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

Neutrophil-to-lymphocyte ratio associated with symptomatic saccular unruptured intracranial aneurysm

BACKGROUND AND PURPOSE

Whether symptomatic unruptured intracranial aneurysms (UIAs) lead to change in circulating inflammation remains unclear. This study aims to evaluate the role of hematological inflammatory indicators in predicting symptomatic UIA.

METHODS

Adult patients diagnosed with saccular intracranial aneurysm from March 2019 to September 2023 were recruited retrospectively. Clinical and laboratory data, including the white blood cells (WBC), neutral counts (NEUT), lymphocyte counts (LYM), and monocyte counts (MONO) of each patient, were collected. The neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) were calculated as NLR = NEUT/LYM, LMR = LYM/MONO, SII = PLT*NEUT/LYM. The hematological inflammatory indicators were compared in symptomatic saccular and asymptomatic UIA patients. Multivariable logistic regression analyses were performed to explore the factors predicting symptomatic UIA.

RESULTS

One hundred and fifty UIA patients with a mean age of 58.5 ± 12.4 were included, of which 68% were females. The NLR and LMR were significantly associated with symptomatic UIA, and the association remained in small UIAs (< 7 mm). The multiple logistic regression analysis showed that NLR was independently associated with symptomatic UIA. On ROC curve analysis, the optimal cutoff value of NLR to differentiate symptomatic from asymptomatic was 2.38. In addition, LMR was significantly associated with symptomatic UIA smaller than 7 mm.

CONCLUSION

There was a significant correlation between NLR and symptomatic UIA. The NLR was independently associated with symptomatic UIA.

Dysregulated Genes and Signaling Pathways in the Formation and Rupture of Intracranial Aneurysm

Intracranial aneurysm (IA) has the potential to rupture. Despite scientific advances, we are still not in a position to screen patients for IA and identify those at risk of rupture. It is critical to comprehend the molecular basis of disease to facilitate the development of novel diagnostic strategies. We used transcriptomics to identify the dysregulated genes and understand their role in the disease biology. In particular, RNA-Seq was performed in tissue samples of controls, unruptured IA, and ruptured IA. Dysregulated genes (DGs) were identified and analyzed to understand the functional aspects of molecules. Subsequently, candidate genes were validated at both transcript and protein level. There were 314 DGs in patients with unruptured IA when compared to control samples. Out of these, SPARC and OSM were validated as candidate molecules in unruptured IA. PI3K-AKT signaling pathway was found to be an important pathway for the formation of IA. Similarly, 301 DGs were identified in the samples of ruptured IA when compared with unruptured IAs. CTSL was found to be a key candidate molecule which along with Hippo signaling pathway may be involved in the rupture of IA. We conclude that activation of PI3K-AKT signaling pathway by OSM along with up-regulation of SPARC is important for the formation of IA. Further, regulation of Hippo pathway through PI3K-AKT signaling results in the down-regulation of YAP1 gene. This along with up-regulation of CTSL leads to further weakening of aneurysm wall and its subsequent rupture.

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.

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.

Bioinformatics approach to analyse COVID-19 biomarkers accountable for generation of intracranial aneurysm in COVID-19 patients

COVID-19 became a health emergency on January 30, 2020. SARS-CoV-2 is the causative agent of the coronavirus disease known as COVID-19 and can develop cardiometabolic and neurological disorders. Intracranial aneurysm (IA) is considered the most significant reason for hemorrhagic stroke,and it accounts for approximately 85% of all subarachnoid hemorrhages (SAH). Retinoid signaling abnormalities may explain COVID-19's pathogenesis with inhibition of AEH2, from which COVID-19 infection may enhance aneurysm formation and rupture due to abrupt blood pressure changes, endothelial cell injury, and systemic inflammation. The objective of this study was to investigate the potential biomarkers, differentially expressed genes (DEGs), and metabolic pathways associated with both COVID-19 and intracranial aneurysm (IA) using simulation databases like DIsGeNET. The purpose was to confirm prior findings and gain a comprehensive understanding of the underlying mechanisms that contribute to the development of these conditions. We combined the regulated genes to describe intracranial aneurysm formation in COVID-19. To determine DEGs in COVID-19 and IA patient tissues, we compared gene expression transcriptomic datasets from healthy and diseased individuals. There were 41 differentially expressed genes (DEGs) shared by both the COVID-19 and IA datasets (27 up-regulated genes and 14 down-regulated genes). Using protein-protein interaction analysis, we were able to identify hub proteins (C3, NCR1, IL10RA, OXTR, RSAD2, CD38, IL10RB, MX1, IL10, GFAP, IFIT3, XAF1, USP18, OASL, IFI6, EPSTI1, CMPK2, and ISG15), which were not described as key proteins for both COVID-19 and IA before. We also used Gene Ontology analysis (6 significant ontologies were validated), Pathway analysis (the top 20 were validated), TF-Gene interaction analysis, Gene miRNA analysis, and Drug-Protein interaction analysis methods to comprehend the extensive connection between COVID-19 and IA. In Drug-Protein interaction analysis, we have gotten the following three drugs: LLL-3348, CRx139, and AV41 against IL10 which was both common for COVID-19 and IA disease. Our study with different cabalistic methods has showed the interaction between the proteins and pathways with drug analysis which may direct further treatment development for certain diseases.

Circular RNA hsa_circ_0007990 as a blood biomarker for unruptured intracranial aneurysm with aneurysm wall enhancement

BACKGROUND

Circular RNAs (circRNAs) may involve the formation and rupture of intracranial aneurysms (IA). Inflammation plays a vital role in the development and progression of IA, which can be reflected by aneurysm wall enhancement (AWE) on high-resolution vessel wall magnetic resonance imaging (HR-VWI). This study aims to evaluate the role of circRNAs as the blood inflammatory biomarker for unruptured IA (UIA) patients with AWE on HR-VWI.

METHODS

We analyzed the circRNA expression profiles in the peripheral blood samples among subjects from saccular UIA with AWE, UIA without AWE, and healthy controls by the circRNA microarray. The differential expression of hsa_circ_0007990 was assessed. We constructed the hsa_circ_0007990-microRNA-mRNA network and the regulatory axis of hub genes associated with the AWE in UIA.

RESULTS

Eighteen patients harboring saccular UIAs with HR VWI and five healthy controls were included. We found 412 differentially expressed circRNAs between UIA patients and healthy controls by circRNA microarray. Two hundred thirty-one circRNAs were significantly differentially expressed in UIA patients with AWE compared with those without AWE. Twelve upregulated circRNAs were associated with AWE of UIA, including hsa_circ_0007990, hsa_circ_0114507, hsa_circ_0020460, hsa_circ_0053944, hsa_circ_0000758, hsa_circ_0000034, hsa_circ_0009127, hsa_circ_0052793, hsa_circ_0000301 and hsa_circ_0000729. The expression of hsa_circ_0007990 was increased gradually in the healthy control, UIA without AWE, and UIA with AWE confirmed by RT-PCR (P<0.001). We predicted 4 RNA binding proteins (Ago2, DGCR8, EIF4A3, PTB) and period circadian regulator 1 as an encoding protein with hsa_circ_0007990. The hsa_circ_0007990-microRNA-mRNA network containing five microRNAs (miR-4717-5p, miR-1275, miR-150-3p, miR-18a-5p, miR-18b-5p), and 97 mRNAs was constructed. The five hub genes (hypoxia-inducible factor 1 subunit alpha, estrogen receptor 1, forkhead box O1, insulin-like growth factor 1, CREB binding protein) were involved in the inflammatory response.

CONCLUSION

Differentially expressed blood circRNAs associated with AWE on HR-VWI may be the novel inflammatory biomarkers for assessing UIA patients. The mechanism of hsa_circRNA_0007990 for UIA progression needs to investigate further.

High-Dimensional Immune Profiling by Mass Cytometry Revealed the Circulating Immune Cell Landscape in Patients With Intracranial Aneurysm

Background

Increasing evidence supports a critical role of chronic inflammation in intracranial aneurysm (IA). Understanding how the immunological alterations in IA provides opportunities for targeted treatment. However, there is a lack of comprehensive and detailed characterization of the changes in circulating immune cells in IA.

Objective

To perform a comprehensive and detailed characterization of the changes in circulating immune cells in patients with IA.

Methods

Peripheral blood mononuclear cell samples from IA patients (n = 26) and age-and sex-matched healthy controls (HCs, n = 20) were analyzed using high dimensional mass cytometry, and the frequency and phenotype of immune cell subtypes were assessed.

Results

We identified 28 cell clusters and found that the immune signature of IA consists of cluster changes. IA patients exhibited dysfunction of immunity, with dysregulation of CD4⁺ T-cell clusters, increased B cells and monocytes, and decreased CD8⁺ T cells, DNT cells, and DPT cells. Moreover, compared with findings in HC, IA was associated with enhanced lymphocyte and monocyte immune activation, with a higher expression of HLA-DR, CXCR3, and CX3CR1. In addition, the expression of TLR4, p-STAT3, and the exhaustion marker PD1 was increased in T cells, B cells, and NK cells in IA patients.

Conclusions

Our data provide an overview of the circulating immune cell landscape of IA patients, and reveal that the dysfunction of circulating immunity may play a potential role in the development of IA.

Web-based transcriptome analysis determines a sixteen-gene signature and associated drugs on hearing loss patients: A bioinformatics approach

Background

Hearing loss is becoming more and more general. It may occur at all age and affect the language learning ability of children and trigger serious social problems.

Methods

The hearing loss differentially expressed genes (HL‐DEGs) were recognized through a comparison with healthy subjects. The Gene Ontology (GO) analysis was executed by DAVID. The reactome analysis of HL‐DEGs was performed by Clue‐GO. Next, we used STRING, an online website, to identify crucial protein‐protein interactions among HL‐DEGs. Cytoscape software was employed to construct a protein‐protein interaction network. MCODE, a plug‐in of the Cytoscape software, was used for module analysis. Finally, we used DGIdb database to ascertain the targeted drugs for MCODE genes.

Results

Four hundred four HL‐DEGs were identified, among which the most up‐regulated 10 genes were AL008707.1, SDR42E1P5, BX005040.1, AL671883.2, MT1XP1, AC016957.1, U2AF1L5, XIST, DAAM2, and ADAMTS2, and the most down‐regulated 10 genes were ALOX15, PRSS33, IL5RA, SMPD3, IGHV1‐2, IGLV3‐9, RHOXF1P1, CACNG6, MYOM2, and RSAD2. Through STRING database and MCODE analysis, we finally got 16 MCODE genes. These genes can be regarded as hearing loss related genes. Through biological analysis, it is found that these genes are enriched in pathways related to apoptosis such as tumor necrosis factor. Among them, MMP8, LTF, ORM2, FOLR3, and TCN1 have corresponding targeted drugs. Foremost, MCODE genes should be investigated for its usefulness as a new biomarker for diagnosis and treatment.

Conclusion

In summary, our study produced a sixteen‐gene signature and associated drugs that could be diagnosis and treatment of hearing loss patients.

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.