Verification of cuproptosis-related diagnostic model associated with immune infiltration in rheumatoid arthritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37547319", "kind": "science-record", "revision": 5, "content_hash": "sha256:80d345de2b77c8e184798bb3dc09e42fea2bef336a5aa4ca2aa8e11bd7b45887", "created_at": "2026-11-01T19:02:19Z", "updated_at": "2026-11-01T22:52:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Verification of cuproptosis-related diagnostic model associated with immune infiltration in rheumatoid arthritis.", "doi": "10.3389/fendo.2023.1204926", "pmid": "37547319", "authors": ["Mingyang Jiang", "Kaicheng Liu", "Shenyi Lu", "Yue Qiu", "Xiaochong Zou", "Ke Zhang", "Chuanliang Chen", "Yiji Jike", "Mingjing Xie", "Yongheng Dai", "Zhandong Bo"], "journal": "Frontiers in endocrinology", "year": 2023, "abstract": "Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease closely related to inflammation. Cuproptosis is a newly discovered unique type of cell death, and it has been found that it may play an essential role in the occurrence and development of RA. Therefore, we intend to explore the potential association between cuproptosis-related genes (CRGs) and RA to provide a new biomarker for the treatment and prognosis of RA.\nMethods: Download GSE93777 datasets from the GEO database. Variance analysis was performed on the CRGs that had been reported. Then, the random forest (RF) model and nomogram of differentially expressed CRGs were constructed, and the ROC curve was used to evaluate the accuracy of the diagnostic model. Next, RA patients were subtyped by consensus clustering, and immune infiltration was analyzed in each subgroup to confirm the correlation between CRGs and abundance of immune cells. The expression levels of CRGs were verified by qRT-PCR.\nResults: Eight differentially expressed CRGs (DLST, DLD, PDHB, PDHA1, ATP7A, CDKN2A, LIAS, DLAT) were screened out by differential analysis to construct an RF model. The ROC curve proved that this model had good diagnostic accuracy. Based on the above eight significant CRGs, a nomogram was built to predict effective and high-precision results. The consensus clustering method identified two CRG patterns. Most of the immune cells were enriched in cluster A, indicating that cluster A may be related to the development of RA. Finally, qRT-PCR verified the expression of eight key genes, further confirming our findings.\nConclusion: The diagnosis model of RA based on the above eight CRGs has excellent diagnostic potential. Based on these, patients can be divided into two different molecular subtypes; it is expected to develop a new treatment strategy for RA.", "claims": [{"id": "claim-method", "statement": "Using the R “limma” package to examine differentially expressed cuproptosis-related genes (CRGs) between normal and RA samples, |logFC| value > 1 and P < 0.05 were considered statistically significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37547319", "locator": "sections[3] \"Data sources\" paragraph 2", "quote": "Using the R “limma” package to examine differentially expressed cuproptosis-related genes (CRGs) between normal and RA samples, |logFC| value > 1 and P < 0.05 were considered statistically significant."}], "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 author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37547319", "locator": "sections[23] \"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 author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The random forest model achieved an AUC of 1.000, compared with an AUC of 0.964 for the support vector machine model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37547319", "locator": "sections[15] \"Construction of the RF and SVM models\" paragraph 4", "quote": "The AUC of SVM is 0.964, The AUC of RF is 1.000;"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The random forest model achieved an AUC of 1.000, compared with an AUC of 0.964 for the support vector machine model.", "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:37547319"], "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:GSE93777"], "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:GSE93777"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37547319", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2023.1204926", "sha256": "sha256:fbb55621e11fbcfc1a3e6f599dd4c741b67ddf167895778c794352005a4025ef", "bytes": 31729, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2023.1204926", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10399571", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE93777", "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=GSE93777", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE93777", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary material. 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