Identification and validation of feature genes of acute myocardial infarction based on ferroptosis-related genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41068967", "kind": "science-record", "revision": 4, "content_hash": "sha256:6ee4eb9e55b4f298429dfcd925b223ac6fcf5ee8205cacfcf949123b6edcda57", "created_at": "2026-11-01T20:31:07Z", "updated_at": "2026-11-01T22:57:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of feature genes of acute myocardial infarction based on ferroptosis-related genes.", "doi": "10.1186/s40001-025-03216-9", "pmid": "41068967", "authors": ["Yuting Zhou", "Yehong Liu", "Baida Xu", "Tianhui Jin", "Ting Ye", "Wentao Su", "Chengsi Li", "Tingting Kang", "Haoran Xie", "Gangjun Zong"], "journal": "European journal of medical research", "year": 2025, "abstract": "BACKGROUND: Acute myocardial infarction (AMI) represents one of the most severe complications of coronary artery disease (CAD) and is a leading cause of mortality from noncommunicable diseases globally. In recent years, with the deepening of research on ferroptosis, the role of this cell death mode accompanied by iron accumulation and lipid peroxidation in myocardial infarction has been increasingly confirmed, which eventually leads to cell oxidative death. However, prognosis mechanism, and participation degree of ferroptosis in the occurrence of AMI still need to be further explored.\nMETHODS: The gene expression data sets (GSE97320, GSE60993) of AMI patients and the control group were obtained from the Gene Expression Omnibus (GEO) data sets. Screening for ferroptosis-related differentially expressed genes (FDEGs). In addition, the biological functions of these FDEGs were analyzed by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) were used to further check FDEGs to construct the prognostication model. ROC curves were used to validate the model's accuracy. GSE48060 and GSE59867 were set as a validation set to verify the model. Finally, the peripheral blood of AMI and control samples was collected for qRT-PCR to examine the differential expression situation of FDEGs.\nRESULTS: The CYB5R1, TSC1, LAMP2, PARK7, and MGST1 were identified as the most characteristic FDEGs, and the diagnostic model composed of them has excellent diagnostic ability for AMI.\nCONCLUSIONS: The identification of FDEGs in AMI by machine learning is a reliable research method that can provide further ideas for the study of ferroptosis in AMI.", "claims": [{"id": "claim-method", "statement": "Lasso regression and SVM–RFE were employed for identifying hub genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41068967", "locator": "sections[7] \"Feature FDEGs were identified by LASSO regression and SVM–RFE and externally validated\" paragraph 2", "quote": "Lasso regression and SVM–RFE were employed for identifying hub genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analysed during the current study are available in the GEO database repository, https://www.ncbi.nlm.nih.gov/geo/ to datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41068967", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "The datasets generated and/or analysed during the current study are available in the GEO database repository, https://www.ncbi.nlm.nih.gov/geo/ to datasets."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The diagnostic model combining CYB5R1, TSC1, LAMP2, PARK7, MGST1, and POR achieved an AUC of 0.972 (95% CI 0.910–1.000) in the training cohort.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The ROC curve of the diagnostical model composed of these FDEGs revealed that there was an impressive AUC of 0.972 (95% CI 0.910–1.000) (Fig. 3F), indicating excellent diagnostic performance."}, "evidence": [{"asset": "source:pmid:41068967", "locator": "sections[15] \"Feature FDEGs were further screened by LASSO regression and SVM–RFE\" paragraph 2", "quote": "The ROC curve of the diagnostical model composed of these FDEGs revealed that there was an impressive AUC of 0.972 (95% CI 0.910–1.000) (Fig. 3F), indicating excellent diagnostic performance."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The diagnostic model combining CYB5R1, TSC1, LAMP2, PARK7, MGST1, and POR achieved an AUC of 0.972 (95% CI 0.910–1.000) in the training cohort.", "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:41068967"], "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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