A MyCAF-Based Signature for Prognosis, Immune Landscape, and Therapeutic Response in Pancreatic Cancer

A MyCAF-Based Signature for Prognosis, Immune Landscape, and Therapeutic Response in Pancreatic 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 Article A MyCAF-Based Signature for Prognosis, Immune Landscape, and Therapeutic Response in Pancreatic Cancer Wei Wei, Wuyang Zhang, Bin Wang, Hongji Zhu, Qingkai Meng, Shuai Ming, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8878509/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 Pancreatic ductal adenocarcinoma (PDAC) remains a highly aggressive cancer with a poor prognosis, driven in part by a complex tumor microenvironment where myofibroblastic cancer-associated fibroblasts (myCAFs) are key players. This study aimed to develop and validate a robust myCAF-related gene signature for prognostic stratification and therapeutic guidance in PAAD. Clinical and transcriptomic data from the TCGA-PAAD cohort were processed, and differentially expressed genes (DEGs) were identified, including 4222 up-regulated and 827 down-regulated genes. By intersecting DEGs with a myCAF gene set, we constructed a four-gene prognostic model (TOP2A, MKI67, COL7A1, MMP1) using LASSO-Cox regression. The model significantly stratified patients into high- and low-risk groups with distinct overall survival outcomes in the TCGA training set (p < 0.0001), a finding which was robustly validated in the independent ICGC-PACA-AU cohort (p = 0.0046). The high-risk group exhibited a significantly suppressed tumor immune microenvironment, characterized by reduced infiltration of critical T cells, including gamma delta T cells, CD8 + T cells, and resting memory CD4 + T cells, as well as monocytes. Crucially, drug sensitivity analysis revealed that the high-risk group demonstrated reduced sensitivity to several targeted therapies, including Afatinib, Dasatinib, Lapatinib, and Trametinib. In conclusion, we have established and validated a novel four-gene prognostic signature based on myCAF biology. This model not only serves as an independent prognostic indicator but also provides insights into the immune landscape and potential therapeutic vulnerabilities, offering a valuable framework for personalized treatment strategies in pancreatic cancer. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Oncology Full Text Additional Declarations No competing interests reported. Supplementary Files TableS1.xls Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 16 Mar, 2026 Editor invited by journal 19 Feb, 2026 Editor assigned by journal 15 Feb, 2026 Submission checks completed at journal 15 Feb, 2026 First submitted to journal 14 Feb, 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. 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-8878509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":607141445,"identity":"0b97e5c0-9771-42e9-88b3-acd6bff05488","order_by":0,"name":"Wei 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Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8878509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8878509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePancreatic ductal adenocarcinoma (PDAC) remains a highly aggressive cancer with a poor prognosis, driven in part by a complex tumor microenvironment where myofibroblastic cancer-associated fibroblasts (myCAFs) are key players. This study aimed to develop and validate a robust myCAF-related gene signature for prognostic stratification and therapeutic guidance in PAAD. Clinical and transcriptomic data from the TCGA-PAAD cohort were processed, and differentially expressed genes (DEGs) were identified, including 4222 up-regulated and 827 down-regulated genes. By intersecting DEGs with a myCAF gene set, we constructed a four-gene prognostic model (TOP2A, MKI67, COL7A1, MMP1) using LASSO-Cox regression. The model significantly stratified patients into high- and low-risk groups with distinct overall survival outcomes in the TCGA training set (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), a finding which was robustly validated in the independent ICGC-PACA-AU cohort (p\u0026thinsp;=\u0026thinsp;0.0046). 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This model not only serves as an independent prognostic indicator but also provides insights into the immune landscape and potential therapeutic vulnerabilities, offering a valuable framework for personalized treatment strategies in pancreatic cancer.\u003c/p\u003e","manuscriptTitle":"A MyCAF-Based Signature for Prognosis, Immune Landscape, and Therapeutic Response in Pancreatic Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 17:58:24","doi":"10.21203/rs.3.rs-8878509/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-16T19:04:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-19T19:07:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T01:46:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T01:46:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-14T08:39:35+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":"ada7bdb0-0068-43fb-ac21-db9a09ce1638","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64604896,"name":"Health sciences/Biomarkers"},{"id":64604897,"name":"Biological sciences/Cancer"},{"id":64604898,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":64604899,"name":"Biological sciences/Immunology"},{"id":64604900,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-18T17:58:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 17:58:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8878509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8878509","identity":"rs-8878509","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}