Identification of key therapeutic targets in nicotine-induced intracranial aneurysm through integrated bioinformatics and machine learning approaches.

ABSTRACT:

Background

Intracranial aneurysm (IA) is a critical cerebrovascular condition, and nicotine exposure is a known risk factor. This study delves into the toxicological mechanisms of nicotine in IA, aiming to identify key biomarkers and therapeutic targets.

Methods

Gene Set Variation Analysis (GSVA), Weighted Gene Co-Expression Network Analysis (WGCNA), and enrichment analyses were conducted on differentially expressed genes (DEGs) from the GSE122897 dataset. Additionally, nicotine-related targets were identified using CTD, SwissTargetPrediction, and Super-PRED databases. Integrative machine learning approaches, such as Random Forest (RF) and Support Vector Machine (SVM), were employed to pinpoint key toxicity targets. Molecular docking and immune cell infiltration analyses wer

SUBMITTER: Ma Q 

PROVIDER: S-EPMC12007307 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature