,"identity":"ca0875d2-bc27-4a27-812c-00c865faf1ee","added_by":"auto","created_at":"2026-05-06 11:57:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7680390,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8886340/v1_covered_deb7ac4f-a121-4114-8000-c549122471fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIdentification of a ubiquitin related gene signature for predicting prognosis and immune infiltration in breast cancer based on single cell and bulk RNA sequencing analysis\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"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":"breast cancer, ubiquitination, prognostic signature, immune microenvironment, biomarker, SORBS1","lastPublishedDoi":"10.21203/rs.3.rs-8886340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8886340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUbiquitination drives breast cancer (BC) progression, yet the clinical value of ubiquitin-related genes (UbRGs) remains underexplored. This study aimed to develop a UbRG-based prognostic model and investigate its associations with the tumor immune microenvironment (TIME) and therapeutic responses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTranscriptomic and clinical data from the TCGA-BRCA cohort were analyzed to identify differentially expressed UbRGs. A ubiquitin-related risk score (URS) was constructed using LASSO and Cox regression. Single-cell RNA sequencing (scRNA-seq) data (GSE161529) were integrated via the Scissor algorithm to identify risk-associated cell populations. Furthermore, weighted gene co-expression network analysis (WGCNA) combined with machine learning approaches was applied to identify a core hub gene, which was subsequently validated using immunohistochemistry (IHC) in a clinical cohort.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA six-gene URS independently stratified patients into high- and low-risk groups with significantly different overall survival. High-risk tumors exhibited an immunosuppressive TIME, featuring decreased lymphocyte infiltration and M2 macrophage enrichment, which was corroborated at the single-cell level. Conversely, low-risk patients demonstrated predicted sensitivity to targeted therapies, including CDK4/6 and PARP inhibitors. Through multi-omics and machine learning integration, SORBS1 was identified as the critical hub gene. IHC validation confirmed that SORBS1 protein expression was significantly downregulated in BC tissues and correlated with improved survival.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe scRNA-seq and machine learning-integrated URS provides robust prognostic stratification and therapeutic guidance in BC. Our findings link ubiquitination to immune exclusion and establish SORBS1 as a clinically validated, protective biomarker.\u003c/p\u003e","manuscriptTitle":"Identification of a ubiquitin related gene signature for predicting prognosis and immune infiltration in breast cancer based on single cell and bulk RNA sequencing analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 17:44:18","doi":"10.21203/rs.3.rs-8886340/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":"41989183-2155-4ab1-992a-9fba236a7234","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-06T11:41:23+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T11:55:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 17:44:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8886340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8886340","identity":"rs-8886340","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}