Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning.

ABSTRACT:

Background

The transformation of smooth muscle cells (SMCs) into alternative phenotypes is a key process in atherosclerosis pathogenesis. Recent studies have revealed oncological parallels between atherosclerosis and cancer, such as DNA damage and oncogenic pathway activation in SMCs, but the precise molecular mechanisms remain poorly understood. This study integrates cancer gene sets using bioinformatics to identify key hub genes associated with atherosclerosis and explores their immune molecular mechanisms.

Methods

Datasets from the Gene Expression Omnibus (GEO) were analyzed to identify differentially expressed genes (DEGs) and module genes using Limma and WGCNA. Machine learning algorithms (SVM-RFE, LASSO regression, and random forest) were employed to identify cancer-re

SUBMITTER: Zhao L 

PROVIDER: S-EPMC12623321 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature