Frontiers in immunology · 2025
Decoding the hypoxia-exosome-immune triad in OSA: PRCP/UCHL1/BTG2-driven metabolic dysregulation revealed by interpretable machine learning.
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Ye Weilong, Yang Yitian, Chen Feiju, Lin Xiaoxi, Wang Yunan, Du Lianfang, Pan Jingjing, Liao Weifeng, Chen Bainian, Chen Riken, Yao Weimin
Аннотация
BACKGROUND: 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.
METHODS: 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 exosome-related 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.
METHODS: 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 exosome-related 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.