Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Aspirin treatment for unruptured intracranial aneurysms: Focusing on its anti-inflammatory role

Intracranial aneurysms (IAs), as a common cerebrovascular disease, claims a worldwide morbidity rate of 3.2%. Inflammation, pivotal in the pathogenesis of IAs, influences their formation, growth, and rupture. This review investigates aspirin's modulation of inflammatory pathways within this context. With IAs carrying significant morbidity and mortality upon IAs rupture and current interventions limited to surgical clipping and endovascular coiling, the quest for pharmacological options is imperative. Aspirin's role in cardiovascular prevention, due to its anti-inflammatory effects, presents a potential therapeutic avenue for IAs. In this review, we examine aspirin's efficacy in experimental models and clinical settings, highlighting its impact on the progression and rupture risks of unruptured IAs. The underlying mechanisms of aspirin's impact on IAs are explored, with its ability examined to attenuate endothelial dysfunction and vascular injury. This review may provide a theoretical basis for the use of aspirin, suggesting a promising strategy for IAs management. However, the optimal dosing, safety, and long-term efficacy remain to be established. The implications of aspirin therapy are significant in light of current surgical and endovascular treatments. Further research is encouraged to refine aspirin's clinical application in the management of unruptured IAs, with the ultimate aim of reducing the incidence of aneurysms rupture.

Global trend in research of intracranial aneurysm management with artificial intelligence technology: a bibliometric analysis

Background

The use of artificial intelligence (AI) technology has been growing in the management of intracranial aneurysms (IAs). This study aims to conduct a bibliometric analysis of researches on intracranial aneurysm management with artificial intelligence technology (IAMWAIT) to gain insights into global research trends and potential future directions.

Methods

A comprehensive search of articles and reviews related to IAMWAIT, published from January 1, 1900 to July 20, 2023, was conducted using the Web of Science Core Collection (WoWCC).Visualizations of the bibliometric analysis were generated utilizing WPS Office, Scimago Graphica, VOSviewer, CiteSpace, and R.

Results

A total of 277 papers were included in the study. China emerged as the most prolific country in terms of publications, institutions, cooperating countries, and prolific authors. The United States garnered the highest number of total citations, institutions with the highest citations/H index, cooperating countries (n=9), and 3 of the top 10 cited papers. Both the total number of papers and the citation count exhibited a positive and significant correlation with the gross domestic product (GDP) of countries. The journal with the highest publication frequency was Frontiers in Neurology, while Stroke recorded the highest number of citations, H-index, and impact factor (IF). Areas of primary interest in IAMWAIT, leveraging AI technology, included rupture risk assessment/prediction, computer-assisted diagnosis, outcome prediction, hemodynamics, and laboratory research of IAs.

Conclusions

IAMWAIT is an active area of research that has undergone rapid development in recent years. Future endeavors should focus on broader application of AI algorithms in various sub-fields of IAMWAIT to better suit the real world.

Molecular Subtypes and Machine Learning-Based Predictive Models for Intracranial Aneurysm Rupture

BACKGROUND

The determination of biological mechanisms and biomarkers related to intracranial aneurysm (IA) rupture is of utmost significance for the development of effective preventive and therapeutic strategies in the clinical field.

METHODS

GSE122897 and GSE13353 datasets were downloaded from Gene Expression Omnibus. Data extracted from GSE122897 were used for analyzing differential gene expression, and consensus clustering was performed to identify stable molecular subtypes. Clinical characteristics were compared between subgroups, and fast gene set enrichment analysis and weighted gene coexpression network analysis were performed. Hub genes were identified via least absolute shrinkage and selection operator analysis. Predictive models were constructed based on hub genes using the Light Gradient Boosting Machine, eXtreme Gradient Boosting, and logistic regression algorithm. Immune cell infiltration in IA samples was analyzed using Microenvironment Cell Population counter, CIBERSORT, and xCell algorithm. The correlation between hub genes and immune cells was analyzed. The predictive model and immune cell infiltration were validated using data from the GSE13353 dataset.

RESULTS

A total of 43 IA samples were classified into 2 subgroups based on gene expression profiles. Subgroup I had a higher risk of rupture, while 70% of subgroup II remained unruptured. In subgroup I, specific genes were associated with inflammation and immunity, and weighted gene coexpression network analysis revealed that the black module genes were linked to IA rupture. We identified 4 hub genes (spermine synthase, macrophage receptor with collagenous structure, zymogen granule protein 16B, and LIM and calponin-homology domains 1), which constructed predictive models with good diagnostic performance in differentiating between ruptured and unruptured IA samples. Monocytic lineage was found to be a significant factor in IA rupture, and the 4 hub genes were linked to monocytic lineage (P < 0.05).

CONCLUSIONS

We reveal a new molecular subtype that can reflect the actual pathological state of IA rupture, and our predictive models constructed by machine learning algorithms can efficiently predict IA rupture.

Gene expression profiles of ischemic stroke clots retrieved by mechanical thrombectomy are associated with disease etiology

BACKGROUND

Determining stroke etiology is crucial for secondary prevention, but intensive workups fail to classify ~30% of strokes that are cryptogenic.

OBJECTIVE

To examine the hypothesis that the transcriptomic profiles of clots retrieved during mechanical thrombectomy are unique to strokes of different subtypes.

METHODS

We isolated RNA from the clots of 73 patients undergoing mechanical thrombectomy. Samples of sufficient quality were subjected to 100-cycle, paired-end RNAseq, and transcriptomes with less than 10 million unique reads were excluded from analysis. Significant differentially expressed genes (DEGs) between subtypes (defined by the Trial of Org 10 172 in Acute Stroke Treatment) were identified by expression analysis in edgeR. Gene ontology enrichment analysis was used to study the biologic differences between stroke etiologies.

RESULTS

In all, 38 clot transcriptomes were analyzed; 6 from large artery atherosclerosis (LAA), 21 from cardioembolism (CE), 5 from strokes of other determined origin, and 6 from cryptogenic strokes. Among all comparisons, there were 816 unique DEGs, 174 of which were shared by at least two comparisons, and 20 of which were shared by all three. Gene ontology analysis showed that CE clots reflected high levels of inflammation, LAA clots had greater oxidoreduction and T-cell processes, and clots of other determined origin were enriched for aberrant platelet and hemoglobin-related processes. Principal component analysis indicated separation between these subtypes and showed cryptogenic samples clustered among several different groups.

CONCLUSIONS

Expression profiles of stroke clots were identified between stroke etiologies and reflected different biologic responses. Cryptogenic thrombi may be related to multiple etiologies.

Transcriptomic Studies on Intracranial Aneurysms

Intracranial aneurysm (IA) is a relatively common vascular malformation of an intracranial artery. In most cases, its presence is asymptomatic, but IA rupture causing subarachnoid hemorrhage is a life-threating condition with very high mortality and disability rates. Despite intensive studies, molecular mechanisms underlying the pathophysiology of IA formation, growth, and rupture remain poorly understood. There are no specific biomarkers of IA presence or rupture. Analysis of expression of mRNA and other RNA types offers a deeper insight into IA pathobiology. Here, we present results of published human studies on IA-focused transcriptomics.

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

Background

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.

Methods

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.

Results

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.

Conclusions

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.

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.

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.

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.

Isolation of RNA from Acute Ischemic Stroke Clots Retrieved by Mechanical Thrombectomy

Mechanical thrombectomy (MT) for large vessel acute ischemic stroke (AIS) has enabled biologic analyses of resected clots. While clot histology has been well-studied, little is known about gene expression within the tissue, which could shed light on stroke pathophysiology. In this methodological study, we develop a pipeline for obtaining useful RNA from AIS clots. A total of 73 clot samples retrieved by MT were collected and stored in RNALater and in 10% phosphate-buffered formalin. RNA was extracted from all samples using a modified Chemagen magnetic bead extraction protocol on the PerkinElmer Chemagic 360. RNA was interrogated by UV–Vis absorption and electrophoretic quality control analysis. All samples with sufficient volume underwent traditional qPCR analysis and samples with sufficient RNA quality were subjected to next-generation RNA sequencing on the Illumina NovaSeq platform. Whole blood RNA samples from three patients were used as controls, and H&E-stained histological sections of the clots were used to assess clot cellular makeup. Isolated mRNA was eluted into a volume of 140 µL and had a concentration ranging from 0.01 ng/µL to 46 ng/µL. Most mRNA samples were partially degraded, with RNA integrity numbers ranging from 0 to 9.5. The majority of samples (71/73) underwent qPCR analysis, which showed linear relationships between the expression of three housekeeping genes (GAPDH, GPI, and HPRT1) across all samples. Of these, 48 samples were used for RNA sequencing, which had moderate quality based on MultiQC evaluation (on average, ~35 M reads were sequenced). Analysis of clot histology showed that more acellular samples yielded RNA of lower quantity and quality. We obtained useful mRNA from AIS clot samples stored in RNALater. qPCR analysis could be performed in almost all cases, while sequencing data could only be performed in approximately two-thirds of the samples. Acellular clots tended to have lower RNA quantity and quality.

A Two-Gene-Based Diagnostic Signature for Ruptured Intracranial Aneurysms

Background: Ruptured intracranial aneurysm (IA) is a disease with high mortality. Despite the great progress in treating ruptured IA, methods for risk assessment of ruptured IA remain limited. Methods: In this study, we aim to develop a robust diagnostic model for ruptured IA. Gene expression profiles in blood samples of 18 healthy persons and 43 ruptured IA patients were obtained from the Gene Expression Omnibus (GEO). Differential expression analysis was performed using limma Bioconductor package followed by functional enrichment analysis via clusterProfiler Bioconductor package. Immune cell compositions in ruptured IA and healthy samples were assessed through the CIBERSORT tool. Protein-protein interaction (PPI) was predicted based on the STRING database. Logistic regression model was used for the construction of predictive model for distinguishing ruptured IA and healthy samples. Real-time quantitative polymerase chain reaction (RT-qPCR) was performed to validate the gene expression between the ruptured IA and healthy samples. Results: A total of 58 differentially expressed genes (DEGs) were obtained for ruptured IA patients compared with healthy controls. Functional enrichment analysis showed that the DEGs were enriched in biological processes related to neutrophil activation, neutrophil degranulation, and cytokine-cytokine receptor interaction. Notably, immune analysis results proved that the rupture of IA might be related to immune cell distribution. We further identified 24 key genes as hub genes using the PPI networks. The logistic regression model trained based on the 24 key genes ultimately retained two genes, i.e., IL2RB and CCR7, which had great potential for risk assessment for rupture of IA. The RT-qPCR further validated that compared with the healthy samples, the expression levels of IL2RB and CCR7 were decreased in ruptured IA samples. Conclusions: This study might be helpful for cohorts who have a high risk of ruptured IA for early diagnosis and prevention of the disease.

Inflammatory Biomarkers and Intracranial Hemorrhage after Endovascular Thrombectomy

BACKGROUND

Intracranial hemorrhage after endovascular thrombectomy is associated with poorer prognosis compared with those who do not develop the complication. Our study aims to determine predictors of post-EVT hemorrhage - more specifically, inflammatory biomarkers present in baseline serology.

METHODS

We performed a retrospective review of consecutive patients treated with EVT for acute large vessel ischemic stroke. The primary outcome of the study is the presence of ICH on the post-EVT scan. We used four definitions: the SITS-MOST criteria, the NINDS criteria, asymptomatic hemorrhage, and overall hemorrhage. We identified nonredundant predictors of outcome using backward elimination based on Akaike Information Criteria. We then assessed prediction accuracy using area under the receiver operating curve. Then we implemented variable importance ranking from logistic regression models using the drop in Naegelkerke R2 with the exclusion of each predictor.

RESULTS

Our study demonstrates a 6.3% SITS (16/252) and 10.0% NINDS (25/252) sICH rate, as well as a 19.4% asymptomatic (49/252) and 29.4% (74/252) overall hemorrhage rate. Serologic markers that demonstrated association with post-EVT hemorrhage were: low lymphocyte count (SITS), high neutrophil count (NINDS, overall hemorrhage), low platelet to lymphocyte ratio (NINDS), and low total WBC (NINDS, asymptomatic hemorrhage).

CONCLUSION

Higher neutrophil counts, low WBC counts, low lymphocyte counts, and low platelet to lymphoycyte ratio were baseline serology biomarkers that were associated with post-EVT hemorrhage. Our findings, particularly the association of diabetes mellitus and high neutrophil, support experimental data on the role of thromboinflammation in hemorrhagic transformation of large vessel occlusions.

Aberrant Whole Blood Gene Expression in the Lumen of Human Intracranial Aneurysms

The rupture of an intracranial aneurysm (IA) causes devastating hemorrhagic strokes. Yet, most IAs remain asymptomatic and undetected until they rupture. In the search for circulating biomarkers of unruptured IAs, we previously performed transcriptome profiling on whole blood and identified an IA-associated panel of 18 genes. In this study, we seek to determine if these genes are also differentially expressed within the IA lumen, which could provide a mechanistic link between the disease and the observed circulating gene expression patterns. To this end, we collected blood from the lumen of 37 IAs and their proximal parent vessels in 31 patients. The expression levels of 18 genes in the lumen and proximal vessel were then measured by quantitative polymerase chain reaction. This analysis revealed that the expression of 6/18 genes (CBWD6, MT2A, MZT2B, PIM3, SLC37A3, and TNFRSF4) was significantly higher in intraluminal blood, while the expression of 3/18 genes (ST6GALNAC1, TCN2, and UFSP1) was significantly lower. There was a significant, positive correlation between intraluminal and proximal expression of CXCL10, MT2A, and MZT2B, suggesting local increases of these genes is reflected in the periphery. Expression of ST6GALNAC1 and TIFAB was significantly positively correlated with IA size, while expression of CCDC85B was significantly positively correlated with IA enhancement on post-contrast MRI, a metric of IA instability and risk. In conclusion, intraluminal expression differences in half of the IA-associated genes observed in this study provide evidence for IA tissue-mediated transcriptional changes in whole blood. Additionally, some genes may be informative in assessing IA risk, as their intraluminal expression was correlated to IA size and aneurysmal wall enhancement.

Identification of Circulating Gene Expression Signatures of Intracranial Aneurysm in Peripheral Blood Mononuclear Cells

Peripheral blood mononuclear cells (PBMCs) play an important role in the inflammation that accompanies intracranial aneurysm (IA) pathophysiology. We hypothesized that PBMCs have different transcriptional profiles in patients harboring IAs as compared to IA-free controls, which could be the basis for potential blood-based biomarkers for the disease. To test this, we isolated PBMC RNA from whole blood of 52 subjects (24 with IA, 28 without) and performed next-generation RNA sequencing to obtain their transcriptomes. In a randomly assigned discovery cohort of n = 39 patients, we performed differential expression analysis to define an IA-associated signature of 54 genes (q < 0.05 and an absolute fold-change ≥ 1.3). In the withheld validation dataset, these genes could delineate patients with IAs from controls, as the majority of them still had the same direction of expression difference. Bioinformatics analyses by gene ontology enrichment analysis and Ingenuity Pathway Analysis (IPA) demonstrated enrichment of structural regulation processes, intracellular signaling function, regulation of ion transport, and cell adhesion. IPA analysis showed that these processes were likely coordinated through NF-kB, cytokine signaling, growth factors, and TNF activity. Correlation analysis with aneurysm size and risk assessment metrics showed that 4/54 genes were associated with rupture risk. These findings highlight the potential to develop predictive biomarkers from PBMCs to identify patients harboring IAs.