Molecular Subtypes of Esophageal Squamous Cell Carcinoma Driven by N- Glycosylation and Construction of a Machine Learning-Based Prognostic Model

Molecular Subtypes of Esophageal Squamous Cell Carcinoma Driven by N- Glycosylation and Construction of a Machine Learning-Based Prognostic Model | 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 Article Molecular Subtypes of Esophageal Squamous Cell Carcinoma Driven by N- Glycosylation and Construction of a Machine Learning-Based Prognostic Model Chuanan Yue, Haifeng Huang, Wenqian Li, Jingya Guo, Shiying Zuo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7391512/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 Despite the same origin from squamous epithelial cells, patients with esophageal squamous cell carcinoma (ESCC) exhibit marked heterogeneity, posing significant challenges to clinical treatment. The development of more precise molecular subtyping strategies has become an urgent need in the ESCC field in recent years. Protein post-translational modifications (PTMs) are pivotal regulators of ESCC development and acquired therapeutic resistance. However, the functional significance and clinical implications of N -glycosylation, a major type of PTM, in this context remain poorly understood and warrant comprehensive investigation. This multi-omics study integrated single-cell, bulk RNA sequencing, and somatic mutation data to investigate N -glycosylation in esophageal squamous cell carcinoma (ESCC). scRNA-seq analysis revealed predominant enrichment of N -glycosylation pathway activity within plasma cells of the ESCC microenvironment, while malignant/epithelial cells exhibited N -glycosylation modification of key receptors ( ITGA6 , ITGB1 , SDC4 , SDC1 , and EGFR ) implicated in tumor progression. Bulk RNA-seq enabled stratification of ESCC into two molecular subtypes: N -glycosylation-active (NGAs) and -inactive (NGIs). Comparative analysis demonstrated NGIs harbored significantly higher KMT2D mutation frequency ( P = 0.049), whereas NGAs showed increased lymph node metastasis and distinct immune infiltration patterns. Utilizing LASSO regression, we established a 5-gene prognostic signature ( BTBD19 , TMEM273 , ALG14 , GGCX , and H2AX ) validated by external cohorts. Application of the risk score cutoff (-17.78) successfully stratified patients, with higher risk score groups exhibiting significantly poorer survival rate(TCGA-ESCC: P < 0.0001; GSE53624: P = 0.039). Drug sensitivity prediction based on the risk score identified three therapeutic agents (vinorelbine, thapsigargin, and docetaxel) demonstrating significant efficacy in ESCC patients; however, their sensitivity exhibited an inverse correlation with increasing risk scores. This finding provides insights for guiding precision oncology therapeutics in clinical practice. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology Full Text Additional Declarations No competing interests reported. Supplementary Files supplementaryFig.docx 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-7391512","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502865173,"identity":"5864efa8-984c-4639-a642-39e738c037d2","order_by":0,"name":"Chuanan Yue","email":"","orcid":"","institution":"Medical College, Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Chuanan","middleName":"","lastName":"Yue","suffix":""},{"id":502865174,"identity":"fec73b17-f0d6-4838-b902-608956246198","order_by":1,"name":"Haifeng Huang","email":"","orcid":"","institution":"The First People's Hospital of 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Glycosylation and Construction of a Machine Learning-Based Prognostic Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7391512/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7391512/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite the same origin from squamous epithelial cells, patients with esophageal squamous cell carcinoma (ESCC) exhibit marked heterogeneity, posing significant challenges to clinical treatment. The development of more precise molecular subtyping strategies has become an urgent need in the ESCC field in recent years. Protein post-translational modifications (PTMs) are pivotal regulators of ESCC development and acquired therapeutic resistance. However, the functional significance and clinical implications of \u003cem\u003eN\u003c/em\u003e-glycosylation, a major type of PTM, in this context remain poorly understood and warrant comprehensive investigation. This multi-omics study integrated single-cell, bulk RNA sequencing, and somatic mutation data to investigate \u003cem\u003eN\u003c/em\u003e-glycosylation in esophageal squamous cell carcinoma (ESCC). scRNA-seq analysis revealed predominant enrichment of \u003cem\u003eN\u003c/em\u003e-glycosylation pathway activity within plasma cells of the ESCC microenvironment, while malignant/epithelial cells exhibited \u003cem\u003eN\u003c/em\u003e-glycosylation modification of key receptors (\u003cem\u003eITGA6\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, \u003cem\u003eSDC4\u003c/em\u003e, \u003cem\u003eSDC1\u003c/em\u003e, and \u003cem\u003eEGFR\u003c/em\u003e) implicated in tumor progression. Bulk RNA-seq enabled stratification of ESCC into two molecular subtypes: \u003cem\u003eN\u003c/em\u003e-glycosylation-active (NGAs) and -inactive (NGIs). Comparative analysis demonstrated NGIs harbored significantly higher \u003cem\u003eKMT2D\u003c/em\u003e mutation frequency (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), whereas NGAs showed increased lymph node metastasis and distinct immune infiltration patterns. Utilizing LASSO regression, we established a 5-gene prognostic signature (\u003cem\u003eBTBD19\u003c/em\u003e, \u003cem\u003eTMEM273\u003c/em\u003e, \u003cem\u003eALG14\u003c/em\u003e, \u003cem\u003eGGCX\u003c/em\u003e, and \u003cem\u003eH2AX\u003c/em\u003e) validated by external cohorts. Application of the risk score cutoff (-17.78) successfully stratified patients, with higher risk score groups exhibiting significantly poorer survival rate(TCGA-ESCC: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; GSE53624: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039). Drug sensitivity prediction based on the risk score identified three therapeutic agents (vinorelbine, thapsigargin, and docetaxel) demonstrating significant efficacy in ESCC patients; however, their sensitivity exhibited an inverse correlation with increasing risk scores. This finding provides insights for guiding precision oncology therapeutics in clinical practice.\u003c/p\u003e","manuscriptTitle":"Molecular Subtypes of Esophageal Squamous Cell Carcinoma Driven by N- Glycosylation and Construction of a Machine Learning-Based Prognostic Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 16:39:35","doi":"10.21203/rs.3.rs-7391512/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":"d7a3d260-7087-481b-8b35-9d5726722fe1","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53416183,"name":"Health sciences/Biomarkers"},{"id":53416184,"name":"Biological sciences/Cancer"},{"id":53416185,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":53416186,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-09-16T19:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 16:39:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7391512","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7391512","identity":"rs-7391512","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}