Expression profile analysis of differentially expressed genes in ruptured intracranial aneurysms: In search of biomarkers

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 two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms

An IA is an abnormal swelling of cerebral vessels, and a subset of these IAs can rupture causing aneurysmal subarachnoid hemorrhage (aSAH), often resulting in death or severe disability. Few studies have used an appropriate method of feature selection combined with machine learning by analyzing transcriptomic sequencing data to identify new molecular biomarkers. Following gene ontology (GO) and enrichment analysis, we found that the distinct status of IAs could lead to differential innate immune responses using all 913 differentially expressed genes, and considering that there are numerous irrelevant and redundant genes, we propose a mixed filter- and wrapper-based feature selection. First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. Finally, we constructed a novel 10-gene signature (YIPF1, RAB32, WDR62, ANPEP, LRRCC1, AADAC, GZMK, WBP2NL, PBX1, and TOR1B) by the proposed two-stage hybrid algorithm FCBF-MLP-PSO and used different machine learning models to predict the rupture status in IAs. The highest ACC value increased from 0.817 to 0.919 (12.5% increase), the highest area under ROC curve (AUC) value increased from 0.87 to 0.94 (8.0% increase), and all evaluation metrics improved by approximately 10% after being processed by our proposed gene selection algorithm. Therefore, these 10 informative genes used to predict rupture status of IAs can be used as complements to imaging examinations in the clinic, meanwhile, this selected gene signature also provides new targets and approaches for the treatment of ruptured IAs.

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.

Sustained expression of MCP-1 induced low wall shear stress loading in conjunction with turbulent flow on endothelial cells of intracranial aneurysm

Shear stress was reported to regulate the expression of AC007362, but its underlying mechanisms remain to be explored. In this study, to isolate endothelial cells of blood vessels, unruptured and ruptured intracranial aneurysm (IA) tissues were collected from IA patients. Subsequently, quantitative real‐time PCR (qRT‐PCR), Western blot and luciferase assay were performed to investigate the relationships between AC007362, miRNAs‐493 and monocyte chemoattractant protein‐1 (MCP‐1) in human umbilical vein endothelial cells (HUVECs) exposed to shear stress. Reduced representation bisulphite sequencing (RRBS) was performed to assess the level of DNA methylation in AC007362 promoter. Accordingly, AC007362 and MCP‐1 were significantly up‐regulated while miR‐493 was significantly down‐regulated in HUVECs exposed to shear stress. AC007362 could suppress the miR‐493 expression and elevate the MCP‐1 expression, and miR‐493 was shown to respectively target AC007362 and MCP‐1. Moreover, shear stress in HUVECs led to the down‐regulated DNA methyltransferase 1 (DNMT1), as well as the decreased DNA methylation level of AC007362 promoter. Similar results were also observed in ruptured IA tissues when compared with unruptured IA tissues. In conclusion, this study presented a deep insight into the operation of the regulatory network of AC007362, miR‐493 and MCP‐1 upon shear stress. Under shear stress, the expression of AC007362 was enhanced by the inhibited promoter DNA methylation, while the expression of MCP‐1 was enhanced by sponging the expression of miR‐493.

Weighted gene co-expression network analysis identified six hub genes associated with rupture of intracranial aneurysms

Intracranial aneurysms (IAs) are characterized by localized dilation or ballooning of a cerebral artery. When IAs rupture, blood leaks into the space around the brain to create a subarachnoid hemorrhage. The latter is associated with a higher risk of disability and mortality. The aims of this study were to gain greater insight into the pathogenesis of ruptured IAs, and to clarify whether identified hub genes represent potential biological markers for assessing the likelihood of IA progression and rupture. Briefly, the GSE36791 and GSE73378 datasets from the National Center of Biotechnology Information Gene Expression Omnibus database were reanalyzed and subjected to a weighted gene co-expression network analysis to test the association between gene sets and clinical features. The clinical significance of these genes as potential biomarkers was also examined, with their expression validated by quantitative real-time PCR. A total of 14 co-expression modules and 238 hub genes were identified. In particular, three modules (labeled turquoise, blue, and brown) were found to highly correlate with IA rupture events. Additionally, six potential biomarkers were identified (BASP1, CEBPB, ECHDC2, GZMK, KLHL3, and SLC2A3), which are strongly associated with the progression and rupture of IAs. Taken together, these findings provide novel insights into potential molecular mechanisms responsible for IAs and they highlight the potential for these particular genes to serve as biomarkers for monitoring IA rupture.