Identifying Hub Genes of Alzheimer’s and Parkinson’s Diseases via h-cutoff: A New Methodological Approach

Identifying Hub Genes of Alzheimer’s and Parkinson’s Diseases via h-cutoff: A New Methodological Approach | 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 Method Article Identifying Hub Genes of Alzheimer’s and Parkinson’s Diseases via h-cutoff: A New Methodological Approach Fred Y. Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9622183/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: Alzheimer’s disease (AD) and Parkinson’s disease (PD) are genetically complex neurodegenerative disorders in which disease-associated genes act through coordinated immune, glial, proteostatic, metabolic, synaptic and vascular programs rather than through isolated single-gene effects. Weighted co-expression and co-occurrence networks are therefore attractive for prioritizing disease genes, but conventional hard thresholds can remove low-weight edges that carry high structural importance. Objective: This manuscript refines the original h-metrics terminology into an explicit h-cutoff framework for weighted biological networks. The revised procedure uses co-expression or co-occurrence values as edge weights, applies a one-order h-cutoff to obtain an h-subnet, adds weak bridge edges to form an h-backbone, and then applies a two-order h-cutoff within the h-backbone to identify final Hub genes. Methods: The method was formalized for non-integer edge weights by extending the integer h-strength definition to a continuous h-strength through scaling and interpolation in rank space. The analytical design was organized into AD/PD-shared, AD-specific and PD-specific layers using public resources, including GSE48350, GSE26927, GSE174367, GSE161045, NIAGADS and related AD/PD transcriptomic and genetic studies. The mathematical workflow was written as a reproducible edge-to-backbone-to-gene pipeline with numbered formulas, clearly separating functional edge strength, structural weak-bridge value and final node-level Hub gene selection. Results: The shared AD/PD h-backbone prioritized CXCR4, FLT1, HSPB1, HSPA1A, CALM3, CDC42, RAB3A, TREM2 and APOE, supporting a convergent neurodegenerative architecture involving neuroinflammation, proteostasis, glial lipid biology and synaptic/cytoskeletal remodeling. The AD-specific layer prioritized DLAT, CCDC88B, SREBF1, APOE, CLU, TREM2 and TYROBP, consistent with mitochondrial stress and glial lipid/immune regulation. The PD-specific layer prioritized PSMB8, GRIA1, RGS8, BAG3, SYN1, CALB2, SNCA and TH, consistent with proteasome/chaperone stress, synaptic signaling, alpha-synuclein biology and dopaminergic vulnerability. Conclusions: Continuous h-cutoff provides an adaptive, interpretable and reproducible alternative to arbitrary correlation thresholding. It preserves the original h-backbone principle of integrating functional and structural network information while adapting h-strength to non-integer biological edge weights. The revised method is suitable for gene-prioritization studies in AD, PD and other complex polygenic diseases. Medical Informatics Alzheimer’s disease Parkinson’s disease h-cutoff continuous h-strength h-subnet h-backbone weak bridge co-expression Hub gene network biology. Full Text Additional Declarations The authors declare no competing interests. Supplementary Files ADPDhdatasets.zip 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-9622183","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":635016504,"identity":"ff7b998f-9833-4bbd-addc-1c3f6ef5b22c","order_by":0,"name":"Fred Y. 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Weighted co-expression and co-occurrence networks are therefore attractive for prioritizing disease genes, but conventional hard thresholds can remove low-weight edges that carry high structural importance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003eThis manuscript refines the original h-metrics terminology into an explicit \u003cstrong\u003eh-cutoff\u003c/strong\u003eframework for weighted biological networks. The revised procedure uses co-expression or co-occurrence values as edge weights, applies a one-order h-cutoff to obtain an h-subnet, adds weak bridge edges to form an h-backbone, and then applies a two-order h-cutoff within the h-backbone to identify final Hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThe method was formalized for non-integer edge weights by extending the integer h-strength definition to a continuous h-strength through scaling and interpolation in rank space. The analytical design was organized into AD/PD-shared, AD-specific and PD-specific layers using public resources, including GSE48350, GSE26927, GSE174367, GSE161045, NIAGADS and related AD/PD transcriptomic and genetic studies. The mathematical workflow was written as a reproducible edge-to-backbone-to-gene pipeline with numbered formulas, clearly separating functional edge strength, structural weak-bridge value and final node-level Hub gene selection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eThe shared AD/PD h-backbone prioritized CXCR4, FLT1, HSPB1, HSPA1A, CALM3, CDC42, RAB3A, TREM2 and APOE, supporting a convergent neurodegenerative architecture involving neuroinflammation, proteostasis, glial lipid biology and synaptic/cytoskeletal remodeling. The AD-specific layer prioritized DLAT, CCDC88B, SREBF1, APOE, CLU, TREM2 and TYROBP, consistent with mitochondrial stress and glial lipid/immune regulation. The PD-specific layer prioritized PSMB8, GRIA1, RGS8, BAG3, SYN1, CALB2, SNCA and TH, consistent with proteasome/chaperone stress, synaptic signaling, alpha-synuclein biology and dopaminergic vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Continuous h-cutoff provides an adaptive, interpretable and reproducible alternative to arbitrary correlation thresholding. It preserves the original h-backbone principle of integrating functional and structural network information while adapting h-strength to non-integer biological edge weights. The revised method is suitable for gene-prioritization studies in AD, PD and other complex polygenic diseases.\u003c/p\u003e","manuscriptTitle":"Identifying Hub Genes of Alzheimer’s and Parkinson’s Diseases via h-cutoff: A New Methodological Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 14:46:11","doi":"10.21203/rs.3.rs-9622183/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":"b256cbf7-627c-4256-9639-b27227ec0b68","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67585550,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2026-05-08T14:46:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 14:46:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9622183","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9622183","identity":"rs-9622183","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}