Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38443855", "kind": "science-record", "revision": 4, "content_hash": "sha256:3ab14a6d7406060fb133c27f4ed89a1c4d0903c7f05a0f629d5f6e49dc4d138b", "created_at": "2026-11-01T19:20:23Z", "updated_at": "2026-11-01T22:53:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis.", "doi": "10.1186/s12890-024-02921-1", "pmid": "38443855", "authors": ["Hong Luo", "Jisong Yan", "Rui Gong", "Dingyu Zhang", "Xia Zhou", "Xianguang Wang"], "journal": "BMC pulmonary medicine", "year": 2024, "abstract": "BACKGROUND: The prevalence of obstructive sleep apnea (OSA) was found to be higher in individuals following COVID-19 infection. However, the intricate mechanisms that underscore this concomitance remain partially elucidated. The aim of this study was to delve deeper into the molecular mechanisms that underpin this comorbidity.\nMETHODS: We acquired gene expression profiles for COVID-19 (GSE157103) and OSA (GSE75097) from the Gene Expression Omnibus (GEO) database. Upon identifying shared feature genes between OSA and COVID-19 utilizing LASSO, Random forest and Support vector machines algorithms, we advanced to functional annotation, analysis of protein-protein interaction networks, module construction, and identification of pivotal genes. Furthermore, we established regulatory networks encompassing transcription factor (TF)-gene and TF-miRNA interactions, and searched for promising drug targets. Subsequently, the expression levels of pivotal genes were validated through proteomics data from COVID-19 cases.\nRESULTS: Fourteen feature genes shared between OSA and COVID-19 were selected for further investigation. Through functional annotation, it was indicated that metabolic pathways play a role in the pathogenesis of both disorders. Subsequently, employing the cytoHubba plugin, ten hub genes were recognized, namely TP53, CCND1, MDM2, RB1, HIF1A, EP300, STAT3, CDK2, HSP90AA1, and PPARG. The finding of proteomics unveiled a substantial augmentation in the expression level of HSP90AA1 in COVID-19 patient samples, especially in severe conditions.\nCONCLUSIONS: Our investigation illuminate a mutual pathogenic mechanism that underlies both OSA and COVID-19, which may provide novel perspectives for future investigations into the underlying mechanisms.", "claims": [{"id": "claim-method", "statement": "Upon identifying shared feature genes between OSA and COVID-19 utilizing LASSO, Random forest and Support vector machines algorithms, we advanced to functional annotation, analysis of protein–protein interaction networks, module construction, and identification of pivotal genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38443855", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Upon identifying shared feature genes between OSA and COVID-19 utilizing LASSO, Random forest and Support vector machines algorithms, we advanced to functional annotation, analysis of protein–protein interaction networks, module construction, and identification of pivotal genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets (accession number: GSE157103 and GSE75097) generated and analysed during the current study are available in GEO (https://www.ncbi.nlm.nih.gov/geo), which are public functional genomics data repositories. In addition, we uploaded the data and scripts to Zenodo (https://zenodo.org/records/10591549).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38443855", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The datasets (accession number: GSE157103 and GSE75097) generated and analysed during the current study are available in GEO (https://www.ncbi.nlm.nih.gov/geo), which are public functional genomics data repositories. In addition, we uploaded the data and scripts to Zenodo (https://zenodo.org/records/10591549)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Fourteen feature genes shared between OSA and COVID-19 were selected for further investigation.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38443855", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Fourteen feature genes shared between OSA and COVID-19 were selected for further investigation."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Fourteen feature genes shared between OSA and COVID-19 were selected for further investigation.", "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:38443855"], "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:GSE157103"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE157103"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38443855", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12890-024-02921-1", "sha256": "sha256:8b509eab25b3834a1ba22d30f3d679eca8728db176de49fb6d1b313e5e26df11", "bytes": 43032, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12890-024-02921-1", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10913609", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE157103", "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=GSE157103", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE157103", "version": null, "note": "The datasets (accession number: GSE157103 and GSE75097) generated and analysed during the current study are available in GEO (https://www.ncbi.nlm.nih.gov/geo), which are public functional genomics data repositories. 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