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

Li, Yuwang; Qin, Jie

doi:10.3389/fcvm.2021.671655

Cited by 7 publications

(6 citation statements)

References 46 publications

(47 reference statements)

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“…ARG1, TPST1, S100A12, and PGLYRP1 were identi ed as key genes for aSAH (57,58). Of all down-regulated genes, CCR7 and CD27 were also identi ed as pivotal genes for aSAH (57,59). Therefore, we suggested that these 22 genes may be associated with immunity and in ammation and involved in the rupture of intracranial aneurysms and thus in the formation of SAH through potential mechanisms.…”

Section: Discussionmentioning

confidence: 87%

Immune-related genes construct and validate a risk model for aneurysm rupture leading to subarachnoid hemorrhage

Jin

Ren

Chen

et al. 2023

Preprint

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Background. Aneurysmal subarachnoid hemorrhage (aSAH) is an acute cerebrovascular illness with a pronounced rate of disability and fatality. Assessing the risk of rupture in unruptured intracranial aneurysms (UIAs) can guide early clinical intervention to avoid subarachnoid hemorrhage (SAH). Current methods to assess the occurrence of aSAH at the molecular level are inadequate. Numerous studies have shown that immunity and inflammation are the initiating factors of UIAs rupture, which provides a potential theoretical support for constructing a risk model for aSAH occurrence based on immune-related genes. Methods. GSE36791 containing 61 samples was used as the training set, and GSE73378 containing 226 samples was used as the validation set, and the above data sets were obtained from the Gene Expression Omnibus database. All samples were gene expression profiling from peripheral blood cells. Using R software to identify differential genes and key gene modules and explore the underlying biological processes of differential genes. Immune-related analyses were based on ssGSEA and CIBERSORT algorithms. Using Cox regression to fit the risk model for aSAH occurrence with 3 randomly selected immune-related genes, and validating the model in the test set. Result. A total of 22 hub genes were obtained by weighted gene co-expression network analysis and gene differential expression analysis. Functional enrichment analysis showed that hub genes were mainly enriched in immune and inflammation-related pathways. The results showed that neutrophils and monocytes made up the largest proportion of the samples. CD6, SLP and SLC2A11 were randomly selected to construct risk model for aSAH occurrence. Our analysis resulted in an AUC value of 0.859 for the training set and 0.627 for the validation set, indicating that the model has some accuracy and potential clinical application. Conclusion. We identified 22 key genes associated with immunity and randomly selected 3 genes to construct and validate a risk model for the development of SAH due to aneurysm rupture. We determined the likelihood of intracranial aneurysm rupture based on the model score. It is possible to quickly and conveniently guide whether early intervention is needed for unruptured aneurysms at the molecular level to prevent the formation of SAH and related complications. In addition, pivotal gene-mediated inflammation provides a new perspective for future studies on the mechanisms of aSAH initiation and subsequent neurological deterioration.

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Section: Discussionmentioning

confidence: 87%

Immune-related genes construct and validate a risk model for aneurysm rupture leading to subarachnoid hemorrhage

Jin

Ren

Chen

et al. 2023

Preprint

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“…Purple and blue represented positive and negative correlation between pathway and MLDGS, respectively. (44)(45)(46); however, they are all IA diagnostic models constructed using healthy subjects as controls and still without the in-depth assessment of molecular pathological features. In clinical settings, a biomarker with the ability to identify high-risk patients from the population with established IAs may be more appropriate for clinical decision-making.…”

Section: Discussionmentioning

confidence: 99%

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

He²,

Han³

et al. 2023

Front. Cardiovasc. Med.

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BackgroundIntracranial 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.MethodsPeripheral 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.ResultsA 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.ConclusionsThe 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.

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“…It is easier to design a new study and to obtain blood samples than aneurysmal specimens. In this category of studies, only three out of nine studies used data from at least two datasets [ 86 , 87 , 88 ], and six analyses were based on a single dataset [ 89 , 90 , 91 , 92 , 93 , 94 ]. Analytical methods used did not significantly differ when compared to tissue-based studies.…”

Section: Studies Based On Existing Datasetsmentioning

confidence: 99%

“…Analytical methods used did not significantly differ when compared to tissue-based studies. Two reports comprised validation cohorts [ 87 , 88 ]. Table 4 shows more details.…”

Section: Studies Based On Existing Datasetsmentioning

confidence: 99%

Transcriptomic Studies on Intracranial Aneurysms

Morga¹,

Pera

2023

Genes

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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.

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

Cited by 7 publications

References 46 publications

Immune-related genes construct and validate a risk model for aneurysm rupture leading to subarachnoid hemorrhage

Immune-related genes construct and validate a risk model for aneurysm rupture leading to subarachnoid hemorrhage

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

Transcriptomic Studies on Intracranial Aneurysms

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