Identification of signature genes and subtypes for heart failure diagnosis based on machine learning.

ABSTRACT:

Background

Heart failure (HF) is a multifaceted clinical condition, and our comprehension of its genetic pathogenesis continues to be significantly limited. Consequently, identifying specific genes for HF at the transcriptomic level may enhance early detection and allow for more targeted therapies for these individuals.

Methods

HF datasets were acquired from the Gene Expression Omnibus (GEO) database (GSE57338), and through the application of bioinformatics and machine-learning algorithms. We identified four candidate genes (FCN3, MNS1, SMOC2, and FREM1) that may serve as potential diagnostics for HF. Furthermore, we validated the diagnostic value of these genes on additional GEO datasets (GSE21610 and GSE76701). In addition, we assessed the diffe

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12034685 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature