Using two-sample Mendelian randomization to study the causal relationship between BMI and pancreatic cancer and applying bioinformatics methods to identify important genes suggestive of poor prognosis in pancreatic cancer

Using two-sample Mendelian randomization to study the causal relationship between BMI and pancreatic cancer and applying bioinformatics methods to identify important genes suggestive of poor prognosis 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 Research Article Using two-sample Mendelian randomization to study the causal relationship between BMI and pancreatic cancer and applying bioinformatics methods to identify important genes suggestive of poor prognosis in pancreatic cancer Zhaoyuan Tang, Hao Wen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4288550/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 Background： A causal relationship between body mass index (BMI) and pancreatic cancer (PC) has been reported; however, due to traditional observational studies, residual confounding, reverse causation, and the presence of measurement error, a causal relationship between them has not yet been confirmed. The present study used Mendelian randomization (MR) to investigate the causal relationship between BMI and PC. Since pancreatic cancer is usually detected at an advanced stage most treatment options are ineffective. Therefore, this study used a bioinformatics approach to explore prognostic biomarkers of PC as a target to improve therapeutic efficacy and better understand the underlying mechanisms. Methods: We conducted a two-sample Mendelian randomization (MR) study to assess their causal relationship. Three MR analysis methods were used for causal inference and inverse variance weighting (IVW) was chosen as the primary method. Mendelian randomized polytomous residuals and outliers (MR-PRESSO) were used to exclude outlier SNPs. To assess the robustness of the MR results, a "leave-one-out" analysis was performed. In addition, to identify important genes suggestive of poor prognosis in PC. We first selected GSE71989, GSE32676, and GSE16515 from the Gene Expression Omnibus (GEO) system. Second, we applied the GEO2R online tool and Venn diagram software to obtain the common differentially expressed genes (DEGs) in the above three datasets. Third, these databases were analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), including molecular function (MF), cellular component (CC), biological process (BP), and KEGG pathways. Fourth, we established a protein-protein interaction (PPI) network and then applied cell-based molecular complex detection (MCODE) to further analyze the DEGs to identify some core genes. In addition, these core EDGs were imported into the unlcan online database for important prognostic information (P < 0.05). In addition, we further verified the expression of DEGs between PC tissues and normal pancreatic tissues by gene expression profiling interaction analysis (P < 0.05) to obtain qualified DEGs. We then reanalyzed the saturation of KEGG pathway enrichment of these DEGs. Finally, important genes suggestive of poor prognosis in PC were obtained. Results: Our MR study provided evidence that no heterogeneous genes were identified, no horizontal pleiotropy was detected by MR-PRESSO analysis, and there was a causal relationship between BMI and PC (IVW, OR = 1.26, 95% CI = [1.012, 1.572], P = 0.0392). Sensitivity analysis confirmed the robustness of the MR results. In addition, bioinformatics analysis identified CDK1 and RRM2 as new effective targets to improve the prognosis of PC patients. A large number of studies have demonstrated that these two genes are associated with the progression of different types of cancer, but few studies have been reported on these two genes in PC. Therefore, the data from our study may provide useful information and direction for future research in PC. Conclusion: Our study confirms the causal relationship between BMI and PC and identifies CDK1, and RRM2 genes as possible key players in the progression of PC. The present study may provide insights to explore the underlying mechanisms of PC development and prevention. body mass index pancreatic cancer Two-sample Mendelian randomization Biomarkers signaling pathways Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1.pdf AdditionalfileTable1.csv 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-4288550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293022810,"identity":"bfed8ee9-49da-4a95-9e24-f84fbb42f4f8","order_by":0,"name":"Zhaoyuan Tang","email":"","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoyuan","middleName":"","lastName":"Tang","suffix":""},{"id":293022811,"identity":"69ad8a6c-45c9-4272-86e6-82b6af4b477f","order_by":1,"name":"Hao Wen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3RsWrDMBCA4TOCmy7xKuNgv4LAax5GIqCt0Kl4CMRDkIdQsiZL8goZOxoMntTOHTo4BDKnFEqnEO0ttrN10DdpuB8kHYDn/UOY1nUr82uCaavcYd6fjDlqcbFVNgZ5Eq1t+pOEk4i2plI7UOfouGQDLhYXIhuZj8BArXNVIITlSnYnk0qe6O3MMDD6Xb1MgNvXQ3cCssroiSEycolFEPyhL1FFTG4eMfx+VIYNSPgMoo2pORJoGJZQg+6TtUAOMy5tQ71vScv1l9vgdLHfgPr8yedJWD53J7/QfeOe53nen25O2El8B7K89gAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Xinjiang Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wen","suffix":""}],"badges":[],"createdAt":"2024-04-18 14:50:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4288550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4288550/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61524619,"identity":"a3b26370-f94a-4e8c-9116-8ac2bb08b98a","added_by":"auto","created_at":"2024-07-31 19:29:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":616847,"visible":true,"origin":"","legend":"","description":"","filename":"pancreatic.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4288550/v1_covered_1ac67c2f-1587-46ba-89bf-5cbab39c5544.pdf"},{"id":55133367,"identity":"81ac31a0-3a06-46fc-b4fc-4cca394a8dde","added_by":"auto","created_at":"2024-04-23 05:16:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53011,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4288550/v1/b007652056ed17517f19d9ea.pdf"},{"id":55133366,"identity":"1712074c-347e-4155-968f-8ab8687641fb","added_by":"auto","created_at":"2024-04-23 05:16:00","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27663,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalfileTable1.csv","url":"https://assets-eu.researchsquare.com/files/rs-4288550/v1/79ed0c7946d2f91f834de57e.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using two-sample Mendelian randomization to study the causal relationship between BMI and pancreatic cancer and applying bioinformatics methods to identify important genes suggestive of poor prognosis in pancreatic 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":"body mass index, pancreatic cancer, Two-sample Mendelian randomization, Biomarkers, signaling pathways","lastPublishedDoi":"10.21203/rs.3.rs-4288550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4288550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground： \u003c/strong\u003eA causal relationship between body mass index (BMI) and pancreatic cancer (PC) has been reported; however, due to traditional observational studies, residual confounding, reverse causation, and the presence of measurement error, a causal relationship between them has not yet been confirmed. The present study used Mendelian randomization (MR) to investigate the causal relationship between BMI and PC. Since pancreatic cancer is usually detected at an advanced stage most treatment options are ineffective. Therefore, this study used a bioinformatics approach to explore prognostic biomarkers of PC as a target to improve therapeutic efficacy and better understand the underlying mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a two-sample Mendelian randomization (MR) study to assess their causal relationship. Three MR analysis methods were used for causal inference and inverse variance weighting (IVW) was chosen as the primary method. Mendelian randomized polytomous residuals and outliers (MR-PRESSO) were used to exclude outlier SNPs. To assess the robustness of the MR results, a \"leave-one-out\" analysis was performed. In addition, to identify important genes suggestive of poor prognosis in PC. We first selected GSE71989, GSE32676, and GSE16515 from the Gene Expression Omnibus (GEO) system. Second, we applied the GEO2R online tool and Venn diagram software to obtain the common differentially expressed genes (DEGs) in the above three datasets. Third, these databases were analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), including molecular function (MF), cellular component (CC), biological process (BP), and KEGG pathways. Fourth, we established a protein-protein interaction (PPI) network and then applied cell-based molecular complex detection (MCODE) to further analyze the DEGs to identify some core genes. In addition, these core EDGs were imported into the unlcan online database for important prognostic information (P \u0026lt; 0.05). In addition, we further verified the expression of DEGs between PC tissues and normal pancreatic tissues by gene expression profiling interaction analysis (P \u0026lt; 0.05) to obtain qualified DEGs. We then reanalyzed the saturation of KEGG pathway enrichment of these DEGs. Finally, important genes suggestive of poor prognosis in PC were obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our MR study provided evidence that no heterogeneous genes were identified, no horizontal pleiotropy was detected by MR-PRESSO analysis, and there was a causal relationship between BMI and PC (IVW, OR = 1.26, 95% CI = [1.012, 1.572], P = 0.0392). Sensitivity analysis confirmed the robustness of the MR results. In addition, bioinformatics analysis identified CDK1 and RRM2 as new effective targets to improve the prognosis of PC patients. A large number of studies have demonstrated that these two genes are associated with the progression of different types of cancer, but few studies have been reported on these two genes in PC. Therefore, the data from our study may provide useful information and direction for future research in PC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our study confirms the causal relationship between BMI and PC and identifies CDK1, and RRM2 genes as possible key players in the progression of PC. The present study may provide insights to explore the underlying mechanisms of PC development and prevention.\u003c/p\u003e","manuscriptTitle":"Using two-sample Mendelian randomization to study the causal relationship between BMI and pancreatic cancer and applying bioinformatics methods to identify important genes suggestive of poor prognosis in pancreatic cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-23 05:15:55","doi":"10.21203/rs.3.rs-4288550/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":"c388bafa-d8d2-4609-a586-98b981aeb4d2","owner":[],"postedDate":"April 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-31T19:21:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-23 05:15:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4288550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4288550","identity":"rs-4288550","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}