Multiomics and Machine Learning Identify Prognostic Immune Related Gene Signatures in Ovarian Cancer

Multiomics and Machine Learning Identify Prognostic Immune Related Gene Signatures in Ovarian Cancer | 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 Multiomics and Machine Learning Identify Prognostic Immune Related Gene Signatures in Ovarian Cancer Xiulan Wang, Xuewang Guo, Yanying Xu, Shaofang Hua This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7658174/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 Objective Ovarian cancer exhibits high heterogeneity, significant prognostic variability, and a lack of reliable prognostic biomarkers. This study aimed to construct and validate risk-associated gene signatures for ovarian cancer using multi-omics integration and machine learning techniques, thereby providing support for precise diagnosis and treatment. Methods Single-cell transcriptome data of ovarian cancer and bulk transcriptome data with clinical prognostic information were collected. Major cell types were identified via UMAP clustering. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to screen modules strongly associated with risk traits. Prognostic genes were initially filtered through differential expression analysis and univariate Cox regression, followed by refinement using LASSO regression to obtain 14 risk-associated genes, which were then used to construct a prognostic risk signature model. Results Kaplan-Meier survival analysis showed a significant difference in survival rates between the high-risk and low-risk groups (p = 0.028). In subgroup analysis, the survival rate of the Cluster C1 group was significantly lower than that of the C2 group (p = 0.012). Receiver Operating Characteristic (ROC) curve analysis demonstrated that the area under the curve (AUC) values of the model for 1-year, 2-year, and 3-year survival prediction were 0.60, 0.66, and 0.64, respectively. After integrating the risk score with clinical factors into a nomogram, the calibration curve confirmed a high consistency between the predicted and actual survival outcomes. The immune score and stromal score of the high-risk group were significantly higher than those of the low-risk group, with high expression of immune marker genes such as SRGN and PTPRC. Drug sensitivity analysis revealed that the high-risk group was more sensitive to Foretinib and Pictilisib, while the low-risk group was more sensitive to Cediranib. Conclusion The 14-gene risk signature model constructed in this study exhibits stable prognostic predictive ability, can correlate with the state of the immune microenvironment, and guide personalized medication. It provides a reliable molecular tool and theoretical basis for precise prognostic evaluation and treatment option selection in ovarian cancer. Full Text Additional Declarations No competing interests reported. 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. 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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-7658174","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560340013,"identity":"adb78dce-696a-4183-b981-071cb8887995","order_by":0,"name":"Xiulan Wang","email":"","orcid":"","institution":"Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiulan","middleName":"","lastName":"Wang","suffix":""},{"id":560340016,"identity":"aa08c482-c3a8-437e-8dc5-2d8ae91a911d","order_by":1,"name":"Xuewang Guo","email":"","orcid":"","institution":"Second Hospital of Tianjin Medical 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Cancer","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7658174/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7658174/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eOvarian cancer exhibits high heterogeneity, significant prognostic variability, and a lack of reliable prognostic biomarkers. This study aimed to construct and validate risk-associated gene signatures for ovarian cancer using multi-omics integration and machine learning techniques, thereby providing support for precise diagnosis and treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSingle-cell transcriptome data of ovarian cancer and bulk transcriptome data with clinical prognostic information were collected. Major cell types were identified via UMAP clustering. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to screen modules strongly associated with risk traits. Prognostic genes were initially filtered through differential expression analysis and univariate Cox regression, followed by refinement using LASSO regression to obtain 14 risk-associated genes, which were then used to construct a prognostic risk signature model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eKaplan-Meier survival analysis showed a significant difference in survival rates between the high-risk and low-risk groups (p\u0026thinsp;=\u0026thinsp;0.028). In subgroup analysis, the survival rate of the Cluster C1 group was significantly lower than that of the C2 group (p\u0026thinsp;=\u0026thinsp;0.012). Receiver Operating Characteristic (ROC) curve analysis demonstrated that the area under the curve (AUC) values of the model for 1-year, 2-year, and 3-year survival prediction were 0.60, 0.66, and 0.64, respectively. After integrating the risk score with clinical factors into a nomogram, the calibration curve confirmed a high consistency between the predicted and actual survival outcomes. The immune score and stromal score of the high-risk group were significantly higher than those of the low-risk group, with high expression of immune marker genes such as SRGN and PTPRC. Drug sensitivity analysis revealed that the high-risk group was more sensitive to Foretinib and Pictilisib, while the low-risk group was more sensitive to Cediranib.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe 14-gene risk signature model constructed in this study exhibits stable prognostic predictive ability, can correlate with the state of the immune microenvironment, and guide personalized medication. It provides a reliable molecular tool and theoretical basis for precise prognostic evaluation and treatment option selection in ovarian cancer.\u003c/p\u003e","manuscriptTitle":"Multiomics and Machine Learning Identify Prognostic Immune Related Gene Signatures in Ovarian Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 16:58:27","doi":"10.21203/rs.3.rs-7658174/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":"0a703b8e-c640-46c3-9229-29439c0d93cf","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T08:25:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 16:58:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7658174","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7658174","identity":"rs-7658174","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}