Integrative Transcriptomic and Machine Learning Analysis Reveals a Robust Gene Signature in Parkinson’s Disease

Integrative Transcriptomic and Machine Learning Analysis Reveals a Robust Gene Signature in Parkinson’s Disease | 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 Integrative Transcriptomic and Machine Learning Analysis Reveals a Robust Gene Signature in Parkinson’s Disease Amir Mahdi Taghizadeh, Fatemeh Farahani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8799127/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 Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuron loss in the substantia nigra and the accumulation of misfolded α-synuclein, leading to motor and non-motor symptoms. Early and accurate diagnosis remains challenging due to the gradual onset of symptoms and disease heterogeneity. In this study, we combined brain-region–specific transcriptomic profiling with interpretable machine learning to identify robust and biologically meaningful predictive gene signatures for PD. Differential gene expression analysis of post-mortem PD and control brain samples revealed a set of significantly dysregulated genes, predominantly involved in mitochondrial function, synaptic signaling, and calcium-mediated neuronal processes. Using Random Forest, Logistic Regression, and XGBoost classifiers, we derived a core set of seven overlapping genes (CALM1, DCLK1, FGF13, HMGN2, PRKACB, SV2C, TAC1) that consistently contributed to PD classification across models. Feature importance and model interpretability analyses highlighted these genes as key drivers of predictive performance. The final gene set achieved robust classification with cross-validated ROC-AUC of 0.845 and was further validated using an independent external dataset (ROC-AUC = 0.743), demonstrating generalizability across cohorts. Functional enrichment analysis linked these genes to neuronal signaling, synaptic function, and Parkinson’s disease–relevant pathways, providing mechanistic context to their predictive power. Overall, our integrative approach illustrates the potential of combining transcriptomics with explainable machine learning to generate reliable, interpretable molecular biomarkers for PD, which may facilitate early diagnosis and improve understanding of disease biology. Computational Neuroscience Medical Genetics Parkinson’s disease Brain transcriptomics Machine learning Molecular signature Biomarker discovery 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-8799127","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586480009,"identity":"8e804037-8f5d-4df5-a882-239b566a9f96","order_by":0,"name":"Amir Mahdi Taghizadeh","email":"data:image/png;base64,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","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Amir","middleName":"Mahdi","lastName":"Taghizadeh","suffix":""},{"id":586480011,"identity":"7cd2ce7d-ccc4-4ad3-b03b-0ae8af840914","order_by":1,"name":"Fatemeh Farahani","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Farahani","suffix":""}],"badges":[],"createdAt":"2026-02-05 16:04:16","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8799127/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8799127/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102295561,"identity":"9bb2a3a9-14ba-48dc-86bc-4e28f4c678e2","added_by":"auto","created_at":"2026-02-10 10:12:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2418742,"visible":true,"origin":"","legend":"","description":"","filename":"IntegrativeTranscriptomicandMachineLearningAnalysisRevealsaRobustGeneSignatureinParkinsonsDisease.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8799127/v1_covered_00b15ebc-a52a-4c60-b32e-4f43d5bac959.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIntegrative Transcriptomic and Machine Learning Analysis Reveals a Robust Gene Signature in Parkinson’s Disease\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Parkinson’s disease, Brain transcriptomics, Machine learning, Molecular signature, Biomarker discovery","lastPublishedDoi":"10.21203/rs.3.rs-8799127/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8799127/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuron loss in the substantia nigra and the accumulation of misfolded α-synuclein, leading to motor and non-motor symptoms. 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