Polygenic insight identifies precision biomarkers decoding protein catabolism and autophagy pathways in obstructive sleep apnea.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40759999", "kind": "science-record", "revision": 4, "content_hash": "sha256:6c15b2d2ae2e1a0afab324a8b898caf956d730bd9a3a705003c391a7fe6f0389", "created_at": "2026-11-01T20:30:51Z", "updated_at": "2026-11-01T22:56:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Polygenic insight identifies precision biomarkers decoding protein catabolism and autophagy pathways in obstructive sleep apnea.", "doi": "10.1038/s41598-025-13687-9", "pmid": "40759999", "authors": ["Xiaoying Ke", "Min Huang", "Yingying Zheng", "Guohao Chen"], "journal": "Scientific reports", "year": 2025, "abstract": "Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurrent upper airway obstructions, leading to substantial health burdens and socioeconomic costs. This study aimed to identify Hypoxia and Mitophagy-Related Differentially Expressed Genes (HMRDEGs) and evaluate their potential as biomarkers and therapeutic targets for OSA. Transcriptomic data from GSE135917 and GSE38792 in the GEO database were analyzed using the limma package to identify differentially expressed genes (DEGs), which were subsequently intersected with hypoxia- and mitophagy-related gene sets(HMRGs) curated from GeneCards and PubMed. A total of 24 HMRDEGs were identified, and four hub genes-NLRP3, MAPK9, RBBP4, and CLINT1-were used to construct a diagnostic model that demonstrated excellent discrimination (AUC = 0.982 in the training set and 0.812 in the validation set). Gene Ontology and KEGG analyses linked these genes to protein catabolism and autophagy pathways, while immune-cell infiltration profiling associated them with specific leukocyte subsets. Collectively, our findings underscore hypoxia-mitophagy crosstalk as a central mechanism in OSA and present a robust biomarker panel with therapeutic potential.", "claims": [{"id": "claim-method", "statement": "We initially assessed the diagnostic significance of 24 HMRDEGs in OSA using logistic regression analysis. The forest plot (Fig. 8A) illustrating this model indicated that all 24 genes were statistically significant (p < 0.05). These genes include RBBP4, UCHL1, MAPK9, CLINT1, GABARAP, CAV1, SQSTM1, FLT3, NLRP3, USP33, ATP6V1A, DUSP1, CDKN2A, ANXA5, MAP1LC3B, NR4A1, PPARGC1A, CLU, PLOD2, SLC2A3, GRN, TXN, DSP, MCL1. Subsequently, we constructed a Support Vector Machine model utilizing these 24 HMRDEGs combined with the SVM algorithm. Analysis of the number of genes associated with the lowest error rate (Fig. 8B) and the highest accuracy rate (Fig. 8C) revealed that the model performed optimally when using 10 genes.To optimize diagnostic efficacy, we employed LASSO regression analysis to further screen the aforementioned 10 genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40759999", "locator": "sections[25] \"Construction of diagnostic model for OSA\" paragraph 2", "quote": "We initially assessed the diagnostic significance of 24 HMRDEGs in OSA using logistic regression analysis. The forest plot (Fig. 8A) illustrating this model indicated that all 24 genes were statistically significant (p < 0.05). These genes include RBBP4, UCHL1, MAPK9, CLINT1, GABARAP, CAV1, SQSTM1, FLT3, NLRP3, USP33, ATP6V1A, DUSP1, CDKN2A, ANXA5, MAP1LC3B, NR4A1, PPARGC1A, CLU, PLOD2, SLC2A3, GRN, TXN, DSP, MCL1. Subsequently, we constructed a Support Vector Machine model utilizing these 24 HMRDEGs combined with the SVM algorithm. Analysis of the number of genes associated with the lowest error rate (Fig. 8B) and the highest accuracy rate (Fig. 8C) revealed that the model performed optimally when using 10 genes.To optimize diagnostic efficacy, we employed LASSO regression analysis to further screen the aforementioned 10 genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The authors state that the data underlying the findings of this study are provided in the article and the Supplementary Information. Bulk RNA-Seq data have been deposited in the Gene Expression Omnibus (GEO) repository under accession number GSE135917 and GSE38792. Additional data can be obtained from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40759999", "locator": "sections[39] \"Data availability\" paragraph 2", "quote": "The authors state that the data underlying the findings of this study are provided in the article and the Supplementary Information. Bulk RNA-Seq data have been deposited in the Gene Expression Omnibus (GEO) repository under accession number GSE135917 and GSE38792. Additional data can be obtained from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene diagnostic model incorporating NLRP3, MAPK9, RBBP4, and CLINT1 achieved excellent discrimination, with an AUC of 0.982 in the training set and 0.812 in an independent validation set.", "kind": "quantitative", "target": {"value": 0.982, "unit": "auc", "tolerance": null, "source": "A four-gene logistic model incorporating NLRP3, MAPK9, RBBP4, and CLINT1 achieved excellent discrimination, with an AUC of 0.982 in the training set and 0.812 in an independent validation set."}, "evidence": [{"asset": "source:pmid:40759999", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "A four-gene logistic model incorporating NLRP3, MAPK9, RBBP4, and CLINT1 achieved excellent discrimination, with an AUC of 0.982 in the training set and 0.812 in an independent validation set."}, {"asset": "source:pmid:40759999", "locator": "sections[25] \"Construction of diagnostic model for OSA\" paragraph 2", "quote": "Ultimately, four key genes – NLRP3, MAPK9, RBBP4, and CLINT1 – were incorporated into the LASSO regression model as model genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A four-gene diagnostic model incorporating NLRP3, MAPK9, RBBP4, and CLINT1 achieved excellent discrimination, with an AUC of 0.982 in the training set and 0.812 in an independent validation set.", "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:40759999"], "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. 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