B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights from Bioinformatics, Machine Learning, and Experimental Validation

B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights from Bioinformatics, Machine Learning, and Experimental Validation | 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 B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights from Bioinformatics, Machine Learning, and Experimental Validation Apeng Yang, Mengying Ke, Lin Feng, Ye Yang, Junmin Chen, Zhiyong Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5882070/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: Glycosylation abnormalities are critical in the progression of various cancers. However, their role in the onset and prognosis of multiple myeloma (MM) remains underexplored. This study aims to identify glycosyltransferase (GT)-related biomarkers and investigate their underlying mechanisms in MM. Methods: GT-related genes were extracted from the MMRF-CoMMpass and GSE57317 datasets. Potential biomarkers were identified using Cox regression and Lasso analyses. A Glycosyltransferase-Related Prognostic Model (GTPM) was developed by evaluating 113 machine learning algorithm combinations. The expression of B4GALT3, a key gene identified through this model, was analyzed in MM bone marrow samples using immunohistochemistry, quantitative PCR, and western blotting. Functional roles of B4GALT3 in MM cell behavior were assessed through knockdown experiments, and its mechanism of action was investigated. Results: The GTPM stratified MM patients into high- and low-risk groups, with significantly better survival in the low-risk group (HR = 55.94, 95% CI = 40.48–77.31, p \(<\) 0.001). The model achieved AUC values of 0.98 and 0.99 for 1-year and 3-year overall survival, outperforming existing gene signatures (including EMC92, UAMS70, and UAMS17). B4GALT3 expression was significantly elevated in advanced MM stages (p $ < $ 0.001) and correlated with poorer survival. Knockdown of B4GALT3 reduced MM cell proliferation, invasion , and increased apoptosis. Mechanistic analyses revealed that B4GALT3 modulates MM cell behavior via the Wnt/ \(\beta\) -catenin/GRP78 pathway, primarily by regulating endoplasmic reticulum (ER) stress. Conclusions: This study developed a novel GTPM for predicting survival in MM and identified B4GALT3 as a key gene influencing disease progression. Experimental evidence highlights B4GALT3's role in modulating ER stress and Wnt/ \(\beta\) -catenin pathways, positioning it as a potential prognostic biomarker and therapeutic target in MM. multiple myeloma B4GALT3 glycosyltransferase machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1.pdf Supplementaryfile2.csv 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-5882070","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406216621,"identity":"f1157ef6-a841-4044-b444-6e863f8f4de1","order_by":0,"name":"Apeng Yang","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Apeng","middleName":"","lastName":"Yang","suffix":""},{"id":406216624,"identity":"f749be7e-adae-4f90-96bb-5cfcc44172cc","order_by":1,"name":"Mengying Ke","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mengying","middleName":"","lastName":"Ke","suffix":""},{"id":406216625,"identity":"dd90dedc-fb77-4936-a2b9-037de6c6d756","order_by":2,"name":"Lin Feng","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Feng","suffix":""},{"id":406216627,"identity":"b806e618-e7c0-4ea7-a9a2-897cf8364cc0","order_by":3,"name":"Ye Yang","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Yang","suffix":""},{"id":406216628,"identity":"6c991cf8-1a86-4f40-9c34-4b2a743ad90c","order_by":4,"name":"Junmin Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junmin","middleName":"","lastName":"Chen","suffix":""},{"id":406216629,"identity":"090c9de7-84b2-4752-a0fe-254dcf18c652","order_by":5,"name":"Zhiyong Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYLACxgYJMH1AosKGh5+/gXgtjA8szqTJSM44QJQWMMVsUNl22MagIQG/avn23sMvf+6wSOyXbr8mcbPtPI8BwwHGDx9z8FjQcy7NQvKMROLMOWfKJGecu81jztzALDlzG24tzBI5ZgaGbRKJG27kpElLlN3msWw4wMbMi0cLm/wbM4NEmJY/bOd4DA4k4NfCI8Fj/OAgWEv6YQOJtgOEtUjw5JgxNrZJGM+ckcP4QOJMMo/kjIPNeP0i337G+OPPtjrZfon0B8CotLPn528++OEjHi0g74Di0bGBARi8EACNJjyA+QOQsGdgYH9ASOUoGAWjYBSMUAAADw9Wd6JdwiQAAAAASUVORK5CYII=","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhiyong","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2025-01-22 15:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5882070/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5882070/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76916679,"identity":"2454d18d-a4d1-4f9e-8477-1f593ddee21c","added_by":"auto","created_at":"2025-02-22 09:31:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5364646,"visible":true,"origin":"","legend":"","description":"","filename":"B4GALT3asaKeyGlycosyltransferaseGeneinMultipleMyelomaProgressionInsightsfromBioinformaticsMachineLearningandExperimentalValidation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5882070/v1_covered_42051f55-7f45-492c-af6f-f75f36ac97bc.pdf"},{"id":74888399,"identity":"120b8620-f2cd-4d86-a34c-6d2e89b9df1c","added_by":"auto","created_at":"2025-01-28 04:25:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":39040,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5882070/v1/4d29e8b978740c1b1e91bc87.pdf"},{"id":74887826,"identity":"8e8bf977-aab4-4359-a305-1418a8cc3b3b","added_by":"auto","created_at":"2025-01-28 04:17:15","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":285,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.csv","url":"https://assets-eu.researchsquare.com/files/rs-5882070/v1/cb1b4a8ae75cb490274f0301.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights from Bioinformatics, Machine Learning, and Experimental Validation","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":"multiple myeloma, B4GALT3, glycosyltransferase, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-5882070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5882070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eGlycosylation abnormalities are critical in the progression of various cancers. However, their role in the onset and prognosis of multiple myeloma (MM) remains underexplored. This study aims to identify glycosyltransferase (GT)-related biomarkers and investigate their underlying mechanisms in MM.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eGT-related genes were extracted from the MMRF-CoMMpass and GSE57317 datasets. Potential biomarkers were identified using Cox regression and Lasso analyses. A Glycosyltransferase-Related Prognostic Model (GTPM) was developed by evaluating 113 machine learning algorithm combinations. The expression of B4GALT3, a key gene identified through this model, was analyzed in MM bone marrow samples using immunohistochemistry, quantitative PCR, and western blotting. Functional roles of B4GALT3 in MM cell behavior were assessed through knockdown experiments, and its mechanism of action was investigated.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eThe GTPM stratified MM patients into high- and low-risk groups, with significantly better survival in the low-risk group (HR = 55.94, 95% CI = 40.48\u0026ndash;77.31, p \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(\u0026amp;#x003C;\\) \u003c/span\u003e \u003c/span\u003e 0.001). The model achieved AUC values of 0.98 and 0.99 for 1-year and 3-year overall survival, outperforming existing gene signatures (including EMC92, UAMS70, and UAMS17). B4GALT3 expression was significantly elevated in advanced MM stages (p \u003cspan\u003e$\u003c/span\u003e\u0026lt;\u003cspan\u003e$\u003c/span\u003e 0.001) and correlated with poorer survival. Knockdown of B4GALT3 reduced MM cell proliferation, invasion , and increased apoptosis. Mechanistic analyses revealed that B4GALT3 modulates MM cell behavior via the Wnt/\u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(\\beta\\) \u003c/span\u003e \u003c/span\u003e-catenin/GRP78 pathway, primarily by regulating endoplasmic reticulum (ER) stress.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eThis study developed a novel GTPM for predicting survival in MM and identified B4GALT3 as a key gene influencing disease progression. Experimental evidence highlights B4GALT3's role in modulating ER stress and Wnt/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e-catenin pathways, positioning it as a potential prognostic biomarker and therapeutic target in MM.\u003c/p\u003e","manuscriptTitle":"B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights from Bioinformatics, Machine Learning, and Experimental Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 04:17:11","doi":"10.21203/rs.3.rs-5882070/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":"e8601ff0-431e-453c-a19a-8d70a8c7619b","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-22T09:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-28 04:17:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5882070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5882070","identity":"rs-5882070","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}