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Cuproptosis of endothelial cells in a hypoxic environment: from molecular classification to PMAIP1-targeted intervention
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DOI：10.1186/s41065-026-00667-w.png)
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Abstract
En 中文
Background Acute myocardial infarction (AMI) is a critical and fatal cardiovascular condition. The role of cuproptosis as an emerging mechanism in the pathogenesis of AMI remains to be fully elucidated. Methods Patient data were acquired from the Gene Expression Omnibus database. Differential expression analysis was performed on cuproptosis-related genes (CRGs). LASSO regression was employed to identify key CRGs and develop a diagnostic classification model for AMI. Weighted gene co-expression network analysis was performed to investigate key modules associated with the disease and its molecular classification. Subsequently, machine learning techniques were used to identify critical genes within these modules. The diagnostic efficacy of these genes for AMI was assessed using receiver operating characteristic analysis. The functions of the identified key genes were ultimately validated at the cellular level. Results Six characteristic CRGs were selected via LASSO regression.The AMI diagnostic classification model based on these CRGs demonstrated superior performance. Patients were classified into two subtypes related to cuproptosis, revealing significant differences in enrichment pathways between these subtypes. SYTL3, SCML1, and PMAIP1 were identified as key genes for AMI, with area under the curves of 0.813, 0.795, and 0.873, respectively. Knocking down PMAIP1 expression reduced intracellular copper levels in hypoxia-induced HUVECs. Conclusions This study clarifies the role of cuproptosis in AMI and highlights the potential involvement of PMAIP1, providing a theoretical basis for further investigation into cuproptosis in AMI.
Keywords:
Acute myocardial infarction
Cuproptosis
Bioinformatics
Machine learning
PMAIP1
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