Identifying MSMO1, ELOVL6, AACS, and CERS2 related to lipid metabolism as biomarkers of Parkinson's disease

Identifying MSMO1, ELOVL6, AACS, and CERS2 related to lipid metabolism as biomarkers of 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 Article Identifying MSMO1, ELOVL6, AACS, and CERS2 related to lipid metabolism as biomarkers of Parkinson's disease Huiqing Wang, Mingpei Zhao, Guorong Chen, Yuanxiang Lin, Dezhi Kang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4266364/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The mechanisms underlying lipid metabolic disorders in Parkinson's diseases (PD) remain unclear. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify PD-related modular genes and differentially expressed genes (DEGs). Lipid metabolism-related genes (LMRGs) were extracted from Molecular Signatures Database. Candidate genes were assessed with overlapping modular genes, DEGs, and LMRGs for the purpose of building protein-protein interaction(PPI) networks. Then, biomarkers were generated by machine learning and Backpropagation Neural Network development according to candidate genes. Biomarker-based enrichment and network modulation analyses were executed to investigate related signal pathway. Following dimensionality reduction clustering and annotation, scRNA-seq was submitted to cellular interactions and trajectory analysis to analyze regulatory mechanisms of critical cells. Finally, qRT-PCR was conducted to confirm the expression of biomarkers in PD patients. Four biomarkers (MSMO1, ELOVL6, AACS, and CERS2) were obtained and highly predictive after analysis mentioned above. Then, OPC, Oli, and Neu cells were the primary expression sites for biomarkers according to scRNA-seq studies. Finally, we confirmed mRNA of MSMO1, ELOVL6 and AACS were downregulated in PD patients comparing with control, while CERS2 was upregulated. In conclusion, MSMO1, ELOVL6, AACS, and CERS2 related to LMRGs could be new biomarkers for diagnosing and treating PD. Biological sciences/Biological techniques/Bioinformatics Biological sciences/Computational biology and bioinformatics Biological sciences/Molecular biology Biological sciences/Neuroscience Parkinson's disease Lipid metabolism Machine learning WGCNA scRNA-seq biomarker Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFiles.zip Cite Share Download PDF Status: Published Journal Publication published 30 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 May, 2024 Reviews received at journal 18 May, 2024 Reviews received at journal 25 Apr, 2024 Reviewers agreed at journal 25 Apr, 2024 Reviewers agreed at journal 25 Apr, 2024 Reviewers invited by journal 25 Apr, 2024 Editor assigned by journal 25 Apr, 2024 Editor invited by journal 18 Apr, 2024 Submission checks completed at journal 18 Apr, 2024 First submitted to journal 14 Apr, 2024 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. 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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-4266364","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":293945159,"identity":"5ffaf816-e45a-49f7-8b3f-6b61ea478802","order_by":0,"name":"Huiqing Wang","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huiqing","middleName":"","lastName":"Wang","suffix":""},{"id":293945161,"identity":"79a5d236-5aed-40cd-859b-e6db22299528","order_by":1,"name":"Mingpei Zhao","email":"","orcid":"","institution":"Fujian Medical 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metabolism, Machine learning, WGCNA, scRNA-seq, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-4266364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4266364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The mechanisms underlying lipid metabolic disorders in Parkinson's diseases (PD) remain unclear. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify PD-related modular genes and differentially expressed genes (DEGs). Lipid metabolism-related genes (LMRGs) were extracted from Molecular Signatures Database. Candidate genes were assessed with overlapping modular genes, DEGs, and LMRGs for the purpose of building protein-protein interaction(PPI) networks. Then, biomarkers were generated by machine learning and Backpropagation Neural Network development according to candidate genes. Biomarker-based enrichment and network modulation analyses were executed to investigate related signal pathway. Following dimensionality reduction clustering and annotation, scRNA-seq was submitted to cellular interactions and trajectory analysis to analyze regulatory mechanisms of critical cells. Finally, qRT-PCR was conducted to confirm the expression of biomarkers in PD patients. Four biomarkers (MSMO1, ELOVL6, AACS, and CERS2) were obtained and highly predictive after analysis mentioned above. Then, OPC, Oli, and Neu cells were the primary expression sites for biomarkers according to scRNA-seq studies. Finally, we confirmed mRNA of MSMO1, ELOVL6 and AACS were downregulated in PD patients comparing with control, while CERS2 was upregulated. In conclusion, MSMO1, ELOVL6, AACS, and CERS2 related to LMRGs could be new biomarkers for diagnosing and treating PD.","manuscriptTitle":"Identifying MSMO1, ELOVL6, AACS, and CERS2 related to lipid metabolism as biomarkers of Parkinson's disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-23 20:41:53","doi":"10.21203/rs.3.rs-4266364/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-22T06:11:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-18T10:06:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-26T01:09:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1c0bf26a-5b01-4286-a5db-e26f0b9651df","date":"2024-04-26T00:38:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42484429-ef7a-41ab-a19d-a977f276de24","date":"2024-04-25T12:56:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-25T09:51:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-25T09:49:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-18T20:01:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-18T19:59:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-14T22:07:32+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":"c61f6a04-63c7-496c-9201-25001bd2463a","owner":[],"postedDate":"April 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30978675,"name":"Biological sciences/Biological techniques/Bioinformatics"},{"id":30978676,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":30978677,"name":"Biological sciences/Molecular biology"},{"id":30978678,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2024-08-05T15:58:46+00:00","versionOfRecord":{"articleIdentity":"rs-4266364","link":"https://doi.org/10.1038/s41598-024-68585-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-07-30 15:56:50","publishedOnDateReadable":"July 30th, 2024"},"versionCreatedAt":"2024-04-23 20:41:53","video":"","vorDoi":"10.1038/s41598-024-68585-3","vorDoiUrl":"https://doi.org/10.1038/s41598-024-68585-3","workflowStages":[]},"version":"v1","identity":"rs-4266364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4266364","identity":"rs-4266364","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}