Identification of Key Biomarkers and Potential Therapeutic Drugs in Nasopharyngeal Carcinoma Based on Comprehensive Bioinformatics Analysis

Identification of Key Biomarkers and Potential Therapeutic Drugs in Nasopharyngeal Carcinoma Based on Comprehensive Bioinformatics Analysis | 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 Identification of Key Biomarkers and Potential Therapeutic Drugs in Nasopharyngeal Carcinoma Based on Comprehensive Bioinformatics Analysis Cong Fu, Lili Zhang, Tong Zhou, Yanzhi Bi, Lin Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5781430/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Nasopharyngeal carcinoma (NPC) is the most prevalent type of head - and - neck cancer, and its diagnosis and treatment are currently facing significant challenges. This study aimed to identify biomarkers associated with NPC by performing bioinformatic analysis on the GSE12452, GSE53819, and GSE64634 datasets from the GEO database. First, differentially expressed genes (DEGs) between NPC and normal nasopharyngeal tissues were screened. Then, these DEGs were subjected to RobustRank Aggregation analysis. Through Receiver Operating Characteristic (ROC) analysis and three machine - learning models, biomarkers such as DNAH5, ZMYND10, LRRC6, ARMC4, DNAI2, and DNALI1 were identified. Enrichment analysis was performed to uncover the common pathways of these biomarkers. Using the Comparative Toxicogenomics Database (CTD), target drugs for NPC were predicted based on these biomarkers. Additionally, immune infiltration analysis was carried out to study the relationship between these biomarkers and immune cells. A regulatory network was also constructed. It was found that these biomarkers are mainly involved in cytokine - cytokine receptor interaction, and some are part of common cancer - related signaling pathways. In addition, quantitative real time polymerase chain reaction (qRT-PCR) results showed that the expression levels of all biomarkers were significantly elevated in normal cell samples. DNAH5 and ZMYND10 were significantly higher in normal surrounding tissues. These findings may offer valuable support for the early clinical diagnosis and treatment of NPC patients. NPC robustRank aggregation biomarker machine learning model diagnosis Full Text Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx Fig.S4.tif Fig.S6.tif Fig.S1.tif Fig.S5.tif Fig.S3.tif Fig.S2.tif Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 May, 2025 Reviews received at journal 13 Apr, 2025 Reviewers agreed at journal 12 Apr, 2025 Reviewers invited by journal 10 Apr, 2025 Editor assigned by journal 10 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 27 Mar, 2025 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-5781430","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441164245,"identity":"8106693b-02fa-4d40-b1c1-5883b79eac0a","order_by":0,"name":"Cong Fu","email":"","orcid":"","institution":"Changzhou Cancer (Fourth People's) Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Fu","suffix":""},{"id":441164246,"identity":"22f43570-0466-467c-866e-73d4c1bebc79","order_by":1,"name":"Lili Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Jiangsu 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