Decoding the hypoxia-exosome-immune triad in OSA: PRCP/UCHL1/BTG2-driven metabolic dysregulation revealed by interpretable machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41221287", "kind": "science-record", "revision": 4, "content_hash": "sha256:9248ed0f9fce82da5222799006f0e00d28c6b3a5bfa6cf0c8796c98154dd7b59", "created_at": "2026-11-01T20:31:15Z", "updated_at": "2026-11-01T22:57:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Decoding the hypoxia-exosome-immune triad in OSA: PRCP/UCHL1/BTG2-driven metabolic dysregulation revealed by interpretable machine learning.", "doi": "10.3389/fimmu.2025.1587522", "pmid": "41221287", "authors": ["Weilong Ye", "Yitian Yang", "Feiju Chen", "Xiaoxi Lin", "Yunan Wang", "Lianfang Du", "Jingjing Pan", "Weifeng Liao", "Bainian Chen", "Riken Chen", "Weimin Yao"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "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.\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.\nResults: 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.\nConclusions: 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.", "claims": [{"id": "claim-method", "statement": "Transcriptomic data from two GEO datasets (GSE135917 and GSE38792) were integrated and analyzed using differential expression analysis via the limma package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41221287", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Transcriptomic data from two GEO datasets (GSE135917 and GSE38792) were integrated and analyzed using differential expression analysis via the limma package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41221287", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "245 differentially expressed genes were identified.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Utilizing thresholds of p-value < 0.05 and |log2FC| > 0.5, 245 differentially expressed genes were identified ( Figure 1C )."}, "evidence": [{"asset": "source:pmid:41221287", "locator": "sections[15] \"Data integration and differential expression analysis\" paragraph 2", "quote": "Utilizing thresholds of p-value < 0.05 and |log2FC| > 0.5, 245 differentially expressed genes were identified ( Figure 1C )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "245 differentially expressed genes were identified.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41221287"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE135917"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE135917"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41221287", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1587522", "sha256": "sha256:98ebb02c46052cdf2fc724ba8f1d3ca4a5524764fff6b8918e23d842753ebebe", "bytes": 41971, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1587522", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12597927", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE135917", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135917", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE135917", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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Cells and mice were exposed to intermittent hypoxia (IH/CIH) wit", "shown in Figures 7A–C (Differentiated 3T3-L1 murine adipocytes), PRCP and UCHL1 mRNA levels were", "ured by qRT-PCR. (A–C) Differentiated 3T3-L1 murine adipocytes (n = 3 per group). (D–F) SW872 hu"], "order_basis": "paper_text_order:2808", "id": "assay:1"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": [") and murine 3T3-L1 preadipocytes (n=6) were cultured under standard conditions, with 3T3-L1 cells", "man SW872 liposarcoma cells (n=6) and murine 3T3-L1 preadipocytes (n=6) were cultured under stan", "ere cultured under standard conditions, with 3T3-L1 cells induced to differentiate into mature a", "ol. As shown in Figures 7A–C (Differentiated 3T3-L1 murine adipocytes), PRCP and UCHL1 mRNA leve"], "order_basis": "paper_text_order:2815", "id": "assay:2"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["rformed in triplicate to ensure reliability. IHC was performed on FFPE iWAT sections using an", "le ΔCt values and reported as 2^−ΔΔCt, while IHC results were quantified as the percentage of", "L1 expression and decreased BTG2 expression. IHC: Representative micrographs ( Figures 7J a–f", "test on ΔCt values. (J, a, b) Representative IHC images of PRCP, (J, c, d) UCHL1, (J, e, f) B"], "order_basis": "paper_text_order:3651", "id": "assay:3"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["on levels. 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