Identification of Risk Genes Associated with Myocardial Infarction-Big Data Analysis and Literature Review

Area of Science:

• Cardiovascular Genetics
• Genomics
• Biomedical Research

Background:

• Acute myocardial infarction (AMI) remains a leading global cause of death, with a significant annual mortality rate.
• Genetic predisposition, indicated by family history, is a potent risk factor for cardiovascular disease.
• Identifying genetic risk factors for AMI is crucial for understanding disease mechanisms and developing targeted interventions.

Purpose of the Study:

• To compile and analyze existing literature on genes associated with acute myocardial infarction (AMI).
• To identify specific genes that confer an increased risk for developing AMI through big data analysis.

Main Methods:

• Conducted a big data analysis using keywords "myocardial infarction", "genes", "involvement", "association", and "risk" across PubMed, Scopus, and Web of Science.
• Exported data from titles, abstracts, and keywords into an Excel spreadsheet for analysis.
• Utilized VOSviewer v. 1.6.18 software for data visualization and analysis of gene associations.

Main Results:

• Identified 28 genes significantly associated with an increased risk for AMI.
• Highlighted key genes such as lymphotoxin-a (LTA), LGALS2, LDLR, and APOA5 as particularly important risk factors.
• Correlated findings from big data analysis with existing review data on AMI genetic associations.

Conclusions:

• A deeper understanding of the functional genomic circuits underlying AMI is essential for future research.
• The identified genes provide potential targets for novel diagnostic and therapeutic strategies for AMI.
• This comprehensive analysis contributes to the knowledge base of genetic factors influencing myocardial infarction risk.