In Silico RNA-Seq Analysis Reveals Key Transcriptomic Signatures in Pancreatic Cancer

In Silico RNA-Seq Analysis Reveals Key Transcriptomic Signatures 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 In Silico RNA-Seq Analysis Reveals Key Transcriptomic Signatures in Pancreatic Cancer varun chhabra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6588745/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 Pancreatic cancer is one of the most lethal malignancies, with limited therapeutic options and a five-year survival rate below 10%. This study aimed to elucidate the molecular mechanisms underlying pancreatic cancer progression and identify potential biomarkers and therapeutic targets through in silico analysis of RNA sequencing (RNA-Seq) data. Using the publicly available GSE119224 dataset, we identified 693 differentially expressed genes (DEGs) with a fold change ≥2.0 and adjusted p-value <0.05. Hierarchical clustering and principal component analysis revealed distinct gene expression profiles between cancerous and normal pancreatic tissues. Gene Ontology (GO) and KEGG pathway analyses indicated significant enrichment in pathways such as chronic myeloid leukemia, ErbB signaling pathway, thyroid hormone signaling pathway, human papillomavirus infection, prostate cancer, AGE-RAGE signaling pathway in diabetic complications, hepatitis C, breast cancer, neurotrophin signaling pathway, colorectal cancer. glioma, endocrine resistance, renal cell carcinoma, pancreatic cancer which are implicated in tumorigenesis and cancer progression. Protein-protein interaction (PPI) network analysis identified hub genes, including UBC, HNF4A, APP, CFTR, ALB, PLK1, TOP2A, INSR, CLU, SUMO2, CDC20, ELAV1, ERBB4, CDC20, APLP1, SUMO1, GATA4, SP1, EGR1 with key roles in cellular processes associated with cancer. Additionally, miRNA-target gene network analysis highlighted microRNAs such as hsa-mir-16-5p and hsa-let-7b-5p as critical regulators. Prognostic assessment using survival analysis and receiver operating characteristic (ROC) curves demonstrated that genes such as CFTR and PLK1 have potential as diagnostic and prognostic biomarkers. Our findings provide a comprehensive transcriptomic profile of pancreatic cancer, offering insights into the molecular pathways and regulatory networks involved in its progression. Although experimental validation is necessary, these results highlight promising biomarkers and therapeutic targets that warrant further investigation. This study underscores the utility of RNA- Seq and bioinformatics tools in advancing pancreatic cancer research and improving patient outcomes. Pancreatic cancer RNA Seq Data MicroRNA in- silico analysis Inflammation Coagulation 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-6588745","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452228006,"identity":"28b07ad9-757e-4ef6-b318-5c5e3a2d0d6d","order_by":0,"name":"varun chhabra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYPCCAwkMEkAqoQJIMDM3kKDlwxmQFkYStDDObANxCGgxOH72mXRBzZ08/tm9Dz/zzquN5m8HavlRsQ23ljPpZtIzjj0rlrhz3Fiad9vx3BmHGRsYe87cxqlFsiGNTZq34XBiw400BqCWY7kNQC3MjG14tPQ/g2iZfyON+TfvnGO58wlp4ZeA2rLhRhqb5MyGmtwNhLU8Y7bmOXY4ceOdY2wWH44dyN0I1HIQn1/Y+NMYb/PUHE6cd7uN+UZCTV3uvPOHDz74UYFbCzo4DCYPEK0eCOpIUTwKRsEoGAUjBAAA5m1ed/Z8hw8AAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"varun","middleName":"","lastName":"chhabra","suffix":""}],"badges":[],"createdAt":"2025-05-04 13:55:24","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-6588745/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6588745/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82127726,"identity":"2bc9d381-5640-4146-9a60-c6b8f8fe5ba2","added_by":"auto","created_at":"2025-05-07 04:36:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2274055,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6588745/v1_covered_0b732224-764c-47e7-bace-822f8cc298b0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIn Silico RNA-Seq Analysis Reveals Key Transcriptomic Signatures in Pancreatic Cancer\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"New Delhi, India ","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":"Pancreatic cancer, RNA Seq Data, MicroRNA, in- silico analysis, Inflammation, Coagulation","lastPublishedDoi":"10.21203/rs.3.rs-6588745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6588745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePancreatic cancer is one of the most lethal malignancies, with limited therapeutic options and a\u0026nbsp;five-year survival rate below 10%. This study aimed to elucidate the molecular mechanisms\u0026nbsp;underlying pancreatic cancer progression and identify potential biomarkers and therapeutic targets\u0026nbsp;through in silico analysis of RNA sequencing (RNA-Seq) data. Using the publicly available\u0026nbsp;GSE119224 dataset, we identified 693 differentially expressed genes (DEGs) with a fold change\u0026nbsp;≥2.0 and adjusted p-value \u0026lt;0.05. Hierarchical clustering and principal component analysis\u0026nbsp;revealed distinct gene expression profiles between cancerous and normal pancreatic tissues. Gene\u0026nbsp;Ontology (GO) and KEGG pathway analyses indicated significant enrichment in pathways such\u0026nbsp;as chronic myeloid leukemia, ErbB signaling pathway, thyroid hormone signaling pathway, human\u0026nbsp;papillomavirus infection, prostate cancer, AGE-RAGE signaling pathway in diabetic\u0026nbsp;complications, hepatitis C, breast cancer, neurotrophin signaling pathway, colorectal cancer.\u0026nbsp;glioma, endocrine resistance, renal cell carcinoma, pancreatic cancer which are implicated in\u0026nbsp;tumorigenesis and cancer progression. Protein-protein interaction (PPI) network analysis\u0026nbsp;identified hub genes, including UBC, HNF4A, APP, CFTR, ALB, PLK1, TOP2A, INSR, CLU,\u0026nbsp;SUMO2, CDC20, ELAV1, ERBB4, CDC20, APLP1, SUMO1, GATA4, SP1, EGR1 with key\u0026nbsp;roles in cellular processes associated with cancer. Additionally, miRNA-target gene network\u0026nbsp;analysis highlighted microRNAs such as hsa-mir-16-5p and hsa-let-7b-5p as critical regulators.\u0026nbsp;Prognostic assessment using survival analysis and receiver operating characteristic (ROC) curves\u0026nbsp; demonstrated that genes such as CFTR and PLK1 have potential as diagnostic and prognostic\u0026nbsp;biomarkers. Our findings provide a comprehensive transcriptomic profile of pancreatic cancer,\u0026nbsp;offering insights into the molecular pathways and regulatory networks involved in its progression.\u0026nbsp;Although experimental validation is necessary, these results highlight promising biomarkers and\u0026nbsp;therapeutic targets that warrant further investigation. This study underscores the utility of RNA-\u003c/p\u003e\n\u003cp\u003eSeq and bioinformatics tools in advancing pancreatic cancer research and improving patient\u0026nbsp;outcomes.\u003c/p\u003e","manuscriptTitle":"In Silico RNA-Seq Analysis Reveals Key Transcriptomic Signatures in Pancreatic Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 04:28:31","doi":"10.21203/rs.3.rs-6588745/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":"167f4cb2-fa26-4fd7-acac-12987eb85cae","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T04:28:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 04:28:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6588745","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6588745","identity":"rs-6588745","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}