Bioinformatics Analysis of the Cuprotosis Gene in Immune Infiltration of Chronic Kidney Disease

Bioinformatics Analysis of the Cuprotosis Gene in Immune Infiltration of Chronic Kidney 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 Bioinformatics Analysis of the Cuprotosis Gene in Immune Infiltration of Chronic Kidney Disease Yu Liu, Mengfan Yang, Youqun Huang, Naijing Ye, Caibin Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4486263/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： Chronic kidney disease is currently a major public health challenge worldwide, and modeling based on gene expression profiling is essential to guide individualized treatment of the disease. According to recent studies, cuprotosis, one of the forms of death of cells, appears to contribute to the progression of various diseases. Therefore, the present study aims to explore clusters associated with cuprotosis genes in chronic kidney disease, delve into immune infiltration, and construct predictive models. Methods： The GSE37171 (GPL570) dataset was downloaded from the Gene Expression Omnibus for analyzing expression profiling and immune characterization of cuprotosis regulators in CKD. Samples were classified into different clusters based on cuprotosis-related genes (CRGs) of kidney disease. Differential expression pathways and biological functions among clusters were identified through gene set variation analysis. The weighted gene co-expression network analysis algorithm was adopted to identify specific differentially expressed genes of clusters. A machine learning model was built to construct and validate nomogram risk prediction maps. Results： A total of seven cuprotosis-related genes are differential genes between chronic kidney disease and control group, with differences in immune infiltration between the two groups. Two different clusters are identified based on the expression profiles of the cuprotosis-related genes. And according to the differences in immune infiltration, it is hypothesized that the prognosis of Cluster 2 may be worse. Cluster 1 may be associated with cellular lipid anabolism, fibrosis, signal reception, inflammation, and other processes, while Cluster 2 is more closely related to DNA replication and binding, cellular protein synthesis and transport, peroxisome, etc. The predictive performance of the four selected machine learning classifiers is compared and a prediction model is developed, which provides the highest predictive validity in the test cohort (AUC = 0.992), indicating satisfactory performance. The model is verified to exhibit good predictive efficacy. Conclusion： The study systematically illustrates the complex relationship between cuprotosis and chronic kidney disease and develops a promising predictive model to assess cuprotosis subtypes in patients with the disease, revealing the underlying molecular mechanisms that lead to its Chronic Kidney Disease Cuprotosis Immune Infiltration Bioinformatics machine learning Full Text Additional Declarations No competing interests reported. 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-4486263","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311106639,"identity":"9f0dc215-5f52-4c24-a303-a5afaf485caa","order_by":0,"name":"Yu Liu","email":"","orcid":"","institution":"South China Hospital of Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Liu","suffix":""},{"id":311106640,"identity":"b971e4ad-19ee-489a-9927-673fcf98f3f7","order_by":1,"name":"Mengfan Yang","email":"","orcid":"","institution":"Hospital of Chengdu University of Traditional Chinese 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According to recent studies, cuprotosis, one of the forms of death of cells, appears to contribute to the progression of various diseases. Therefore, the present study aims to explore clusters associated with cuprotosis genes in chronic kidney disease, delve into immune infiltration, and construct predictive models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods：\u003c/strong\u003eThe GSE37171 (GPL570) dataset was downloaded from the Gene Expression Omnibus for analyzing expression profiling and immune characterization of cuprotosis regulators in CKD. Samples were classified into different clusters based on cuprotosis-related genes (CRGs) of kidney disease. Differential expression pathways and biological functions among clusters were identified through gene set variation analysis. The weighted gene co-expression network analysis algorithm was adopted to identify specific differentially expressed genes of clusters. 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