Identification of Three Cuproptosis-specific Expressed Genes as Diagnostic Biomarkers and Therapeutic Targets for Atherosclerosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37324184", "kind": "science-record", "revision": 3, "content_hash": "sha256:5ecb7cda43692ad321db87b5120c63fc15672219b8ce7c39a9edd4e3d380955c", "created_at": "2026-11-01T19:02:03Z", "updated_at": "2026-11-01T22:09:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Three Cuproptosis-specific Expressed Genes as Diagnostic Biomarkers and Therapeutic Targets for Atherosclerosis.", "doi": "10.7150/ijms.83009", "pmid": "37324184", "authors": ["Yong-Tong Chen", "Xuan-Hao Xu", "Ling Lin", "Shuai Tian", "Gui-Fu Wu"], "journal": "International journal of medical sciences", "year": 2023, "abstract": "Atherosclerosis is a chronic, inflammatory disease characterized by a lipid-driven infiltration of inflammatory cells in large and medium arteries and is considered to be a major underlying cause of cardiovascular diseases. Cuproptosis, a novel form of cell death, is highly linked to mitochondrial metabolism and mediated by protein lipoylation. However, the clinical implication of cuproptosis-related genes (CRGs) in atherosclerosis remains unclear. In this study, genes collected from the GEO database intersected with CRGs were identified in atherosclerosis. GSEA, GO and KEGG pathway enrichment analyses were performed for functional annotation. Through the random forest algorithm and the construction of a protein-protein interaction (PPI) network, eight selected genes (LOXL2, SLC31A1, ATP7A, SLC31A2, COA6, UBE2D1, CP and SOD1) and a vital cuproptosis-related gene FDX1 were then further validated. Two independent datasets (GSE28829 (N = 29), GSE100927 (N = 104)) were collected to construct the signature of CRGs for validation in atherosclerosis. Consistently, the atherosclerosis plaques showed significantly higher expression of SLC31A1, SLC31A2 and lower expression of SOD1 than the normal intimae. The area under the curve (AUC) of SLC31A1, SLC31A2 and SOD1 performed well for the diagnostic validation in the two datasets. In conclusion, the cuproptosis-related gene signature could serve as a potential diagnostic biomarker for atherosclerosis and may offer novel insights into the treatment of cardiovascular diseases. Based on the hub genes, a competing endogenous RNA (ceRNA) network of lncRNA-miRNA-mRNA and a transcription factor regulation network were ultimately constructed to explore the possible regulatory mechanism in atherosclerosis.", "claims": [{"id": "claim-method", "statement": "ROC single factor analyses were performed in different datasets to evaluate the diagnostic value of the three hub cuproptosis-related biomarkers in atherosclerosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37324184", "locator": "sections[18] \"The Diagnostic Value of the Three Cuproptosis-Related Biomarkers in Atherosclerosis\" paragraph 2", "quote": "ROC single factor analyses were performed in different datasets to evaluate the diagnostic value of the three hub cuproptosis-related biomarkers in atherosclerosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Training dataset GSE97210(LncRNA/mRNA: n=6, 3 advanced atherosclerotic plaques vs. 3 normal arterial intimae, all males) were collected from the GEO database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37324184", "locator": "sections[3] \"Collection of Datasets and CRGs\" paragraph 2", "quote": "Training dataset GSE97210(LncRNA/mRNA: n=6, 3 advanced atherosclerotic plaques vs. 3 normal arterial intimae, all males) were collected from the GEO database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC of these three cuproptosis-related biomarkers for atherosclerosis was 0.736, 0.838 and 0.848 respectively in the GSE100927 dataset, with SLC31A2 and SOD1 performing good diagnostic value (Figure 9B).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37324184", "locator": "sections[18] \"The Diagnostic Value of the Three Cuproptosis-Related Biomarkers in Atherosclerosis\" paragraph 2", "quote": "The AUC of these three cuproptosis-related biomarkers for atherosclerosis was 0.736, 0.838 and 0.848 respectively in the GSE100927 dataset, with SLC31A2 and SOD1 performing good diagnostic value (Figure 9B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The AUC of these three cuproptosis-related biomarkers for atherosclerosis was 0.736, 0.838 and 0.848 respectively in the GSE100927 dataset, with SLC31A2 and SOD1 performing good diagnostic value (Figure 9B).", "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:37324184"], "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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