Sex-Specific Gene Expression in MASLD: A Transcriptomic and Systems Biology Analysis Reveals Key Regulatory Modules and Immune Networks

Sex-Specific Gene Expression in MASLD: A Transcriptomic and Systems Biology Analysis Reveals Key Regulatory Modules and Immune Networks | 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 Sex-Specific Gene Expression in MASLD: A Transcriptomic and Systems Biology Analysis Reveals Key Regulatory Modules and Immune Networks Guanlin Wu, Xiaohan Zhang, Boxuan Li, Yalin Xi, Zhengwu Sun, Yangxia Fu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9063561/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 Metabolic dysfunction-associated steatotic liver disease (MASLD) is a complex metabolic disorder disease characterized by significant sex-related differences in its pathogenesis and progression. Elucidating the sex-specific molecular mechanisms of MASLD is crucial for identifying novel therapeutic targets. Methods We analyzed liver gene expression profiles from MASLD patients retrieved from the Gene Expression Omnibus (GEO) database (dataset GSE159088). Sex-stratified analysis was performed to identify differentially expressed genes (DEGs) between male and female patients. Functional annotation of these DEGs was conducted through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Further mechanistic insights were explored using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). To identify robust sex-associated gene modules in MASLD, we applied Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms. Additionally, immune infiltration patterns in MASLD were assessed using the single-sample GSEA (ssGSEA) algorithm, and their correlation with sex-specific gene expression was examined. Results We identified 31 sex-specific DEGs in MASLD liver tissues. Functional enrichment revealed these DEGs were significantly involved in metabolism-related pathways such as glycine, serine, threonine, cysteine, and methionine metabolism. GSVA and GSEA further confirmed their enrichment in core sexually dimorphic biological pathways. We constructed a sex-associated gene co-expression network by WGCNA and identified a key module (purple module) highly correlated with male sex. Machine learning algorithms, including Random Forest and Support Vector Machine (SVM), prioritized hub genes (e.g., FAM224A, CPE, ASCL1, HYDIN, TSPAN8, ESPL1) within this module. Integrative analysis further demonstrated that these sex-specific hub genes were significantly correlated with altered immune infiltration landscapes (particularly involving Natural Killer T cells, Type 1 T helper cells, and Activated CD8 + T cells) and associated with epigenetic regulatory processes such as chromatin remodeling and DNA methylation. Conclusion This study delineates a multi-dimensional sex-specific molecular signature in MASLD, providing insights into mechanisms of sexual dimorphism in disease progression and highlighting potential targets for precision, sex-tailored interventions. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics Biological sciences/Immunology MASLD sex-specific WGCNA machine learning immunology 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. 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-9063561","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":607438884,"identity":"7f0e7e57-fa47-4c7c-907c-704299265348","order_by":0,"name":"Guanlin Wu","email":"","orcid":"","institution":"Central Hospital of Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Guanlin","middleName":"","lastName":"Wu","suffix":""},{"id":607438889,"identity":"9fa5737b-e93b-4ac7-a6ba-5e0159790286","order_by":1,"name":"Xiaohan Zhang","email":"","orcid":"","institution":"Dalian Women and Children’s Medical 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immunology","lastPublishedDoi":"10.21203/rs.3.rs-9063561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9063561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is a complex metabolic disorder disease characterized by significant sex-related differences in its pathogenesis and progression. Elucidating the sex-specific molecular mechanisms of MASLD is crucial for identifying novel therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed liver gene expression profiles from MASLD patients retrieved from the Gene Expression Omnibus (GEO) database (dataset GSE159088). Sex-stratified analysis was performed to identify differentially expressed genes (DEGs) between male and female patients. Functional annotation of these DEGs was conducted through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Further mechanistic insights were explored using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA). To identify robust sex-associated gene modules in MASLD, we applied Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms. Additionally, immune infiltration patterns in MASLD were assessed using the single-sample GSEA (ssGSEA) algorithm, and their correlation with sex-specific gene expression was examined.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 31 sex-specific DEGs in MASLD liver tissues. Functional enrichment revealed these DEGs were significantly involved in metabolism-related pathways such as glycine, serine, threonine, cysteine, and methionine metabolism. GSVA and GSEA further confirmed their enrichment in core sexually dimorphic biological pathways. We constructed a sex-associated gene co-expression network by WGCNA and identified a key module (purple module) highly correlated with male sex. Machine learning algorithms, including Random Forest and Support Vector Machine (SVM), prioritized hub genes (e.g., FAM224A, CPE, ASCL1, HYDIN, TSPAN8, ESPL1) within this module. Integrative analysis further demonstrated that these sex-specific hub genes were significantly correlated with altered immune infiltration landscapes (particularly involving Natural Killer T cells, Type 1 T helper cells, and Activated CD8\u003csup\u003e+\u003c/sup\u003e T cells) and associated with epigenetic regulatory processes such as chromatin remodeling and DNA methylation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study delineates a multi-dimensional sex-specific molecular signature in MASLD, providing insights into mechanisms of sexual dimorphism in disease progression and highlighting potential targets for precision, sex-tailored interventions.\u003c/p\u003e","manuscriptTitle":"Sex-Specific Gene Expression in MASLD: A Transcriptomic and Systems Biology Analysis Reveals Key Regulatory Modules and Immune Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 11:33:04","doi":"10.21203/rs.3.rs-9063561/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"d21feece-014c-43fe-80c9-d8ce5be907bd","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64635892,"name":"Health sciences/Biomarkers"},{"id":64635894,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":64635896,"name":"Health sciences/Diseases"},{"id":64635897,"name":"Biological sciences/Genetics"},{"id":64635898,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2026-04-06T10:12:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 11:33:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9063561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9063561","identity":"rs-9063561","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}