Identification of hub genes and their correlation with immune infiltration in coronary artery disease through bioinformatics and machine learning methods.

ABSTRACT:

Background

Coronary artery disease (CAD) is a multifactorial disease and its pathogenesis remains unclear. We aimed to explore the optimal feature genes (OFGs) for CAD and to investigate the function of immune cell infiltration of CAD. It will be helpful for better understanding of the pathogenesis and the development of genetic prediction of CAD.

Methods

Datasets related to CAD were obtained from the Gene Expression Omnibus (GEO) database. Cases from the datasets met diagnostic criteria including clinical symptoms, electrocardiographic (ECG) and angiographic evidence. We identified differentially expressed genes (DEGs) and conducted functional enrichment analysis. OFGs were obtained from the least absolute shrinkage and selection operator (LASSO) algorithm, support vector m

SUBMITTER: Huang KK 

PROVIDER: S-EPMC9344404 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature