Integrative Transcriptomic Analysis and Interpretable Machine Learning Uncover a Core Biomarker Signature and Its Immu-noregulatory Network Mediating the Sodium Arsenite-MASLD Axis

Integrative Transcriptomic Analysis and Interpretable Machine Learning Uncover a Core Biomarker Signature and Its Immu-noregulatory Network Mediating the Sodium Arsenite-MASLD Axis | 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 Integrative Transcriptomic Analysis and Interpretable Machine Learning Uncover a Core Biomarker Signature and Its Immu-noregulatory Network Mediating the Sodium Arsenite-MASLD Axis Zefeng Zhang, Oleksandr Andriiovych Boiko This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7692993/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Sodium arsenite, a ubiquitous environmental hepatotoxin, has an ill-defined role in the pathophysiology of metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to systematically identify key molecular targets linking sodium arsenite exposure to MASLD and to elucidate their potential mechanisms in regulating the liver immune microenvironment. Leveraging the GSE135251 transcriptomic dataset, we employed a dual-strategy approach combining Weighted Gene Co-expression Network Analysis (WGCNA) and Differential Expression Analysis (DEA) to identify high-confidence genes associated with MASLD. An intersection with network toxicology databases yielded 283 core candidate genes. To distill the most predictive biomarkers, we deployed an ensemble machine learning pipeline featuring LASSO, RF-RFE, and Random Forest. We identified a robust 11-gene biomarker signature, which was significantly dysregulated in MASLD patients and formed a highly synergistic internal regulatory network. Immune infiltration analysis demonstrated a significant increase in both M1 and M2 macrophages in MASLD livers. Crucially, the expression of the 11 core genes strongly correlated with macrophage subset abundances (e.g., DUSP1 was strongly negatively correlated with M1 macrophages, r=-0.70). Functional enrichment highlighted inflammatory pathways, including TNF and IL-17 signaling. Finally, drug repositioning identified 14 approved drugs targeting FOS, KDM4A, and DUSP1. Through an innovative, multi-layered analytical framework, this study successfully identified a sodium arsenite-associated 11-gene signature for MASLD, providing profound insights into its immunomodulatory role and presenting novel therapeutic targets. metabolic dysfunction-associated steatotic liver disease (MASLD) sodium arsenite machine learning biomarker immune microenvironment macrophage polarization network toxicology transcriptomics drug repositioning Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 Oct, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 24 Sep, 2025 First submitted to journal 23 Sep, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7692993","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522406034,"identity":"1d93319a-8f92-41ac-b3be-2d14b569dcc3","order_by":0,"name":"Zefeng Zhang","email":"","orcid":"","institution":"Jiangnan University Wuxi Medical College: Jiangnan University Wuxi School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zefeng","middleName":"","lastName":"Zhang","suffix":""},{"id":522406035,"identity":"9e222197-da97-497f-aa8e-b799a8ce74b6","order_by":1,"name":"Oleksandr Andriiovych 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