Identification of novel inflammatory response-related biomarkers in patients with ischemic stroke based on WGCNA and machine learning.

ABSTRACT:

Background

Ischemic stroke (IS) is one of the most common causes of disability in adults worldwide. This study aimed to identify key genes related to the inflammatory response to provide insights into the mechanisms and management of IS.

Methods

Transcriptomic data for IS were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were used to identify inflammation-related genes (IRGs) associated with IS. Hub IRGs were screened using Lasso, SVM-RFE, and random forest algorithms, and a nomogram diagnostic model was constructed. The diagnostic performance of the model was assessed using receiver operating characteristic (ROC) curves and calibration plots. Additionally, immune cell in

SUBMITTER: Huang C 

PROVIDER: S-EPMC11929209 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature