Abstract 197: Roles of Neutrophil Extracellular Trap in the Rupture of Intracranial Aneurysm

Neutrophil extracellular traps in intracerebral hemorrhage: implications for pathogenesis and therapeutic targets

Intracerebral hemorrhage is a common neurological disease, and its pathological mechanism is complex. As the first recruited leukocyte subtype after intracerebral hemorrhage, neutrophils play an important role in tissue damage. In the past, it was considered that neutrophils performed their functions through phagocytosis, chemotaxis, and degranulation. In recent years, studies have found that neutrophils also have the function of secreting extracellular traps. Extracellular traps are fibrous structure composed of chromatin and granular proteins, which plays an important role in innate immunity. Studies have shown a large number of neutrophil extracellular traps in hematoma samples, plasma samples, and drainage samples after intracerebral hemorrhage. In this paper, we summarized the related mechanisms of neutrophil external traps and injury after intracerebral hemorrhage. Neutrophil extracellular traps are involved in the process of brain injury after intracerebral hemorrhage. The application of related inhibitors to inhibit the formation of neutrophil external traps or promote their dissolution can effectively alleviate the pathological damage caused by intracerebral hemorrhage.

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Background

Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods.

Methods

Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction.

Results

Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance.

Conclusions

We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.