Identification of Biomarkers for m6A-Mediated Programmed Cell Death in Osteoarthritis Transcripts with Experimental Validation.

Identification of Biomarkers for m6A-Mediated Programmed Cell Death in Osteoarthritis Transcripts with 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 Article Identification of Biomarkers for m6A-Mediated Programmed Cell Death in Osteoarthritis Transcripts with Experimental Validation. Gang Xue, Yihai Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9134544/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Introduction: Osteoarthritis (OA) is a common progressive joint disorder with limited treatment options. Emerging evidence suggests that N6-methyladenosine (m6A)-mediated programmed cell death (PCD) plays a crucial role in OA progression, yet the key regulatory genes remain unidentified. This study aims to identify m6A-modified PCD biomarkers to facilitate improved OA management. Materials and Methods We performed a comprehensive multi-omics analysis using transcriptomic data from two independent OA cohorts. Differentially expressed genes were integrated with 1,548 PCD-related genes and 23 m6A regulators. Feature selection was conducted using LASSO regression and Boruta algorithm. Biomarkers were validated through cross-dataset consistency verification, ROC curve analysis, and RT-qPCR. Mechanistic insights were explored through gene set enrichment analysis, immune infiltration deconvolution, and drug-gene interaction prediction. Results Integration analysis identified 26 candidate genes, from which machine learning selected six key genes. Five biomarkers (TNFAIP3, MYC, CDKN1A, ATF3, and CX3CR1) were robustly validated. These genes are involved in inflammatory and apoptotic pathways. Immune infiltration analysis revealed increased M2 macrophages, resting mast cells, plasma cells, and Tregs in OA tissues. Cadmium compounds were predicted as potential therapeutics. RT-qPCR confirmed significant downregulation of TNFAIP3, MYC, CDKN1A, and ATF3 in OA patients. Conclusions This study identified five m6A-PCD-related biomarkers associated with OA pathogenesis, providing new insights into OA mechanisms and potential therapeutic targets. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics Biological sciences/Immunology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers invited by journal 04 Apr, 2026 Editor assigned by journal 04 Apr, 2026 Editor invited by journal 03 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 01 Apr, 2026 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-9134544","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619158144,"identity":"32a45ee6-40be-425a-bc74-c35025d873c3","order_by":0,"name":"Gang Xue","email":"","orcid":"","institution":"Xi'an Daxing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Xue","suffix":""},{"id":619158145,"identity":"10b3a254-85a2-4d73-8b0e-ee280bd767ec","order_by":1,"name":"Yihai Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie3RMUvDQBTA8XccvHN4tGtCQb/CC4G0QqhfpaVwU4dOrkYCdQl0bcEPkUnX4Om59AM4CBYEJ4eMGYTadnHL1U3wfsst78+74wA87w86A/Pc1E16enEDCDWnQ2cSXdtRuCp0zBJQLGd64l5j1twjNONyl0iqH0TmKsR8zTGRFPeSkveUKwnKPJVtiSI7G98OUJ7n1I+n/NoB0vqldcvSlNUnEYKhpDflDwkBJa0JvG2ijDCgQzJgIzJnUj3GgpAD3idwTBJlVotVMeIwx8uwYD1B11t2X2mhbrZXC5XfBc1XOuwqY9sv9uOEDwceOb6nNr8Y9jzP+0++AYypSnSHgGTpAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Gansu Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yihai","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-03-16 07:41:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9134544/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9134544/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106725384,"identity":"5a41e3bf-c0aa-4053-84cc-9c2dcf3b7b49","added_by":"auto","created_at":"2026-04-12 18:32:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1175333,"visible":true,"origin":"","legend":"","description":"","filename":"anonymisedmanuscript1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9134544/v1_covered_7130ef80-36b7-4baf-afb0-9c205c2bf158.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Biomarkers for m6A-Mediated Programmed Cell Death in Osteoarthritis Transcripts with Experimental Validation.","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9134544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9134544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eOsteoarthritis (OA) is a common progressive joint disorder with limited treatment options. Emerging evidence suggests that N6-methyladenosine (m6A)-mediated programmed cell death (PCD) plays a crucial role in OA progression, yet the key regulatory genes remain unidentified. This study aims to identify m6A-modified PCD biomarkers to facilitate improved OA management.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eWe performed a comprehensive multi-omics analysis using transcriptomic data from two independent OA cohorts. Differentially expressed genes were integrated with 1,548 PCD-related genes and 23 m6A regulators. Feature selection was conducted using LASSO regression and Boruta algorithm. Biomarkers were validated through cross-dataset consistency verification, ROC curve analysis, and RT-qPCR. Mechanistic insights were explored through gene set enrichment analysis, immune infiltration deconvolution, and drug-gene interaction prediction.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIntegration analysis identified 26 candidate genes, from which machine learning selected six key genes. Five biomarkers (TNFAIP3, MYC, CDKN1A, ATF3, and CX3CR1) were robustly validated. These genes are involved in inflammatory and apoptotic pathways. Immune infiltration analysis revealed increased M2 macrophages, resting mast cells, plasma cells, and Tregs in OA tissues. Cadmium compounds were predicted as potential therapeutics. RT-qPCR confirmed significant downregulation of TNFAIP3, MYC, CDKN1A, and ATF3 in OA patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study identified five m6A-PCD-related biomarkers associated with OA pathogenesis, providing new insights into OA mechanisms and potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Identification of Biomarkers for m6A-Mediated Programmed Cell Death in Osteoarthritis Transcripts with Experimental Validation.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 19:03:37","doi":"10.21203/rs.3.rs-9134544/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T03:49:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119348957326463555654467968206465578115","date":"2026-05-13T12:37:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-05T00:25:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-05T00:23:10+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-03T13:25:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T14:37:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-01T12:15:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4034a7e6-ce70-4b83-99be-d32af166ab26","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T03:49:57+00:00","index":65,"fulltext":""},{"type":"reviewerAgreed","content":"119348957326463555654467968206465578115","date":"2026-05-13T12:37:32+00:00","index":64,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65886076,"name":"Health sciences/Biomarkers"},{"id":65886077,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":65886078,"name":"Health sciences/Diseases"},{"id":65886079,"name":"Biological sciences/Genetics"},{"id":65886080,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2026-04-09T19:03:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 19:03:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9134544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9134544","identity":"rs-9134544","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}