Profiling of Circulating Gene Expression Reveals Molecular Signatures Associated with Intracranial Aneurysm Rupture Risk

Background: Following detection, rupture risk assessment for intracranial aneurysms (IAs) is critical. Towards molecular prognostics, we hypothesized that circulating blood RNA expression profiles are associated with IA risk. Methods: We performed RNA sequencing on 68 blood samples from IA patients. Here, patients were categorized as either high or low risk by assessment of aneurysm size (≥ 5 mm = high risk) and Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, Site (PHASES) score (≥ 1 = high risk). Modified F-statistics and Benjamini-Hochberg false discovery rate correction was performed on transcripts per million-normalized gene counts. Protein-coding genes expressed in ≥ 50% of samples with a q value < 0.05 and an absolute fold-change ≥ 2 were considered significantly differentially expressed. Bioinformatics in Ingenuity Pathway Analysis was performed to understand the biology of risk-associated expression profiles. Association was assessed between gene expression and risk via Pearson correlation analysis. Linear discriminant analysis models using significant genes were created and validated for classification of high-risk cases. Results: We analyzed transcriptomes of 68 IA patients. In these cases, 31 IAs were large (≥ 5 mm), while 26 IAs had a high PHASES score. Based on size, 36 genes associated with high-risk IAs, and two were correlated with the size measurement. Alternatively, based on PHASES score, 76 genes associated with high-risk cases, and nine of them showed significant correlation to the score. Similar ontological terms were associated with both gene profiles, which reflected inflammatory signaling and vascular remodeling. Prediction models based on size and PHASES stratification were able to correctly predict IA risk status, with > 80% testing accuracy for both. Conclusions: Here, we identified genes associated with IA risk, as quantified by common clinical metrics. Preliminary classification models demonstrated feasibility of assessing IA risk using whole blood expression.