Integrative transcriptomics and molecular simulations identify IRS1 and PRKCQ as translational biomarkers for EDC-associated PCOS risk stratification

Integrative transcriptomics and molecular simulations identify IRS1 and PRKCQ as translational biomarkers for EDC-associated PCOS risk stratification | 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 Integrative transcriptomics and molecular simulations identify IRS1 and PRKCQ as translational biomarkers for EDC-associated PCOS risk stratification Xin Liu, Lei Zhang, Sai Han, Jia Liu, Zhihong Wu, Li Gong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9227802/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Endocrine-disrupting chemicals (EDCs) are linked to polycystic ovary syndrome (PCOS), but their transcriptional mechanisms remain unclear. Methods Microarray datasets were analyzed to identify DEGs, with GSE10946 as the training cohort and the merged external datasets GSE106724, GSE34526, and GSE98595 as the validation cohort. EDC-related targets were intersected with DEGs to obtain EDC-associated DEGs. Protein-protein interaction network construction and machine learning modeling were employed to identify hub genes. The diagnostic performance of hub genes was evaluated using receiver operating characteristic analysis, and a nomogram was constructed based on the key predictors. Gene set enrichment analysis (GSEA), immune cell infiltration analysis, molecular docking, and molecular dynamics simulations were conducted to explore potential mechanisms. Results A total of 63 EDC-related DEGs were screened, displaying multi-target regulatory features and enrichment in DNA replication/repair, cell cycle, and p53 signaling pathways. 13 hub genes were identified, among which insulin receptor substrate 1 (IRS1) and protein kinase C theta (PRKCQ) showed consistent upregulation in PCOS samples and demonstrated good diagnostic performance. An IRS1-PRKCQ nomogram achieved an area under the receiver operating characteristic curve (AUC) of 0.773 with good calibration and clinical net benefit. GSEA and immune analyses suggested associations with metabolic/replicative programs and immune infiltration patterns. Bisphenol A showed favorable docking to IRS1/PRKCQ, and molecular dynamics simulations supported a stable IRS1-bisphenol A complex. Conclusions IRS1 and PRKCQ represent EDC-associated biomarkers with potential clinical utility for PCOS risk stratification and mechanistic insight into EDC-related dysregulation. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Endocrinology Polycystic ovary syndrome Endocrine-disrupting chemicals Machine learning molecular docking Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 May, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Editor invited by journal 03 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 01 Apr, 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-9227802","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":629634558,"identity":"bc8f4e2b-4767-4942-b016-8bd78c1a2a72","order_by":0,"name":"Xin Liu","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":629634559,"identity":"3795ac20-f583-4e9b-9d56-a6efc481032c","order_by":1,"name":"Lei Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Shandong First Medical University \u0026 Shandong Provincial QianFoShan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":629634560,"identity":"a230aac3-ca85-460d-9526-e925cbb748fb","order_by":2,"name":"Sai Han","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Sai","middleName":"","lastName":"Han","suffix":""},{"id":629634561,"identity":"13fe13d7-0a4c-4c0b-83ae-5c6232e558bf","order_by":3,"name":"Jia Liu","email":"","orcid":"","institution":"Qilu Hospital, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":629634562,"identity":"faeb1d72-1fda-4124-8c30-510ecca42c7b","order_by":4,"name":"Zhihong Wu","email":"","orcid":"","institution":"Qilu Hospital, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Zhihong","middleName":"","lastName":"Wu","suffix":""},{"id":629634563,"identity":"eb98d61c-3acf-441e-8e90-6a73515e9206","order_by":5,"name":"Li Gong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYLCCBwY2DGzsDGwgNg8DA3MDYS0JBmkMbMxQLTwMjMRoYTgMNBuihYGgFnn33sMvEgrOy/Yx85g9+PDnjow9+8EGhh8V23BqMTxzLs0iweC2cRszj7nhzLZnPDw8iQ2MPWdu49YyI8fMAKglEajFTJq34TDQL4kNzIxtBLWcg2jh+QPUwv8QvxZ5iRzjBwkGB6Ba2IBaJAjYYsBzxgwYyMlAv7CVSYL9cuNhw0F8fpFv7zH+8OGPnez89uZtEsAQs2fvTz744EcFHlsOMLBJAGlYXBxAInHZ0sDA/AFDyygYBaNgFIwCZAAAwlxUXp3lWFsAAAAASUVORK5CYII=","orcid":"","institution":"Qilu Hospital, Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Gong","suffix":""}],"badges":[],"createdAt":"2026-03-26 01:08:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9227802/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9227802/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182829,"identity":"603d7ec6-0963-4ae9-99c4-e9ba63ffc31b","added_by":"auto","created_at":"2026-04-30 08:59:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1289393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9227802/v1_covered_88f6f432-206a-4776-b81b-d34a58bc9483.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative transcriptomics and molecular simulations identify IRS1 and PRKCQ as translational biomarkers for EDC-associated PCOS risk stratification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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},"keywords":"Polycystic ovary syndrome, Endocrine-disrupting chemicals, Machine learning, molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-9227802/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9227802/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEndocrine-disrupting chemicals (EDCs) are linked to polycystic ovary syndrome (PCOS), but their transcriptional mechanisms remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMicroarray datasets were analyzed to identify DEGs, with GSE10946 as the training cohort and the merged external datasets GSE106724, GSE34526, and GSE98595 as the validation cohort. EDC-related targets were intersected with DEGs to obtain EDC-associated DEGs. Protein-protein interaction network construction and machine learning modeling were employed to identify hub genes. The diagnostic performance of hub genes was evaluated using receiver operating characteristic analysis, and a nomogram was constructed based on the key predictors. Gene set enrichment analysis (GSEA), immune cell infiltration analysis, molecular docking, and molecular dynamics simulations were conducted to explore potential mechanisms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 63 EDC-related DEGs were screened, displaying multi-target regulatory features and enrichment in DNA replication/repair, cell cycle, and p53 signaling pathways. 13 hub genes were identified, among which insulin receptor substrate 1 (IRS1) and protein kinase C theta (PRKCQ) showed consistent upregulation in PCOS samples and demonstrated good diagnostic performance. An IRS1-PRKCQ nomogram achieved an area under the receiver operating characteristic curve (AUC) of 0.773 with good calibration and clinical net benefit. GSEA and immune analyses suggested associations with metabolic/replicative programs and immune infiltration patterns. Bisphenol A showed favorable docking to IRS1/PRKCQ, and molecular dynamics simulations supported a stable IRS1-bisphenol A complex.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIRS1 and PRKCQ represent EDC-associated biomarkers with potential clinical utility for PCOS risk stratification and mechanistic insight into EDC-related dysregulation.\u003c/p\u003e","manuscriptTitle":"Integrative transcriptomics and molecular simulations identify IRS1 and PRKCQ as translational biomarkers for EDC-associated PCOS risk stratification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 18:15:13","doi":"10.21203/rs.3.rs-9227802/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"210150664697094483130131243047491490161","date":"2026-05-19T04:13:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T22:13:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T22:11:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-03T08:43:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T16:01:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-01T14:12:01+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":"1cd17d66-319e-4aeb-b759-a4ebc2e07774","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"210150664697094483130131243047491490161","date":"2026-05-19T04:13:39+00:00","index":97,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67018010,"name":"Health sciences/Biomarkers"},{"id":67018011,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":67018012,"name":"Health sciences/Diseases"},{"id":67018013,"name":"Health sciences/Endocrinology"}],"tags":[],"updatedAt":"2026-04-29T18:15:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 18:15:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9227802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9227802","identity":"rs-9227802","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}