Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms.

ABSTRACT:

Purpose

The aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci.

Methods

To do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs i

SUBMITTER: Liu YJ 

PROVIDER: S-EPMC10999885 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature