Comprehensive analysis of cuproptosis-related genes in immune infiltration in ischemic stroke.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36818726", "kind": "science-record", "revision": 4, "content_hash": "sha256:e447b32162493b35ede18d1a209c13b6ee653dc8e9d2e2bef19097329b793e36", "created_at": "2026-11-01T19:01:25Z", "updated_at": "2026-11-01T22:51:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of cuproptosis-related genes in immune infiltration in ischemic stroke.", "doi": "10.3389/fneur.2022.1077178", "pmid": "36818726", "authors": ["Xuehui Fan", "Hongping Chen", "Fangchao Jiang", "Chen Xu", "Yingju Wang", "Haining Wang", "Meng Li", "Wan Wei", "Jihe Song", "Di Zhong", "Guozhong Li"], "journal": "Frontiers in neurology", "year": 2022, "abstract": "Background: Immune infiltration plays an important role in the course of ischemic stroke (IS) progression. Cuproptosis is a newly discovered form of programmed cell death. To date, no studies on the mechanisms by which cuproptosis-related genes regulate immune infiltration in IS have been reported.\nMethods: IS-related microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database and standardized. Immune infiltration was extracted and quantified based on the processed gene expression matrix. The differences between the IS group and the normal group as well as the correlation between the infiltrating immune cells and their functions were analyzed. The cuproptosis-related DEGs most related to immunity were screened out, and the risk model was constructed. Finally, Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and drug target were performed using the Enrichr website database. miRNAs were predicted using FunRich software. Finally, cuproptosis-related differentially expressed genes (DEGs) in IS samples were typed, and Gene Set Variation Analysis (GSVA) was used to analyze the differences in biological functions among the different types.\nResults: Seven Cuproptosis-related DEGs were obtained by merging the GSE16561 and GSE37587 datasets. Correlation analysis of the immune cells showed that NLRP3, NFE2L2, ATP7A, LIPT1, GLS, and MTF1 were significantly correlated with immune cells. Subsequently, these six genes were included in the risk study, and the risk prediction model was constructed to calculate the total score to analyze the risk probability of the IS group. KEGG analysis showed that the genes were mainly enriched in the following two pathways: D-glutamine and D-glutamate metabolism; and lipids and atherosclerosis. Drug target prediction found that DMBA CTD 00007046 and Lithocholate TTD 00009000 were predicted to have potential therapeutic effects of candidate molecules. GSVA showed that the TGF-β signaling pathway and autophagy regulation pathways were upregulated in the subgroup with high expression of cuproptosis-related DEGs.\nConclusions: NLRP3, NFE2L2, ATP7A, LIPT1, GLS and MTF1 may serve as predictors of cuproptosis and play an important role in the pathogenesis of immune infiltration in IS.", "claims": [{"id": "claim-method", "statement": "Finally, the “Limma” package was applied to screen DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36818726", "locator": "sections[4] \"Data processing and identification of cuproptosis-related DEGs\" paragraph 2", "quote": "Finally, the “Limma” package was applied to screen DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36818726", "locator": "sections[15] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Seven Cuproptosis-related DEGs were obtained by merging the GSE16561 and GSE37587 datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36818726", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Seven Cuproptosis-related DEGs were obtained by merging the GSE16561 and GSE37587 datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Seven Cuproptosis-related DEGs were obtained by merging the GSE16561 and GSE37587 datasets.", "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:36818726"], "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:GSE16561"], "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:GSE16561"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36818726", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fneur.2022.1077178", "sha256": 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