[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127387-en":3,"doc-seo-127387-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127387,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Decoding the hypoxia-exosomeimmune triad in OSA - PRCP/UCHL1/BTG2-driven metabolic dysregulation revealed by interpretable machine learning","Obstructive sleep apnea (OSA) involves metabolic and immune dysregulation, yet exosome-related biomarkers linking hypoxia signaling to immune-metabolic disturbance remain insufficiently clarified. This study integrates transcriptomic profiles from two GEO cohorts and uses limma-based differential expression, feature selection with LASSO and Random Forest, and interpretable XGBoost modeling to identify diagnostic candidates. Immune infiltration is quantified via ssGSEA, and therapeutic hypotheses are supported by DSigDB drug enrichment. PRCP, UCHL1 and BTG2 emerge as a central exosome-immune axis, with ROC AUC reaching 0.968.","TYPE Original Research PUBLISHED 27 October 2025  \nDOI 10.3389/fimmu.2025.1587522  \nOPEN ACCESS  \nEDITED BY  \nYejun Tan,  \nHong Kong Polytechnic University,  \nHong Kong SAR, China  \nREVIEWED BY  \nJinwei Li,  \nSichuan University, China Zoya Serebrovska,  \nInstitute of Gerontology (NAN Ukraine), Ukraine  \n*CORRESPONDENCE  \nRiken Chen  \n [chenriken@126.com](chenriken@126.com)[ ](chenriken@126.com)Weimin Yao  \n[490296443@qq.com](490296443@qq.com)  \n†These authors have contributed equally to this work  \nRECEIVED 05 March 2025  \nACCEPTED 06 October 2025  \nPUBLISHED 27 October 2025  \nCITATION  \nYe W, Yang Y, Chen F, Lin X, Wang Y, Du L, Pan J, Liao W, Chen B, Chen R and Yao W (2025) Decoding the hypoxia-exosomeimmune triad in OSA: PRCP/UCHL1/BTG2-driven metabolic dysregulation revealed by interpretable machine learning.  \nFront. Immunol. 16:1587522 .  \ndoi: 10.3389/fimmu.2025.1587522  \nCOPYRIGHT  \n© 2025 Ye, Yang, Chen, Lin, Wang, Du, Pan, Liao, Chen, Chen and Yao. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDecoding the hypoxia-exosomeimmune triad in OSA: PRCP/ UCHL1/BTG2-driven metabolic dysregulation revealed by interpretable machine learning  \nWeilong Ye †, Yitian Yang †, Feiju Chen †, Xiaoxi Lin, Yunan Wang, Lianfang Du, Jingjing Pan, Weifeng Liao, Bainian Chen,  \nRiken Chen* and Weimin Yao*  \nThe Second Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China  \nBackground: Obstructive sleep apnea (OSA) is a prevalent disorder characterized by significant metabolic and immune dysregulation. This study aims to uncover exosome-related biomarkers implicated in immune-metabolic disturbances in OSA and explore their potential as diagnostic and therapeutic targets.  \nMethods: Transcriptomic data from two GEO datasets (GSE135917 and GSE38792) were integrated and analyzed using differential expression analysis via the limma package. Key biomarkers were identified using feature selection techniques including LASSO and Random Forest. Machine learning models, specifically XGBoost, were trained to evaluate biomarker performance, with model accuracy assessed by ROC curve analysis and AUC values. Immune cell infiltration was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA) . Drug enrichment predictions were made through the Drug Signatures Database (DSigDB) . Vivo and Vitro Experimental Validation on Multiple Independent cohorts. Results: Three exosome-related biomarkers—PRCP, UCHL1, and BTG2—were identified as central to OSA ’ s immune-metabolic dysregulation . XGBoost modeling demonstrated robust predictive power (AUC = 0 . 968) . Immune analysis revealed significant correlations between gene expression and immune cell subsets, particularly CD56 bright natural killer cells and Memory B cells. Drug enrichment analysis identified potential therapeutic compounds, including Pentaphenate and Delphinidin, which target these biomarkers. OSA is associated with a reproducible transcriptional signature characterized by increased PRCP and UCHL1 expression and decreased BTG2 expression. Conclusions: This study identifies PRCP, UCHL1, and BTG2 as key exosomerelated biomarkers in OSA that regulate immune-metabolic disruption. By integrating transcriptomic data, machine learning, and immune analysis, we uncover an “exosome-immune” axis in OSA pathophysiology.  \nKEYWORDS  \nexosome signaling, obstructive sleep apnea (OSA), immune infiltration, machine learning, biomarkers  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nYe et al. 10.3389/fimmu.2025.1587522  \nGRAPHICAL ABSTRACT  \n1 Introduction  \nAn estimated ","cbCaiu0swxAqN7XJ","https://ap.wps.com/l/cbCaiu0swxAqN7XJ","pdf",9360308,1,14,"English","en",105,"# Background\n# Methods\n## Data integration and biomarker selection\n## Machine learning and performance evaluation\n## Immune infiltration and drug enrichment\n## Experimental validation\n# Results\n## Identification of PRCP, UCHL1, BTG2\n## Immune-cell correlations\n## Predicted therapeutic compounds\n# Conclusions\n# Key biomarkers and exosome-immune axis","[{\"question\":\"What biomarkers does the study identify for OSA immune-metabolic dysregulation?\",\"answer\":\"PRCP, UCHL1, and BTG2 are identified as central exosome-related biomarkers linked to immune-metabolic disruption in OSA.\"},{\"question\":\"How is the predictive performance of the biomarker panel evaluated?\",\"answer\":\"An XGBoost model is trained and assessed using ROC curves and AUC values, reporting AUC = 0.968.\"},{\"question\":\"What immune cells are most associated with the identified genes?\",\"answer\":\"Immune analysis shows significant correlations between gene expression and immune cell subsets, especially CD56 bright natural killer cells and Memory B cells.\"}]","Decoding the hypoxia-exosomeimmune triad in OSA - 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