Potential biomarkers and immune cell infiltration involved in aortic valve calcification identified through integrated bioinformatics analysis.

ABSTRACT: Background: Calcific aortic valve disease (CAVD) is the most common valvular heart disease in the aging population, resulting in a significant health and economic burden worldwide, but its underlying diagnostic biomarkers and pathophysiological mechanisms are not fully understood. Methods: Three publicly available gene expression profiles (GSE12644, GSE51472, and GSE77287) from human Calcific aortic valve disease (CAVD) and normal aortic valve samples were downloaded from the Gene Expression Omnibus database for combined analysis. R software was used to identify differentially expressed genes (DEGs) and conduct functional investigations. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimin

SUBMITTER: Lv X 

PROVIDER: S-EPMC9797982 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature