Identification of Oral Microbiome Biomarkers Associated with Lung Cancer Diagnosis and Radiotherapy Response Prediction

Identification of Oral Microbiome Biomarkers Associated with Lung Cancer Diagnosis and Radiotherapy Response Prediction | 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 Oral Microbiome Biomarkers Associated with Lung Cancer Diagnosis and Radiotherapy Response Prediction Xiaoqian Shi, Wenyang Liu, Liying Ma, Mingyang Liu, Tongzhen Xu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7343436/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 The oral cavity serves as the anatomical entrance of the respiratory tract, sharing microbiological and pathophysiological connections with the lower airways. Despite radiotherapy being a cornerstone treatment for lung cancer, the field lacks robust oral microbiome biomarkers capable of predicting therapeutic outcomes. Methods In this study, we analyzed the oral microbiome of 136 lung cancer patients and 199 healthy controls from a discovery cohort and two validation cohorts using 16S rRNA sequencing. Differential microbial taxa were subsequently identified through an integrative analysis combining Wilcoxon rank-sum tests, LEfSe, and ANCOM-BC2. Random forest analysis was employed to construct models for lung cancer diagnosis and treatment response prediction. The prognostic value of discriminatory features was then assessed using Kaplan-Meier analysis. Results Healthy controls exhibited significantly higher Streptococcus abundance than patients. Microbial community structure shifted substantially during treatment. Responders showed enrichment of Rothia aeria and Prevotella salivae , associated with prolonged OS and PFS, whereas non-responders exhibited elevated Porphyromonas endodontalis correlating with shorter OS and PFS. ANCOM-BC2 analysis indicated that Akkermansia and Alistipes were virtually absent in non-responders, whereas Desulfovibrio and Moraxella were near-complete absence in responders. In the independent validation cohorts, the Streptococcus -based diagnostic signature exhibited outstanding discriminatory capacity, attaining AUCs of 0.85 and 0.99, respectively. Meanwhile, the response prediction model based on Prevotella salivae and Neisseria oralis yielded an AUC of 0.74. Regression analysis demonstrated that oral microbiota richness and diversity were inversely associated with ECOG score and ProGRP level in small cell lung cancer patients. Conclusions Lung cancer patients harbor distinct oral microbiota signatures whose dynamics correlate significantly with therapeutic response and survival outcomes. The developed diagnostic and predictive models demonstrate robust performance, supporting oral microbiota as non-invasive biomarkers for lung cancer management. Oral microbiota Lung cancer Diagnostic model Treatment response prediction Radiotherapy Full Text Supplementary Files Additionalfile1.pdf Additionalfile2figS1.pdf Additionalfile3figS2.pdf 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. 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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-7343436","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502043321,"identity":"5badf6e2-981d-4e64-8046-5e5b9e2045fc","order_by":0,"name":"Xiaoqian Shi","email":"","orcid":"","institution":"Cancer Hospital Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqian","middleName":"","lastName":"Shi","suffix":""},{"id":502043322,"identity":"b2dc3c91-90ed-465f-a427-5868453f6bb4","order_by":1,"name":"Wenyang Liu","email":"","orcid":"","institution":"Cancer Hospital Chinese Academy of Medical 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Prediction","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":"Oral microbiota, Lung cancer, Diagnostic model, Treatment response prediction, Radiotherapy","lastPublishedDoi":"10.21203/rs.3.rs-7343436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7343436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003e The oral cavity serves as the anatomical entrance of the respiratory tract, sharing microbiological and pathophysiological connections with the lower airways. Despite radiotherapy being a cornerstone treatment for lung cancer, the field lacks robust oral microbiome biomarkers capable of predicting therapeutic outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this study, we analyzed the oral microbiome of 136 lung cancer patients and 199 healthy controls from a discovery cohort and two validation cohorts using 16S rRNA sequencing. Differential microbial taxa were subsequently identified through an integrative analysis combining Wilcoxon rank-sum tests, LEfSe, and ANCOM-BC2. Random forest analysis was employed to construct models for lung cancer diagnosis and treatment response prediction. The prognostic value of discriminatory features was then assessed using Kaplan-Meier analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHealthy controls exhibited significantly higher \u003cem\u003eStreptococcus\u003c/em\u003e abundance than patients. Microbial community structure shifted substantially during treatment. Responders showed enrichment of \u003cem\u003eRothia aeria\u003c/em\u003e and \u003cem\u003ePrevotella salivae\u003c/em\u003e, associated with prolonged OS and PFS, whereas non-responders exhibited elevated \u003cem\u003ePorphyromonas endodontalis\u003c/em\u003e correlating with shorter OS and PFS. ANCOM-BC2 analysis indicated that \u003cem\u003eAkkermansia\u003c/em\u003e and \u003cem\u003eAlistipes\u003c/em\u003e were virtually absent in non-responders, whereas \u003cem\u003eDesulfovibrio\u003c/em\u003e and \u003cem\u003eMoraxella\u003c/em\u003e were near-complete absence in responders. In the independent validation cohorts, the \u003cem\u003eStreptococcus\u003c/em\u003e-based diagnostic signature exhibited outstanding discriminatory capacity, attaining AUCs of 0.85 and 0.99, respectively. Meanwhile, the response prediction model based on \u003cem\u003ePrevotella salivae\u003c/em\u003e and \u003cem\u003eNeisseria oralis\u003c/em\u003e yielded an AUC of 0.74. Regression analysis demonstrated that oral microbiota richness and diversity were inversely associated with ECOG score and ProGRP level in small cell lung cancer patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eLung cancer patients harbor distinct oral microbiota signatures whose dynamics correlate significantly with therapeutic response and survival outcomes. The developed diagnostic and predictive models demonstrate robust performance, supporting oral microbiota as non-invasive biomarkers for lung cancer management.\u003c/p\u003e","manuscriptTitle":"Identification of Oral Microbiome Biomarkers Associated with Lung Cancer Diagnosis and Radiotherapy Response Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 13:22:51","doi":"10.21203/rs.3.rs-7343436/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":"feecc418-66be-4506-bfac-1fb557f49bda","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-24T03:19:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-26 13:22:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7343436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7343436","identity":"rs-7343436","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}