RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood

After detection, identifying which intracranial aneurysms (IAs) will rupture is imperative. We hypothesized that RNA expression in circulating blood reflects IA growth rate as a surrogate of instability and rupture risk. To this end, we performed RNA sequencing on 66 blood samples from IA patients, for which we also calculated the predicted aneurysm trajectory (PAT), a metric quantifying an IA's future growth rate. We dichotomized dataset using the median PAT score into IAs that were either more stable and more likely to grow quickly. The dataset was then randomly divided into training (n = 46) and testing cohorts (n = 20). In training, differentially expressed protein-coding genes were identified as those with expression (TPM > 0.5) in at least 50% of the samples, a q-value < 0.05 (based on modified F-statistics with Benjamini-Hochberg correction), and an absolute fold-change >= 1.5. Ingenuity Pathway Analysis was used to construct networks of gene associations and to perform ontology term enrichment analysis. The MATLAB Classification Learner was then employed to assess modeling capability of the differentially expressed genes, using a 5-fold cross validation in training. Finally, the model was applied to the withheld, independent testing cohort (n = 20) to assess its predictive ability. In all, we examined transcriptomes of 66 IA patients, of which 33 IAs were growing (PAT >= 4.6) and 33 were more stable. After dividing dataset into training and testing, we identified 39 genes in training as differentially expressed (11 with decreased expression in growing and 28 with increased expression). Model genes largely reflected organismal injury and abnormalities and cell to cell signaling and interaction. Preliminary modeling using a subspace discriminant ensemble model achieved a training AUC of 0.85 and a testing AUC of 0.86. In conclusion, transcriptomic expression in circulating blood indeed can distinguish growing and stable IA cases. The predictive model constructed from these differentially expressed genes could be used to assess IA stability and rupture potential.

Automated summary

Following detection, it is crucial to determine which intracranial aneurysms (IAs) will rupture. In this study, we investigated the possibility of using RNA expression in circulating blood to reflect IA growth rate and predict instability and rupture risk. Through RNA sequencing and gene analysis, we identified differentially expressed genes that were associated with IA stability. We then constructed a predictive model using these genes' expression levels and demonstrated its ability to assess IA stability and rupture potential.