A machine learning-derived gene signature for assessing rupture risk and circulatory immunopathologic landscape in patients with intracranial aneurysms

Frontiers in Cardiovascular Medicine

Cardiovascular Medicine

Chi Ma¹  Tianxiao Li¹  Rufeng Jia¹  Yingkun He¹  Taoyuan Lu¹  Lin Duan¹  Dehua Guo¹  Yanyan He¹  Chunguang Guo²  Zaoqu Liu³  Yiying Liu⁴  Song Chen⁵ 

[1] Department of Cerebrovascular Disease and Neurosurgery, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, Henan, China;Henan International Joint Laboratory of Cerebrovascular Disease, Henan Provincial NeuroInterventional Engineering Research Center, Henan Engineering Research Center of Cerebrovascular Intervention Innovation, Zhengzhou, China;Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China;Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China;Department of Rehabilitation Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China;Translational Research Institute, Henan Provincial People’s Hospital, Zhengzhou, Henan, China;

关键词: intracranial aneurysm;    machine learning;    multigene model;    rupture risk;    immunopathological features;    precision medicine;   

DOI  :  10.3389/fcvm.2023.1075584

 received in 2022-11-03, accepted in 2023-01-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundIntracranial aneurysm (IA) is an uncommon but severe subtype of cerebrovascular disease, with high mortality after aneurysm rupture. Current risk assessments are mainly based on clinical and imaging data. This study aimed to develop a molecular assay tool for optimizing the IA risk monitoring system.MethodsPeripheral blood gene expression datasets obtained from the Gene Expression Omnibus were integrated into a discovery cohort. Weighted gene co-expression network analysis (WGCNA) and machine learning integrative approaches were utilized to construct a risk signature. QRT-PCR assay was performed to validate the model in an in-house cohort. Immunopathological features were estimated using bioinformatics methods.ResultsA four-gene machine learning-derived gene signature (MLDGS) was constructed for identifying patients with IA rupture. The AUC of MLDGS was 1.00 and 0.88 in discovery and validation cohorts, respectively. Calibration curve and decision curve analysis also confirmed the good performance of the MLDGS model. MLDGS was remarkably correlated with the circulating immunopathologic landscape. Higher MLDGS scores may represent higher abundance of innate immune cells, lower abundance of adaptive immune cells, and worse vascular stability.ConclusionsThe MLDGS provides a promising molecular assay panel for identifying patients with adverse immunopathological features and high risk of aneurysm rupture, contributing to advances in IA precision medicine.

【 授权许可】

Unknown   
Copyright © 2023 Lu, He, Liu, Ma, Chen, Jia, Duan, Guo, Liu, Guo, Li and He.