Identification of amino acid metabolism-based molecular subtypes and prognostic signature to predict immune landscape and guide clinical drug treatment in osteosarcoma

Identification of amino acid metabolism-based molecular subtypes and prognostic signature to predict immune landscape and guide clinical drug treatment in osteosarcoma | 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 Identification of amino acid metabolism-based molecular subtypes and prognostic signature to predict immune landscape and guide clinical drug treatment in osteosarcoma Jingfang Xu, Guannan Bai, Lin Zhang, Manli Zhao, Shiqiang Shang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7107108/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: Osteosarcoma (OS) is the most common bone malignancy in children and adolescents. The 5-year survival rate is only approximately 20% in patients with metastatic and recurrent OS. There is a critical necessity to ascertain prognostic markers that can predict the survival outcomes of OS patients, as well as to explore new therapeutic targets for the treatment of these patients. Method: In this study, the RNA sequencing profile of osteosarcoma patients and a total of 101 genes relevant to amino acid metabolism were subjected to analyze. Cox analysis and the NMF algorithm were utilized to identified clusters based on genes involved in amino acid metabolism. Then, we performed LASSO regression analysis based on these genes to construct a prognostic signature and a nomogram to predict the prognosis of OS patients. Additionally, tumor microenvironment, tumor mutation burden, tumor immunity responses and drug sensitivity were analyzed by R packages. The expression of these genes in osteosarcoma and normal tissues was determined by immunohistochemistry assays. Results: Four clusters of OS were identified based on genes related to amino acid metabolism. Cluster 2 exhibited the poorest outcomes, may better benefit from immunotherapy, and exhibits specific sensitivity differences to conventional drugs. Therefore, we developed a classifier score calculator based on the amino acid metabolism-related genes. A prognostic signature containing three risk genes and five protective genes and a nomogram were developed to predict the prognosis of OS patients, which had excellent predictive power. Immunohistochemistry assays illustrated that ATF4 and DIO1 were highly expressed in OS tissues while SLC7A7 was suppressed in OS tissues. Conclusion: In conclusion, multiple databases and analytical techniques were utilized to develop a robust eight-gene signature model for predicting OS outcomes. A distinct cluster of OS patients was identified based on genes related to amino acid metabolism, suggesting potential benefits from immunotherapy and targeted drug therapies. This model has the potential to enhance clinical decision-making for the treatment of OS patients. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology osteosarcoma amino acid metabolism prognosis tumor microenvironment immunity drug sensitivity Full Text Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx 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. 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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-7107108","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":555342080,"identity":"dbfa98d7-722d-4179-a578-ca3fa6ea5f2f","order_by":0,"name":"Jingfang Xu","email":"","orcid":"","institution":"Zhejiang University School of Medicine, National Children's Regional Medical Center, National Clinical Research Center for Child Health","correspondingAuthor":false,"prefix":"","firstName":"Jingfang","middleName":"","lastName":"Xu","suffix":""},{"id":555342082,"identity":"ea5ffdc7-dbbb-42f3-b769-444dfe872be1","order_by":1,"name":"Guannan 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osteosarcoma","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"osteosarcoma, amino acid metabolism, prognosis, tumor microenvironment, immunity, drug sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-7107108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7107108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eOsteosarcoma (OS) is the most common bone malignancy in children and adolescents. The 5-year survival rate is only approximately 20% in patients with metastatic and recurrent OS. There is a critical necessity to ascertain prognostic markers that can predict the survival outcomes of OS patients, as well as to explore new therapeutic targets for the treatment of these patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eIn this study, the RNA sequencing profile of osteosarcoma patients and a total of 101 genes relevant to amino acid metabolism were subjected to analyze. Cox analysis and the NMF algorithm were utilized to identified clusters based on genes involved in amino acid metabolism. Then, we performed LASSO regression analysis based on these genes to construct a prognostic signature and a nomogram to predict the prognosis of OS patients. Additionally, tumor microenvironment, tumor mutation burden, tumor immunity responses and drug sensitivity were analyzed by R packages. 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