System biology based integrative analysis to predict early biomarkers for COVID-related MIS in children

System biology based integrative analysis to predict early biomarkers for COVID-related MIS in children | 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 System biology based integrative analysis to predict early biomarkers for COVID-related MIS in children Awadhesh Kumar Verma, Tanya Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5759195/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 The outbreak of COVID-19 caused by SARS-CoV-2 has introduced unparalleled difficulties, including the appearance of a rare but serious condition in children referred to as Multisystem Inflammatory Syndrome in Children (MIS-C). This syndrome, marked by an overactive immune response resulting in extensive inflammation throughout various organ systems, has underscored the pressing necessity for predictive biomarkers to enhance early diagnosis and treatment. This research aimed to fill this gap using a comprehensive bioinformatics approach, applying fold-change and adjusted p-value techniques to detect differentially expressed genes (DEGs). DESeq2 was utilized to enhance these biomarkers, ensuring a strong and precise selection procedure. To accurately predict MIS-C biomarkers, numerous machine learning classifiers were evaluated, and the Random Forest model excelled compared to the rest. It showed a significant accuracy of 0.81 for moderate DEGs, confirming its appropriateness for biomarker prediction in COVID-19-related MIS-C. The results highlight the strength of computational biology in understanding the molecular processes associated with intricate conditions such as MIS-C, facilitating focused exploration of treatment approaches. Through in silico analysis and visualisation, a particular drug ligand named Methylprednisolone came out to an ideal candidate that shows favourable binding affinity with the genes deduced from ML analysis. This study not only clarifies the pathophysiology of MIS-C but also opens avenues for utilizing advanced statistical and machine learning techniques in clinical studies. By connecting computational and clinical fields, this study marks an important advancement in reducing the effects of severe COVID-19-related issues in children. Bioinformatics Computational Biology Pediatrics Infectious Diseases Epidemiology Scientific Communication Artificial Intelligence and Machine Learning COVID-19 MIS-C Biomarkers DEGs Random Forest Accuracy Molecular Docking In silico analysis Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Supplementarycovidmisc.pdf Supplementary file for System biology based integrative analysis to predict early biomarkers for COVID-related MIS in children 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. 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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-5759195","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397372369,"identity":"e88f5b11-ab64-4d29-985a-cdf4a5e5ffc9","order_by":0,"name":"Awadhesh Kumar Verma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDCCAwwMEmAGewOQMLAgRQvPAZAWCVK0SCSAScI6+G4ffnibp+JeNP/M51c3/CiQYOBv707Aq0XyXJqxNc+Z4twZt3PKbvYAHSZx5uwGvFoMzjCYSc5sS8htuJ2TdoMHqMVAIpeQFvZvkjP/JeTOv3km7eYf4rTwmEl8bEjI3XCD/dhtomyRPMNTbPHhWELuxjM5bLdlDCR4CPqF7wz7xhsJNQm5844ff3bzzR8bOf72XvxakACPAZgkVjkIsD8gRfUoGAWjYBSMIAAAzahMPORV/eoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1497-6210","institution":"Lovely Professional University, Phagwara, Punjab","correspondingAuthor":true,"prefix":"","firstName":"Awadhesh","middleName":"Kumar","lastName":"Verma","suffix":""},{"id":397372370,"identity":"45f1fa54-72dd-45e4-be71-2d1ba6fe89b6","order_by":1,"name":"Tanya Singh","email":"","orcid":"https://orcid.org/0000-0002-3817-145X","institution":"Lovely Professional University, Phagwara, Punjab","correspondingAuthor":false,"prefix":"","firstName":"Tanya","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2025-01-03 15:13:41","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-5759195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5759195/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73014913,"identity":"09edb441-1640-43dd-bda6-5e4bf65b4386","added_by":"auto","created_at":"2025-01-06 02:28:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1112568,"visible":true,"origin":"","legend":"","description":"","filename":"03012025PredictearlybiomarkersforcovidrelatedMISinchildren.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5759195/v1_covered_2542330f-acf9-45c1-874c-1dc9fabb3e27.pdf"},{"id":73014335,"identity":"a7e3c4ce-94a1-4312-b990-c324049da5c7","added_by":"auto","created_at":"2025-01-06 02:20:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3788711,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary file for System biology based integrative analysis to predict early biomarkers for COVID-related MIS in children\u003c/p\u003e","description":"","filename":"Supplementarycovidmisc.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5759195/v1/aa8cdc16dd33da1604f3012b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSystem biology based integrative analysis to predict early biomarkers for COVID-related MIS in children\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Lovely Professional University","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":"COVID-19, MIS-C, Biomarkers, DEGs, Random Forest, Accuracy, Molecular Docking, In silico analysis","lastPublishedDoi":"10.21203/rs.3.rs-5759195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5759195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe outbreak of COVID-19 caused by SARS-CoV-2 has introduced unparalleled difficulties, including the appearance of a rare but serious condition in children referred to as Multisystem Inflammatory Syndrome in Children (MIS-C). 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