Comprehensive bioinformatics analytics and in vivo validation reveal SLC31A1 as an emerging diagnostic biomarker for...

Area of Science:

• Cardiovascular Medicine
• Cell Death Mechanisms
• Computational Biology

Background:

• Acute Myocardial Infarction (AMI) is a major cause of heart failure and mortality.
• Mitochondrial energy production is implicated in heart disease development.
• Cuproptosis, a novel cell death pathway, lacks comprehensive cardiovascular analysis.

Purpose of the Study:

• To develop a diagnostic model for AMI using Cuproptosis-related genes.
• To identify key Cuproptosis-related genes contributing to AMI diagnosis.
• To investigate the immunological implications of key genes in AMI.

Main Methods:

• Integrated 8 transcriptome profiles from the GEO database.
• Developed a diagnostic model using the Stacking algorithm.
• Validated key gene expression via qPCR and immunohistochemistry in animal models.

Main Results:

• A machine learning model incorporating PDHB, CDKN2A, GLS, and SLC31A1 for AMI diagnosis was created.
• SLC31A1 was identified as the key contributor gene, with validated overexpression in AMI.
• High SLC31A1 expression correlated with immune cell infiltration, particularly monocytes.

Conclusions:

• An AMI diagnostic model based on Cuproptosis-related genes was successfully constructed.
• The key contributor gene, SLC31A1, was validated in vivo.
• Overexpression of SLC31A1 in AMI has significant immunological implications.