{"paper_id":"0fd0d2bf-f71a-4ddd-a8df-706571a50a45","body_text":"Advancing Breast Cancer Biomarkers: A Centromere-Related Gene Signature Integrated with Single-Cell Analysis for Prognostic 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 Advancing Breast Cancer Biomarkers: A Centromere-Related Gene Signature Integrated with Single-Cell Analysis for Prognostic Prediction Ye Lu, Shengbing Pei, Wenxiang Zhang, Zheng Qu, Xiangyu Wang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7015899/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Breast cancer is the most common malignancy among women worldwide and exhibits marked heterogeneity. Among its various subtypes, triple-negative breast cancer (TNBC) is associated with an inferior prognosis. Although molecular stratification tools such as Oncotype DX and MammaPrint have been adopted in clinical settings, prognostic models based on chromosomal instability remain inadequate. The centromere protein (CENP) family, as a key regulator of genomic stability, has been closely linked to tumor progression due to its aberrant expression. In this study, we integrated multi-omics data—including RNA transcriptomic profiles and single-cell RNA sequencing—and employed weighted gene co-expression network analysis (WGCNA) to identify core gene modules associated with CENPA. A prognostic risk model was developed using Cox regression analysis and the LASSO algorithm. Validation in independent cohorts demonstrated that the model effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly reduced five-year survival (p < 0.001). Furthermore, the single-cell analysis revealed that CENPA-high subpopulations were enriched in proliferative tumor cells and were associated with an immunosuppressive tumor microenvironment. This study is the first to systematically construct a CENP-based prognostic model for breast cancer, providing novel molecular biomarkers and potential therapeutic targets for personalized treatment. The biological function of the key molecule MMP1 in breast cancer was further validated through both in vitro and in vivo experiments. Breast Cancer CENPs Prognostic Model Single-cell RNA Sequencing MMP1 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 01 Jul, 2025 Submission checks completed at journal 01 Jul, 2025 First submitted to journal 01 Jul, 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. 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-7015899\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":572538463,\"identity\":\"353c461f-bbd2-4165-a1f5-b4642b3c72b5\",\"order_by\":0,\"name\":\"Ye Lu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical 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