Abstract:

Objective To screen biomarkers of acute myocardial ischemia （AMI） using bioinformatics approaches. Methods Datasets related to myocardial ischemia, including GSE415, GSE775, GSE4648, GSE19875 and GSE8322, were downloaded from the Gene Expression Omnibus （GEO） database. The data were divided into an AMI dataset （20 control samples and 5 experimental samples） and the myocardial ischemia dataset （16 control samples and 18 experimental samples）. Weighted gene co-expression network analysis （WGCNA）, least absolute shrinkage and selection operator （LASSO） regression, and receiver operating characteristic （ROC） curve analysis were applied to identify potential biomarkers. A mouse myocardial ischemia model, with myocardial ischemia induced for 15 min and 60 min, was established, and real-time quantitative PCR （qPCR） was utilized to verify whether the potential biomarkers could assist in the identification of AMI. Results The most accurate indicator for identifying AMI was inhibitor of nuclear factor-κB alpha （IκBα, AUC=0.91）. The accurate indicators for identifying myocardial ischemia were B cell translocation gene 2 （BTG2, AUC=0.78） and vesicle transport protein USE1 （AUC=0.77）. The validation results revealed that the expression levels of IκBα differed significantly among the control, myocardial ischemia and AMI groups （P<0.05）. BTG2 expression was the highest in the myocardial ischemia group （P<0.05）, whereas no statistically significant difference in USE1 expression was observed across the three groups （P>0.05）. Conclusion IκBα can distinguish AMI and myocardial ischemia groups, and BTG2 serves as an auxiliary biomarker for the diagnosis of myocardial ischemia.

Key words: forensic pathology, bioinformatics, acute myocardial ischemia, biomarker, inhibitor of nuclear factor-κB alpha （IκBα）, B cell translocation gene 2 （BTG2）, mice