{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["20"],"submitter":["Sun SW"],"pubmed_abstract":["<h4>Background</h4>This study aimed to identify differentially expressed oxidative stress-related genes (DEOSRGs) in ST-elevation MI (STEMI) patients and examine their connection to clinical outcomes.<h4>Methods</h4>We conducted a systematic review of Gene Expression Omnibus datasets, selecting GSE49925, GSE60993 and GSE61144 for analysis. DEOSRGs were identified using GEO2R2, overlapping across the selected datasets. Functional enrichment analysis was performed to understand the biological roles of the DEOSRGs. An optimal model was constructed using Least Absolute Shrinkage and Selection Operator penalised Cox proportional hazards regression. The clinical utility of the signature was assessed through survival analysis, receiver operating characteristic (ROC) curve and decision curve analy"],"journal":["European cardiology"],"pagination":["e11"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12060176"],"repository":["biostudies-literature"],"pubmed_title":["Oxidative Stress-related Gene Signature: A Prognostic Tool for Predicting Survival in ST-elevation MI."],"pmcid":["PMC12060176"],"pubmed_authors":["Hou M","Yan RC","Huang L","Sun SW","Wang XJ"],"additional_accession":[]},"is_claimable":false,"name":"Oxidative Stress-related Gene Signature: A Prognostic Tool for Predicting Survival in ST-elevation MI.","description":"<h4>Background</h4>This study aimed to identify differentially expressed oxidative stress-related genes (DEOSRGs) in ST-elevation MI (STEMI) patients and examine their connection to clinical outcomes.<h4>Methods</h4>We conducted a systematic review of Gene Expression Omnibus datasets, selecting GSE49925, GSE60993 and GSE61144 for analysis. DEOSRGs were identified using GEO2R2, overlapping across the selected datasets. Functional enrichment analysis was performed to understand the biological roles of the DEOSRGs. An optimal model was constructed using Least Absolute Shrinkage and Selection Operator penalised Cox proportional hazards regression. The clinical utility of the signature was assessed through survival analysis, receiver operating characteristic (ROC) curve and decision curve analy","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-06-01T19:18:02.887Z","creation":"2026-05-21T03:09:07.201Z"},"accession":"S-EPMC12060176","cross_references":{"pubmed":["40343143"],"doi":["10.15420/ecr.2024.58"]}}