Identification of biomarkers and immune microenvironment associated with heart failure through bioinformatics and machine learning.

ABSTRACT:

Background

Heart failure (HF) is the end stage of various cardiovascular diseases. Identifying new biomarkers is essential for early diagnosis, prognosis, and treatment. This study applied bioinformatics to identify potential HF biomarkers and explore the role of the immune microenvironment.

Methods

Gene expression data were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify key genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis were performed. Feature genes were further determined using two machine learning algorithms, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO), wit

SUBMITTER: Jin J 

PROVIDER: S-EPMC12095026 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature