Unraveling the copper-death connection: Decoding COVID-19's immune landscape through advanced bioinformatics and machine learning approaches.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38468184", "kind": "science-record", "revision": 4, "content_hash": "sha256:6ab459d0e04d0c1733bbaf499e421b14779419e5b0d276b75eb3d33876a05cf6", "created_at": "2026-11-01T19:20:24Z", "updated_at": "2026-11-01T22:11:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Unraveling the copper-death connection: Decoding COVID-19's immune landscape through advanced bioinformatics and machine learning approaches.", "doi": "10.1080/21645515.2024.2310359", "pmid": "38468184", "authors": ["Qi Wang", "Zhenzhong Su", "Jing Zhang", "He Yan", "Jie Zhang"], "journal": "Human vaccines & immunotherapeutics", "year": 2024, "abstract": "This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1's pivotal role validated. FDX1's high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1's connection to immune pathways offers insights into COVID-19 mechanisms and therapy.", "claims": [{"id": "claim-data-access", "statement": "The data that supports the findings of this study are available on request from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38468184", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The data that supports the findings of this study are available on request from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Machine learning models selected six copper-death-related feature genes with high diagnostic value for COVID-19, with FDX1 showing the highest AUC among the six candidate genes.", "kind": "quantitative", "target": {"value": 0.848, "unit": "auc", "tolerance": null, "source": "Among the six candidate genes, FDX1 showed the highest area under the ROC curve (AUC = 0.848, Figure 8f)."}, "evidence": [{"asset": "source:pmid:38468184", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset."}, {"asset": "source:pmid:38468184", "locator": "sections[20] \"Machine learning models identify copper-death-related feature genes with high diagnostic value for COVID-19\" paragraph 3", "quote": "Among the six candidate genes, FDX1 showed the highest area under the ROC curve (AUC = 0.848, Figure 8f)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Machine learning models selected six copper-death-related feature genes with high diagnostic value for COVID-19, with FDX1 showing the highest AUC among the six candidate genes.", "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:38468184"], "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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