Using Bioinformatics and Machine Learning to Predict the Genetic Characteristics of Ferroptosis-Cuproptosis-Related Genes Associated with Sleep Deprivation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39347483", "kind": "science-record", "revision": 3, "content_hash": "sha256:906094574b5708f37736ffaf62c4d4dfa6029d18f53881e21a6fd868ae14f5e4", "created_at": "2026-11-01T20:29:33Z", "updated_at": "2026-11-01T22:12:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Using Bioinformatics and Machine Learning to Predict the Genetic Characteristics of Ferroptosis-Cuproptosis-Related Genes Associated with Sleep Deprivation.", "doi": "10.2147/NSS.S473022", "pmid": "39347483", "authors": ["Liang Wang", "Shuo Wang", "Chujiao Tian", "Tao Zou", "Yunshan Zhao", "Shaodan Li", "Minghui Yang", "Ningli Chai"], "journal": "Nature and science of sleep", "year": 2024, "abstract": "Purpose: Sleep deprivation (SD), a common sleep disease in clinic, has certain risks, and its pathogenesis is still unclear. This study aimed to identify ferroptosis-cuproptosis-related genes (FCRGs) associated with SD through bioinformatics and machine learning, thus elucidating their biological significance and clinical value.\nMethods: SD-DEGs were obtained from GEO. We intersected key WGCNA module genes of DE-FCRGs with SD-DEGs to obtain SD-DE-FCRGs. GO and KEGG analyses were performed. Machine learning was used to screen SD-DE-FCRGs, and filtered genes were intersected to obtain SD characteristic genes. ROC curves were used to evaluate the accuracy of SD characteristic genes. CIBERSORT was used to analyze the correlation between SD-DE-FCRGs and immune cells. We constructed a ceRNA network of SD-DE-FCRGs and used DGIbd to predict gene drug targets.\nResults: The 156 DEGs were identified from GSE98566. Five SD-DE-FCRGs from DE- FCRGs and SD-DEGs were analyzed via WGCNA, and enrichment analysis involved mainly ribosome regulation, mitochondrial pathways, and neurodegenerative diseases. Machine learning was used to obtain Four SD-DE-FCRGs (IKZF1, JCHAIN, MGST3, and UQCR11), and these gene analyses accurately evaluated the distribution model (AUC=0.793). Immune infiltration revealed that SD hub genes were correlated with most immune cells. Unsupervised cluster analysis revealed significant differential expression of immune-related genes between two subtypes. GSVA and GSEA revealed that enriched biological functions included oxidative phosphorylation, ribonucleic acid, metabolic diseases, activation of oxidative phosphorylation, and other pathways. Four SD-DE-FCRGs associated with 29 miRNAs were identified via the construction of a ceRNA network. The important target lenalidomide of IKZF1 was predicted.\nConclusion: We first used bioinformatics and machine learning to screen four SD-DE-FCRGs. These genes may affect the involvement of infiltrating immune cells in pathogenesis of SD by regulating FCRGs. We predicted that lenalidomide may target IKZF1 from SD-DE-FCRGs.", "claims": [{"id": "claim-data-access", "statement": "Both the SD-related database and the aggregated analysis data in this study are available for public download by qualified researchers. The SD dataset GSE98566 (annotation platform: GPL6244) was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/GEO/). The FRGs were obtained from the FerrDb database (http://www.zhounan.org/ferrdb/). Data from the current study can be obtained from the corresponding author (Shaodan Li, Email: lsd301@126.com) upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39347483", "locator": "sections[27] \"Data Share Statement\" paragraph 2", "quote": "Both the SD-related database and the aggregated analysis data in this study are available for public download by qualified researchers. The SD dataset GSE98566 (annotation platform: GPL6244) was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/GEO/). The FRGs were obtained from the FerrDb database (http://www.zhounan.org/ferrdb/). Data from the current study can be obtained from the corresponding author (Shaodan Li, Email: lsd301@126.com) upon reasonable request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "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:39347483"], "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:GSE98566"], "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:group_comparison_wilcoxon", "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:GSE98566"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39347483", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/NSS.S473022", "sha256": "sha256:a92817fc3ce6d67d3c6cf965f4b1ead77aeec643b0e5c563f7069f2116981646", "bytes": 48129, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/NSS.S473022", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11438466", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE98566", "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=GSE98566", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE98566", "version": null, "note": "Both the SD-related database and the aggregated analysis data in this study are available for public download by qualified researchers. The SD dataset GSE98566 (annotation platform: GPL6244) was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/GEO/). The FRGs were obtained from the FerrDb database (http://www.zhounan.org/ferrdb/). Data from the current study can be obtained from the corresponding author (Shaodan Li, Email: lsd301@126.com) upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Both the SD-related database and the aggregated analysis data in this study are available for public download by qualified researchers. The SD dataset GSE98566 (annotation platform: GPL6244) was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/GEO/). The FRGs were obtained from the FerrDb database (http://www.zhounan.org/ferrdb/). 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