Integrative identification of immune-related key genes in atrial fibrillation using weighted gene coexpression network analysis and machine learning.

ABSTRACT:

Background

The immune system significantly participates in the pathologic process of atrial fibrillation (AF). However, the molecular mechanisms underlying this participation are not completely explained. The current research aimed to identify critical genes and immune cells that participate in the pathologic process of AF.

Methods

CIBERSORT was utilized to reveal the immune cell infiltration pattern in AF patients. Meanwhile, weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules that were significantly correlated with AF. The characteristic genes correlated with AF were identified by the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algo

SUBMITTER: Zheng PF 

PROVIDER: S-EPMC9363882 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature