Identification of lactylation-related biomarkers in HNSC by integrating machine learning and spatial transcriptomics analysis

Identification of lactylation-related biomarkers in HNSC by integrating machine learning and spatial transcriptomics 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 lactylation-related biomarkers in HNSC by integrating machine learning and spatial transcriptomics analysis Pu-Yu Wang, Yi-Ping Zheng, Yu-Lin Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7239065/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective: Head and neck squamous cell carcinoma (HNSC) is a common malignant tumor with a 5-year survival rate less than 60% and lactylation modification plays a key role in its occurrence and progression. This study aims to identify key lactylation-related biomarkers in HNSC and their prognostic impact by combining machine learning methods and spatial transcriptomics analysis. Methods: This study obtained high-throughput gene expression data of HNSC from the TCGA database. The “Limma” R package was used to screen differentially expressed genes (DEGs), and their biological significance was investigated through GO and KEGG functional enrichment analyses. Subsequently, weighted gene co-expression network analysis (WGCNA) identified disease trait-associated modules. We used 12 machine learning methods to screen for key genes and validated them in the GSE6631 dataset. Survival curves for TCGA-HNSC were plotted to evaluate the prognostic ability of key genes. Transcriptional regulation analysis via “RcisTarget” package identified transcription factors associated with motifs exceeding NES>3.5. Single-cell and spatial transcriptomic analysis was performed using Seurat packages, which involved dimensionality reduction ,clustering via UMAP .cell type annotation and plotted spatial expression distribution maps of key genes. Finally, for immune infiltration analysis, the CIBERSORT algorithm was used to calculate the proportions of 22 types of immune cells, and analyzed correlations between key genes and immune-related genes. Results: This study obtained high-throughput gene expression data of HNSC from the TCGA database. The “Limma” R package was used to screen differentially expressed genes (DEGs), and their biological significance was investigated through GO and KEGG functional enrichment analyses. Subsequently, weighted gene co-expression network analysis (WGCNA) identified disease trait-associated modules. We used 12 machine learning methods to screen for key genes and validated them in the GSE6631 dataset. Survival curves for TCGA-HNSC were plotted to evaluate the prognostic ability of key genes. Transcriptional regulation analysis via “RcisTarget” package identified transcription factors associated with motifs exceeding NES>3.5. Single-cell and spatial transcriptomic analysis was performed using Seurat packages, which involved dimensionality reduction ,clustering via UMAP .cell type annotation and plotted spatial expression distribution maps of key genes. Finally, for immune infiltration analysis, the CIBERSORT algorithm was used to calculate the proportions of 22 types of immune cells, and analyzed correlations between key genes and immune-related genes. Conclusion: MSN, KIF2C and RFC4 can be used as potential biomarkers for diagnosing and predicting the prognosis of HNSC, providing a new insight into diagnosis and targeted therapy lactylation HNSC machine learning biomarkers Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor invited by journal 31 Jul, 2025 Editor assigned by journal 30 Jul, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 29 Jul, 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. 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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-7239065","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498038245,"identity":"521b0cb9-7611-4f4d-90e4-bfb70be4265e","order_by":0,"name":"Pu-Yu Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Pu-Yu","middleName":"","lastName":"Wang","suffix":""},{"id":498038246,"identity":"6c50145e-dfda-4ec4-9dfd-0939e2251fd3","order_by":1,"name":"Yi-Ping Zheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yi-Ping","middleName":"","lastName":"Zheng","suffix":""},{"id":498038247,"identity":"eaedd8f8-6f51-4e4e-ad53-e0b2706eabb3","order_by":2,"name":"Yu-Lin Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACCRBRwMDAxg5iVEjIyROnxQCohRlIHzhjYWzYQKwWBpCWg20ViQwHCOiQn9388DGPwTZ5PmbmY48/zpNIYGxgfvjoBh4tjHOOGRvzGNw2bGNmSzc4uE0ij52Bzdg4B48WZokEM2mgFsY2Zh4zCaCWYsYGHjZpfFrYJNK/gbTYtzHzf5M4OEciseEAAS08EjlgWxKBtrBJHGwgQouERE6x4RyD28lAv5hJnDkmYWzYTMAv8jPSNz54U3Hbdn578zOJipo6OXl2YBji0wICTDwoXGYCykGA8QcRikbBKBgFo2AEAwA62UMnMvhlPgAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Yu-Lin","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-07-29 05:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7239065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7239065/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88951214,"identity":"6db9f6d9-b8a9-4189-acca-4201f0445fc8","added_by":"auto","created_at":"2025-08-13 05:53:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1740117,"visible":true,"origin":"","legend":"","description":"","filename":"revisedmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7239065/v1_covered_e12b55c4-133e-492f-9bbb-a2c64d05e558.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of lactylation-related biomarkers in HNSC by integrating machine learning and spatial transcriptomics analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"lactylation, HNSC, machine learning, biomarkers ","lastPublishedDoi":"10.21203/rs.3.rs-7239065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7239065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: Head and neck squamous cell carcinoma (HNSC) is a common malignant tumor with a 5-year survival rate less than 60% and lactylation modification plays a key role in its occurrence and progression. This study aims to identify key lactylation-related biomarkers in HNSC and their prognostic impact by combining machine learning methods and spatial transcriptomics analysis.\u003c/p\u003e\n\u003cp\u003eMethods: This study obtained high-throughput gene expression data of HNSC from the TCGA database. The “Limma” R package was used to screen differentially expressed genes (DEGs), and their biological significance was investigated through GO and KEGG functional enrichment analyses. Subsequently, weighted gene co-expression network analysis (WGCNA) identified disease trait-associated modules. We used 12 machine learning methods to screen for key genes and validated them in the GSE6631 dataset. Survival curves for TCGA-HNSC were plotted to evaluate the prognostic ability of key genes. Transcriptional regulation analysis via “RcisTarget” package identified transcription factors associated with motifs exceeding NES\u0026gt;3.5. Single-cell and spatial transcriptomic analysis was performed using Seurat packages, which involved dimensionality reduction ,clustering via UMAP .cell type annotation and plotted spatial expression distribution maps of key genes. Finally, for immune infiltration analysis, the CIBERSORT algorithm was used to calculate the proportions of 22 types of immune cells, and analyzed correlations between key genes and immune-related genes.\u003c/p\u003e\n\u003cp\u003eResults: This study obtained high-throughput gene expression data of HNSC from the TCGA database. The “Limma” R package was used to screen differentially expressed genes (DEGs), and their biological significance was investigated through GO and KEGG functional enrichment analyses. Subsequently, weighted gene co-expression network analysis (WGCNA) identified disease trait-associated modules. We used 12 machine learning methods to screen for key genes and validated them in the GSE6631 dataset. Survival curves for TCGA-HNSC were plotted to evaluate the prognostic ability of key genes. Transcriptional regulation analysis via “RcisTarget” package identified transcription factors associated with motifs exceeding NES\u0026gt;3.5. Single-cell and spatial transcriptomic analysis was performed using Seurat packages, which involved dimensionality reduction ,clustering via UMAP .cell type annotation and plotted spatial expression distribution maps of key genes. Finally, for immune infiltration analysis, the CIBERSORT algorithm was used to calculate the proportions of 22 types of immune cells, and analyzed correlations between key genes and immune-related genes.\u003c/p\u003e\n\u003cp\u003eConclusion: MSN, KIF2C and RFC4 can be used as potential biomarkers for diagnosing and predicting the prognosis of HNSC, providing a new insight into diagnosis and targeted therapy\u003c/p\u003e","manuscriptTitle":"Identification of lactylation-related biomarkers in HNSC by integrating machine learning and spatial transcriptomics analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 05:45:08","doi":"10.21203/rs.3.rs-7239065/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-15T11:43:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T02:09:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T02:47:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299588523403085133458495194100653005151","date":"2025-08-24T07:25:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290749889639839159294070202528710362988","date":"2025-08-24T01:15:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18769368787464854229812530940681013066","date":"2025-08-23T09:29:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T18:16:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-31T10:19:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T11:23:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T11:22:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-07-29T04:55:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07ee32ac-dabb-4593-b56f-bba33a06bc88","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-09T17:38:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 05:45:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7239065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7239065","identity":"rs-7239065","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}