Lu Taoyuan, He Yanyan, Liu Zaoqu, Ma Chi, Chen Song, Jia Rufeng, Duan Lin, Guo Chunguang, Liu Yiying, Guo Dehua, Li Tianxiao, He Yingkun
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.
Front Cardiovasc Med. 2023 Feb 10;10:1075584. doi: 10.3389/fcvm.2023.1075584. eCollection 2023.
Intracranial 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.
Peripheral 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.
A 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.
The 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.
颅内动脉瘤（IA）是一种罕见但严重的脑血管疾病亚型，动脉瘤破裂后死亡率很高。目前的风险评估主要基于临床和影像学数据。本研究旨在开发一种分子检测工具，以优化IA风险监测系统。
从基因表达综合数据库中获取的外周血基因表达数据集被整合到一个发现队列中。利用加权基因共表达网络分析（WGCNA）和机器学习整合方法构建风险特征。采用实时定量聚合酶链反应（QRT-PCR）检测在一个内部队列中验证该模型。使用生物信息学方法评估免疫病理特征。
构建了一个由四个基因组成的机器学习衍生基因特征（MLDGS），用于识别IA破裂患者。MLDGS在发现队列和验证队列中的曲线下面积（AUC）分别为1.00和0.88。校准曲线和决策曲线分析也证实了MLDGS模型的良好性能。MLDGS与循环免疫病理格局显著相关。较高的MLDGS评分可能代表先天免疫细胞丰度较高、适应性免疫细胞丰度较低以及血管稳定性较差。
MLDGS为识别具有不良免疫病理特征和高动脉瘤破裂风险的患者提供了一个有前景的分子检测面板，有助于IA精准医学的发展。
