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

Abstract

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.

Artificial Intelligence Innovations in Cerebrovascular Neurosurgery: A Systematic Review of Cutting-edge Applications

“…After screening, we identi ed 21 studies related to the management of Intracranial Aneurysm (ICA) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46]. They consisted of eleven studies on diagnosing ICA [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], one study on treatment outcomes [38], nine studies on risk assessment [39], [40], [41], [42], [43], [44], [45], [47], and one study on prediction of post procedural complications [46], Table 4.…”

“…A four-gene machine learning-derived gene signature (MLDGS) was used. This signature identi ed patients with high risk of aneurysm rupture [45].…”

“…Studies comparing ML models with traditional scoring systems have demonstrated higher accuracy of ML models in predicting risk of cerebral infarction in AF patients [7]. Similarly, AI models have outperformed both traditional LR model and scoring systems in foreseeing the risk of rupture in intracerebral aneurysms, thereby presenting the potential to tailor treatment approaches [39], [40], [41], [42], [44], [45], [47]. In case of moyamoya disease, evidence suggests the AI model can be a potential model for e ciently analyzing risk of hemorrhage [56].…”