Identification of noval diagnostic biomarker for HFpEF based on proteomics and machine learning.

ABSTRACT:

Background

Heart failure with preserved ejection fraction (HFpEF) is a complex syndrome that currently lacks effective biomarkers for early diagnosis and treatment. This study seeks to identify new potential biomarkers for HFpEF using proteomics and machine learning.

Methods

Plasma samples were collected from 20 patients newly diagnosed age, sex, BMI matched HFpEF and 20 healthy controls (HCs). Proteomic analysis was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-independent acquisition mode. Differentially expressed proteins (DEPs) were identified and analyzed through enrichment analyses and protein-protein interaction (PPI) network construction. Machine learning methods, including LASSO regression and the Boruta algorithm were used to sel

SUBMITTER: Abudurexiti M 

PROVIDER: S-EPMC11980230 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature