Geometric Prognostic Singularities and Structural Leverage Drivers: A Manifold-Based Framework (M-CIM) for Cancer Gene Prioritization

Geometric Prognostic Singularities and Structural Leverage Drivers: A Manifold-Based Framework (M-CIM) for Cancer Gene Prioritization | 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 Geometric Prognostic Singularities and Structural Leverage Drivers: A Manifold-Based Framework (M-CIM) for Cancer Gene Prioritization Chi Hsing Wu, Kai Siang Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8537800/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 Precision cancer diagnosis relies on identifying driver genes, yet distinguishing these from passenger mutations in high-dimensional transcriptomics remains challenging. Gene prioritization in high-dimensional transcriptomic data conventionally relies on variance-based filtering, under the assumption that biologically important genes exhibit high expression variability. However, accumulating evidence suggests that structurally influential genes may exert disproportionate effects on cellular programs despite modest marginal variance. We introduce a manifold-based framework that quantifies gene importance through geometric sensitivity analysis. By training a nonlinear embedding with manifold regularization, we define a gradient-based sensitivity measure that captures how perturbations along individual gene dimensions propagate through the learned representation space. Applied to TCGA breast cancer transcriptomics (N=526, G=17,800), our approach identified structurally influential genes including ASCL2, NAPRT1, and OR10AG1, which ranked beyond the top 15,000 genes by variance yet exhibited high geometric leverage. Survival stratification based on manifold-sensitive genes achieved log-rank p=0.0048, compared to p=0.157 for PCA-based approaches. Ablation studies confirmed that performance gains arise from structural alignment rather than model complexity. These findings demonstrate that geometry-aware gene prioritization can reveal functionally important features that are systematically suppressed by variance-based filtering. Computational Biology Bioinformatics Cancer Biology gene prioritization manifold learning gradient sensitivity transcriptomics feature selection Full Text Additional Declarations The authors declare no competing interests. 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-8537800","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570621654,"identity":"f71204c8-8fd0-4e42-aeef-927c1e512873","order_by":0,"name":"Chi Hsing Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACxgYg8YGBgQfKJFIL44wEUrSAADNPAikOY24/YyZt+8NORr79cOPjghobBn7p4xfwO6wnx0w6JyGZx+BMYrPxjGNpDJJ9OQX4tczg3QbUwsxjIMHYJs3bcJjB4AwBd4K1WCTU88jPYGz/zdvwn0gtDAmHeRhuMLYx8zYcAGphP0DAL/mfLXvSjoP9Is1zLJlHsocHrw4Gw/ZjiTd+2FTby7cff/iZp8ZOjp+H/QF+LQ1oAkAreAzwapHHIkbAllEwCkbBKBhxAACKGj25xsJ3UgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0001-3396-6835","institution":"TaiScience Institution","correspondingAuthor":true,"prefix":"","firstName":"Chi","middleName":"Hsing","lastName":"Wu","suffix":""},{"id":570621655,"identity":"7c9e7593-a43a-4b0d-aa5c-e5a5f9197fd4","order_by":1,"name":"Kai Siang Chen","email":"","orcid":"https://orcid.org/0009-0004-3496-3371","institution":"Chia Nan University of Pharmacy and Science","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"Siang","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-01-07 07:03:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8537800/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8537800/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99758906,"identity":"5efe043a-6647-4f05-8c27-898d75187ee5","added_by":"auto","created_at":"2026-01-08 06:28:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":746359,"visible":true,"origin":"","legend":"","description":"","filename":"GenesPrioritizedbyManifoldGeometry3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8537800/v1_covered_35480011-5eb2-4845-abed-262d52e795e4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGeometric Prognostic Singularities and Structural Leverage\u003c/p\u003e\n\u003cp\u003eDrivers: A Manifold-Based Framework (M-CIM) for Cancer Gene Prioritization\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"TaiScience 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":"gene prioritization, manifold learning, gradient sensitivity, transcriptomics, feature selection","lastPublishedDoi":"10.21203/rs.3.rs-8537800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8537800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrecision cancer diagnosis relies on identifying driver genes, yet distinguishing these from passenger mutations in high-dimensional transcriptomics remains challenging. Gene prioritization in high-dimensional transcriptomic data conventionally relies on variance-based filtering, under the assumption that biologically important genes exhibit high expression variability. However, accumulating evidence suggests that structurally influential genes may exert disproportionate effects on cellular programs despite modest marginal variance.\u003c/p\u003e\n\u003cp\u003eWe introduce a manifold-based framework that quantifies gene importance through geometric sensitivity analysis. By training a nonlinear embedding with manifold regularization, we define a gradient-based sensitivity measure that captures how perturbations along individual gene dimensions propagate through the learned representation space. Applied to TCGA breast cancer transcriptomics (N=526, G=17,800), our approach identified structurally influential genes including ASCL2, NAPRT1, and OR10AG1, which ranked beyond the top 15,000 genes by variance yet exhibited high geometric leverage. Survival stratification based on manifold-sensitive genes achieved log-rank p=0.0048, compared to p=0.157 for PCA-based approaches. Ablation studies confirmed that performance gains arise from structural alignment rather than model complexity. These findings demonstrate that geometry-aware gene prioritization can reveal functionally important features that are systematically suppressed by variance-based filtering.\u003c/p\u003e","manuscriptTitle":"Geometric Prognostic Singularities and Structural Leverage\nDrivers: A Manifold-Based Framework (M-CIM) for Cancer Gene Prioritization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 06:28:09","doi":"10.21203/rs.3.rs-8537800/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":"a2e933cd-0545-4c63-810d-6ab0022d6bd8","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60720784,"name":"Computational Biology"},{"id":60720785,"name":"Bioinformatics"},{"id":60720786,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2026-01-08T06:28:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 06:28:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8537800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8537800","identity":"rs-8537800","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}