An RNA Modification–Associated Gene-Based Prognostic Model and Its Relevance to the Immune Microenvironment and Therapeutic Response in Lung Adenocarcinoma

An RNA Modification–Associated Gene-Based Prognostic Model and Its Relevance to the Immune Microenvironment and Therapeutic Response in Lung Adenocarcinoma | 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 An RNA Modification–Associated Gene-Based Prognostic Model and Its Relevance to the Immune Microenvironment and Therapeutic Response in Lung Adenocarcinoma Zhen Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7997081/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective The most frequent subtype of non-small cell lung cancer, lung adenocarcinoma (LUAD), has extraordinary molecular heterogeneity and poor survival. While studies on individual RNA modifications like m⁶A or m⁵C are extensive, how multiple types of RNA alterations collectively shape LUAD prognosis, immune contexture, and therapeutic response is still not well understood. Methods TCGA provided LUAD patients' transcriptome and clinical data. We identified DEGs associated to RNA modification and created a predictive model using LASSO and Cox regression. Ten genes were used to create a RiskScore to separate patients into high- and low-risk groups in the final model. A total of thirty-seven DEGs associated with this stratification were further analyzed through GO/KEGG enrichment, GSEA, PPI network construction, immune infiltration, and drug-sensitivity prediction. External validation was performed using the GSE31210 dataset, and functional assays were conducted in LUAD cell lines to confirm key gene effects. Results The model showed moderate prognostic accuracy in the TCGA cohort (1-year AUC = 0.618) and a similar but non-significant trend in the validation dataset (GSE31210; HR = 1.33, p = 0.405). Differentiating genes between high- and low-risk groups were focused on epithelial-mesenchymal transition (EMT, NES = 2.65), NF-κB signaling (NES = 2.44), and KRAS signaling (NES = 2.10). The high-risk group showed decreased CD8⁺ T-cell infiltration and increased inflammatory activity, indicating immunological dysregulation. Drug-sensitivity research showed that Dasatinib responsiveness increased with TNS4 expression. In vitro, silencing of KLK6 or TNS4 markedly suppressed LUAD cell proliferation and migration. Conclusion This work developed a ten-gene prognostic model based on RNA-modification-related signatures and extended it to risk stratification, functional pathway analysis, and experimental validation of KLK6 and TNS4. The model may assist in early prognostic assessment and individualized therapeutic planning for LUAD, and highlights KLK6/TNS4 as potential molecular targets for precision treatment. Lung adenocarcinoma RNA modification Prognostic model Immune microenvironment Drug sensitivity Biomarker Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers invited by journal 30 Jan, 2026 Editor invited by journal 08 Jan, 2026 Editor assigned by journal 14 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 13 Nov, 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-7997081","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582888171,"identity":"0e66efa3-7b40-4000-bb3d-0c14ae2ac30c","order_by":0,"name":"Zhen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3RPQuCQBjA8QtBlwdcT7A+w4EgQYJfpcnpgqamiEBwq9Y+RhA4K0fX0svqGkJDkxGFU+SFs94YdH+4l+F+6HEIqVQ/WlINMM1QrNiWJra15kNBQPpLHkkoEZt2Qs7skj4jD1ByeNyyaR+QwXabRpJwwrpRAJ1wEQ8or34MgiBrIm46J8yKGGjoGDtUrwgGt5kwo/gSHdGrQ98yhANJ7xUBRLV8FEkQ/wBjhk4BYMxdbbTEoLfdxVrtt/dy4vn+Ocwf9DXrmQbjjUSk1W+h4+/cdlzUKWtayJxWqVSq/+sDVIdKahJtI84AAAAASUVORK5CYII=","orcid":"","institution":"Suining County People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-10-31 09:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7997081/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7997081/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101753081,"identity":"07fea2e4-0c56-4866-8588-a8387815222b","added_by":"auto","created_at":"2026-02-03 10:39:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2006809,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptultr.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7997081/v1_covered_05934d29-5971-49d4-86c6-8a013e2e9b09.pdf"},{"id":101660992,"identity":"7fbf4e92-e87f-4d1f-bc0d-b891533c01c5","added_by":"auto","created_at":"2026-02-02 10:51:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2265787,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7997081/v1/4578c54af636984a4ea34573.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An RNA Modification–Associated Gene-Based Prognostic Model and Its Relevance to the Immune Microenvironment and Therapeutic Response in Lung Adenocarcinoma","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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma, RNA modification, Prognostic model, Immune microenvironment, Drug sensitivity, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7997081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7997081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe most frequent subtype of non-small cell lung cancer, lung adenocarcinoma (LUAD), has extraordinary molecular heterogeneity and poor survival. While studies on individual RNA modifications like m⁶A or m⁵C are extensive, how multiple types of RNA alterations collectively shape LUAD prognosis, immune contexture, and therapeutic response is still not well understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTCGA provided LUAD patients' transcriptome and clinical data. We identified DEGs associated to RNA modification and created a predictive model using LASSO and Cox regression. Ten genes were used to create a RiskScore to separate patients into high- and low-risk groups in the final model. A total of thirty-seven DEGs associated with this stratification were further analyzed through GO/KEGG enrichment, GSEA, PPI network construction, immune infiltration, and drug-sensitivity prediction. External validation was performed using the GSE31210 dataset, and functional assays were conducted in LUAD cell lines to confirm key gene effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe model showed moderate prognostic accuracy in the TCGA cohort (1-year AUC\u0026thinsp;=\u0026thinsp;0.618) and a similar but non-significant trend in the validation dataset (GSE31210; HR\u0026thinsp;=\u0026thinsp;1.33, p\u0026thinsp;=\u0026thinsp;0.405). Differentiating genes between high- and low-risk groups were focused on epithelial-mesenchymal transition (EMT, NES\u0026thinsp;=\u0026thinsp;2.65), NF-κB signaling (NES\u0026thinsp;=\u0026thinsp;2.44), and KRAS signaling (NES\u0026thinsp;=\u0026thinsp;2.10). The high-risk group showed decreased CD8⁺ T-cell infiltration and increased inflammatory activity, indicating immunological dysregulation. Drug-sensitivity research showed that Dasatinib responsiveness increased with TNS4 expression. In vitro, silencing of KLK6 or TNS4 markedly suppressed LUAD cell proliferation and migration.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis work developed a ten-gene prognostic model based on RNA-modification-related signatures and extended it to risk stratification, functional pathway analysis, and experimental validation of KLK6 and TNS4. The model may assist in early prognostic assessment and individualized therapeutic planning for LUAD, and highlights KLK6/TNS4 as potential molecular targets for precision treatment.\u003c/p\u003e","manuscriptTitle":"An RNA Modification–Associated Gene-Based Prognostic Model and Its Relevance to the Immune Microenvironment and Therapeutic Response in Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 10:51:35","doi":"10.21203/rs.3.rs-7997081/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-19T08:18:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139468973866052538858871856255087895742","date":"2026-02-13T08:31:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T01:33:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310069154027323729724617435912565126755","date":"2026-02-09T00:12:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62744413968085293850857112964565016888","date":"2026-02-06T08:30:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T07:45:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-08T19:23:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-14T12:52:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T11:03:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-11-13T10:59:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11de4192-7b47-4354-bbd7-4cfac368702c","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T10:51:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 10:51:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7997081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7997081","identity":"rs-7997081","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}