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

doi:10.21203/rs.3.rs-2956995/v1

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

https://doi.org/10.21203/rs.3.rs-2956995/v1

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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.

Subarachnoid hemorrhage (SAH) is an acute vascular event that can be life-threatening. 5–10% of all strokes are SAH, which has a much lower average age than other types(1, 2). 80–85% of SAHs can be attributed to cerebral aneurysm rupture(1, 3). The incidence of SAH due to ruptured aneurysms is about 7.9 cases per 100 000 person-years(4). A study showed that the incidence of SAH decreased by 0.8% per year(5). The 90-day case fatality rate for aneurysmal subarachnoid hemorrhage (aSAH) decreased from 39–30% between 2000 and 2010(6). Although the morbidity and mortality of aSAH have declined over the past 20 years, approximately 30% of patients die and only 30% can return to independent living(7, 8). The loss of the ability to live independently of those patients will aggravate the economic burden on society(9).

Brain injury after aSAH includes early (direct) brain injury (EBI) and delayed (secondary) brain injury(10). Both stages of brain injury are major factors in poor prognoses and decreased survival in patients with aSAH (1, 11). EBI refers to direct damage to the brain that occurs between aSAH and delayed vasospasm(12). As the main complications of aSAH, cerebral vasospasm (CV) and delayed cerebral ischemia (DCI) is the primary causes of delayed brain injury(13). An early diagnostic method for aSAH is computed tomography (CT), with a sensitivity and specificity of almost 100% within the first few hours after the event(14, 15). In patients with CT-negative aSAH and within seven days of the event, diagnostic lumbar puncture and magnetic resonance imaging (MRI) can serve as a complement(16, 17). The diagnosis of aSAH, however, occurs after a pathological event, and even early intervention cannot completely prevent subsequent brain damage. However, we believe that assessing the risk of rupture of unruptured aneurysms is a more effective method. Intracranial aneurysm (IA) rupture is currently assessed using the PHASES score, which took into account age, hypertension, history of SAH, aneurysm size, location, and geographic region(18). However, patients with a PHASE score of 3 or less are also at risk for rupture, and PHASE may not provide a satisfactory guide to clinical intervention sometimes(19, 20). However, there are still risk classifier models that outperform the PHASE score(21). Certainly, more dimensions of risk assessment are beneficial to improve the accuracy of prediction. The development of molecular-level-based risk assessment methods for the occurrence of inflammation-related aSAH is still in its infancy.

The inflammatory mediators and immune cells play a pivotal role in the initiation of aSAH and subsequent brain injury. In animal models, Toll-Like Receptor 4 (TLR4)-mediated myeloid differentiation primary-response protein 88 (MyD88) has been implicated in IA rupture(22). Also, NF-B- and STAT3 signaling are involved in the rupture of the IA(23, 24). The mechanism by which lymphocytes and neutrophils promote IA rupture by affecting matrix metalloproteinases 9 (MMP9) has been demonstrated in animal models(25, 26). Research on the inflammatory response after aSAH begins with the discovery that inflammation and aSAH are related(27). The role of inflammation in post-aSAH EBI, CV, and DCI has been extensively studied(28). Neutrophils, monocytes (macrophages), and microglia are the main components of the inflammatory response after aSAH. Macrophages and matrix metalloproteinases (MMPs) are involved in the formation, progression, and rupture of IAs(29). Based on the study of the above mechanism, it can be concluded that immuno-related risk assessments of aSAH occurrence are feasible.

In this paper, we used weighted gene co-expression network analysis (WGCNA) and Gene Expression Omnibus (GEO) data set differential expressed genes to identify pivotal genes of aSAH occurrence and performed relevant analyses. We used R software to analyze the immune differences between aSAH and normal subjects and construct an immune cell-related risk model. The risk model presents appearing aSAH occurrence risk assessment capability with certain accuracy in both the training and validation sets, which has potential clinical application and enriches the risk assessment methods perspectives for aSAH occurrence. The inflammatory response regulated by hub genes provides research and risk assessment perspective for aSAH formation and subsequent neurological deterioration.

2.1. Data preparation. We obtained the aSAH-related data sets GSE36791 for training and GSE73378 for the test from the GEO database (https://www.ncbi.nlm.nih.gov/geo/)(30). GSE36791 was derived from the gene expression profiling platform GPL10558 (Illumina HumanHT-12 V4.0 expression bead chip), and GSE73378 was from GPL10558 and GPL10904 (Illumina HumanHT-12 V4.0expression bead chip). GSE36791 and GSE73378 were both whole-blood gene expression profiles. GSE36791 contained 43 samples with aneurysm rupture resulting in aSAH and 18 control samples. GSE73378 contained 112 samples with aneurysm rupture resulting in aSAH and 114 control samples.

2.2. Obtaining differentially expressed genes (DEGs) and construction of weighted gene co-expression network analysis (WGCNA). The raw data of the GSE36791 dataset was processed using R software to obtain DEGs. The differential gene screening criteria were |log2(FC)|>1 & p.adj<0.01. The volcano plot of DEGs was plotted via the R package “ggplot2”. The DEGs in different groups were shown in the heatmap(31). We constructed a WGCNA network using the R package “WGCNA” to find the common aSAH-related genes(32). The standard deviation of each gene was calculated and the former 8000 genes with larger standard deviations were taken as the basis of analysis. The abnormal samples are then eliminated by sample clustering identification. Choose the appropriate soft threshold power to make the network have the best correlation and continuity. Next, we verify that our network at that soft threshold power was consistent with the scale-free network. The soft threshold power adjacency matrix was converted into the topological overlap matrix (TOM) and then into the TOM dissimilarity matrix (1-TOM). Gene modules with correlations over 0.8 were merged by the dynamic tree-cut algorithm. Afterward, module-clinical trait relationships were analyzed. Then the most relevant module was selected for analysis. Crossover genes of WGCNA and DEGs were identified as hub genes for subsequent analysis.

2.3. Function enrichment analyses of aSAH and construction of protein-protein interaction (PPI) network. We undertook to Disease Ontology (DO) enrichment analysis of the DEGs between aSAH and the control sample from GSE36791 using the R package “DOSE”(33). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were undertaken on hub genes from the intersection of DEGs and WGCNA modules(34). The screening criterion was p-value<0.05. The PPI network of hub genes was constructed online from the STRING database (https://cn.string-db.org/) and visualized by CYTOSCAPE software(35, 36). We input the obtained hub genes into the STRING database and the lowest required interaction score was 0.15.

2.4. Construction of microRNA (miRNA)-transcription factor (TF)-messenger RNA (mRNA) regulatory network. The online databases TargetScan (https://www.targetscan.org/vert_72/), Starbase (https://starbase.sysu.edu.cn/starbase2/), and miRTarBase (https://ngdc.cncb.ac.cn/databasecommons/database/) were used to predict miRNAs associated with common genes, and the results were taken as intersections(37-40). We also used the Erichr database (https://maayanlab.cloud/Enrichr/) to predict the TF, which was associated with the above genes(41, 42). The data acquired from the database were prepared using R software. The results were visualized using CYTOSCAPE software(36).

2.5. Immune cell proportion and immune function analysis. We used the CIBERSORT and the single sample gene set enrichment analysis (ssGSEA) algorithm to compare immune cell proportion levels of the GSE36791(43, 44). The differences in immune function were further assessed using the ssGSEA algorithm. We then calculated correlations between immune cells and immune function. Then we calculated the differences in the expression of different immune cells in different groups. p-value<0.05 was considered to be statistically significant.

2.6. Construction of immune-related gene risk model for aSAH occurrence. We used the ssGSEA algorithm in R software to analyze the correlation of common genes with 28 immune cells. Gene extraction and simultaneous construction of a risk model for aSAH occurrence were performed using a randomized method. GSE36791 served as the training set and GSE73378 as the test set. The Cox regression method was used to fit the risk model. The formula is as follows:

Receiver operating characteristic (ROC) and “bootstrapping” methods were used to test the prediction efficiency and accuracy of the model. “bootstrapping” method was repeated 1000 times for robustness. The nomogram and calibration curves were plotted to assess the model fit. The magnitude of the AUC value can further reflect the risk-predictive efficacy. The box plot was used to present the differential expression of the model genes in the training set and the validation set. All plots and data analyses were done with R software.

3.1. Screening for differentially expressed genes. We provide the study analysis workflow for this study to better understand our analysis process (Figure 1). 61 samples were normalized from GSE36791, including 43 aSAH samples and 18 headache samples (Figure 2A). 37 genes with differential expression were obtained from the analysis of the two groups. The results were presented by the volcano plot (Figure 2B). 26 genes were up-regulated. The other 11 genes were down-regulated. The heatmap showed the high and low expression of differentially expressed genes among different groups (Figure 2C).

3.2. WGCNA analysis identifies key module gene sets and takes intersections with differentially expressed genes. The top 8000 genes with the largest standard deviation were calculated and obtained. One sample was identified and removed by the sample cluster identification algorithm, and the remaining 60 samples were retained for subsequent analysis. The most suitable soft threshold value was calculated to be 6 (Figure 3B). And it met the criteria for scale-free network validation (R^2>0.8 & slope<0) (Figure 3C). Modules with a similarity of being greater than 0.8 were merged using the dynamic shear tree algorithm, which was visualized using R software (Figure 3A). The correlation between modules was shown in the heatmap (Figure 3E). 400 genes were randomly selected for visualization, and the brighter the color in the heatmap, the stronger the linkage between modules (Figure 3D). Correlations between aSAH and different modules were analyzed in conjunction with clinical information (Figure 3F). We concluded that lightsteelblue1 exhibited the strongest negative correlation with aSAH (r = -0.56; p = 3e-06). Module membership and gene significance showed a strong positive correlation (Figure 3G). There were 3294 genes in the above module, and we obtained 22 shared genes by intersecting the above genes and the genes with differential expression (Figure 3H). We believed that these 22 genes played an important role in aSAH.

3.3. Functional enrichment analysis and construction of gene interaction network. We performed DO analysis of the DEGs and found that these genes were associated with Human immunodeficiency virus infectious disease, primary bacterial infectious disease, and some respiratory diseases (Figure 4A). Next, we performed GO/ KEGG analysis on 22 common genes (Figure 4B). GO analysis showed that the biological process was mainly focused on immune cell activation and differentiation. The molecular function was mainly focused on the vesicle lumen and secretory granule lumen. The cellular component indicated that DEGs were mainly related to endopeptidase regulation. KEGG analysis showed that DEGs were mainly associated with cytokine receptor interactions and viral protein-cytokine and cytokine receptor interactions. Further PPI network analysis was performed for the common genes. After removing the fragmented genes, we obtained a network with 6 highly expressed genes and 6 low-expressed genes (Figure 4C). A regulatory network containing 17 genes, 3 TFs, and 33 miRNAs were derived by using R software combined with database analysis (Figure 4D). Among them, BCL11B was associated with most miRNAs in the regulatory network. We found that 61 samples of GSE36791 were closely associated with most immune cells and immune functions, except for dendritic cells (DCs) (Figure 5C). Correlation analysis of immune cells revealed that neutrophils were negatively correlated with most immune cells (Figure 5D). And the correlation analysis between immune functions showed that most immune functions showed a positive correlation with other immune functions, while Type-II_IFN_RESPONSE showed a negative correlation with other immune functions (Figure 5E).

3.4. Immune cell proportion has an important relationship with aSAH. The CIBERSORT algorithm was able to assess the percentage of immune cells within samples. We used the CIBERSORT algorithm to conclude that the proportion of Neutrophils and Monocytes was the largest (Figure 5A). The heat map showed that neutrophil expression was up-regulated in the aSAH group compared with the control group (Figure 5B). The ssGSEA algorithm was used to further investigate the relationship between immune cells and immune function and aSAH. We performed a differential analysis of immune cells and immune function between the aSAH and control groups and found that the expressions of CD8+ T cells, plasmacytoid dendritic cells (pDCs), T helper cells, mast cells, TIL, and Th2 cells were lower in the aSAH group, while the expression of neutrophils was significantly higher in the aSAH group (Figure 5F). Immune functions including checkpoint, inflammation promotion, cytolytic activity, T-cell co-inhibition, and T-cell co-stimulation were significantly down-regulated in patients in the aSAH group (Figure 5G). We believe that the appearance of aSAH is closely related to alterations in immune function and neutrophils.

3.5. A risk model for aSAH occurrence based on immune-related gene. 22 hub genes derived from the intersection of WGCNA and DEGs were used to analyze the relevance to immune cells (Figure 6B). We randomly selected 3 genes (CD6, SLPI, SLC2A11) to construct the model and plot the nomogram, risk score = 0.1895229 + CD6*(-0.9472389) + SLC2A11*1.4073248 + SLPI*2.0979795 (Figure 6A). The calibration curve in the training set showed a good fit for the model (Figure 6C). And the AUC value of 0.859 had a high risk predictive efficacy (Figure 6D). The calibration curve also had a good fit in the validation set. The AUC value was 0.627, which also had credible risk predictive power (Figure 6E-F). Expression analysis of these 3 model genes showed that CD6 was under-expressed in both the training and validation sets of the aSAH group, but was not statistically significant in the validation set. SLC2A11 and SLPI were significantly highly expressed in both the training and validation sets of the aSAH group (Figure 6G-6L). We believe that the model had potential clinical application in risk assessment for aSAH occurrence.

Multiple complications are involved after aSAH resulting in deterioration of neurological function and poor prognosis of the patient(45). Additionally, aSAH therapy has limitations that reduce the benefit to patients. In clinical practice, nimodipine and maintenance of euvolemia are fundamental treatments of CV and DCI after aSAH(29). Numerous studies have been conducted on the treatment of CV and DCI, including calcium channel blockers, magnesium, statins, and angioplasty(46). However, only two basic therapies mentioned above were found to improve outcomes. As a result, some researchers have begun to focus on EBI, the pathological mechanism within 72 hours after aSAH. To the data, there is no specific treatment for EBI. In the current situation, even early intervention cannot entirely prevent brain damage following aSAH. Mechanistically, aSAH occurrence risk assessment for patients is the premise of Preventive intervention to avoid aSAH occurrence. Risk assessment of aSAH occurrence has potential clinical application. Previously, Yan et al. have identified seven genes as possible biomarkers of aSAH. In view of this, our study developed and validated a new immune-related model for predicting the risk of intracranial aneurysm rupture leading to SAH.

Through WGCNA and differential expression genes analysis, we identified 3294 and 37 genes, respectively, and identified 22 collective genes related to aSAH occurrence. We suspect that these 22 genes are inextricably related to aSAH. The mechanisms by which several of these genes are involved in disease have been elucidated. Among the up-regulated genes, ARG1 has been shown to be increased in microglia and macrophages in the early stages of CNS (central nervous system) injury(47, 48). S100A12 is constitutively expressed in neutrophils and is implicated in the pathological remodeling of arterial smooth muscles(49, 50). CCR7 is specifically expressed in macrophages of the M1 type, and the number of macrophages M1 expressing CCR7 increased following SAH(51–53). FER1A encodes the alpha subunit of the immunoglobulin E receptor(54). The deletion of FCER1A protects mice from developing abdominal aortic aneurysms(55). IL1R2, IL18RAP, and IL18R have also been identified as key genes in the aSAH regulatory network(56). ARG1, TPST1, S100A12, and PGLYRP1 were identified as key genes for aSAH(57, 58). Of all down-regulated genes, CCR7 and CD27 were also identified as pivotal genes for aSAH(57, 59). Therefore, we suggested that these 22 genes may be associated with immunity and inflammation and involved in the rupture of intracranial aneurysms and thus in the formation of SAH through potential mechanisms.

To more fully explore the possible involvement of DEGs in disease pathways, we performed a DO analysis of 37 DEGs. Next, we performed GO/KEGG enrichment analysis of the 22 key genes intersected, with the aim of exploring their possible involvement in the biological functions and potential mechanisms of aSAH. The results showed that the enriched pathways were mostly involved in immune response and cytokines. Roles of immune and inflammation in aSAH have been widely studied(10, 60). Macrophages, lymphocytes, neutrophils, and various inflammatory factors are involved in the initiation of aSAH. Inflammatory cascades after aSAH begin with the release of products from erythrocyte lysis, such as heme and hemin, and cause brain injury subsequently(61, 62). Aiming to clarify the potential regulatory mechanism of 22 genes in aSAH, we then used PPI network analysis to identify interacting genes of 22 genes and constructed a triple regulatory network consisting of mRNA, transcription factors, and miRNA. The results show that BCL11B interacts with most miRNAs. BCL11B contributes significantly to adaptive immune function. BCL11B regulates the differentiation and function of NK cells and CD8 + T cells(63–65). Moreover, a study found a significant increase in cerebral microbleeds in mice lacking BCL11B in vascular smooth muscle compared with mice with BCL11B(66). Based on these findings, the initiation of aSAH driven by inflammation may be mediated by a triple regulatory network. This may be the future direction of aSAH mechanism research.

Based on the above analyses, we conducted a correlation study on immune cells and function. Immune cell abundance analysis revealed that neutrophils and monocytes accounted for the largest proportion of the samples. The ssGSEA algorithm also indicates that neutrophils have higher scores in the aSAH group than immune cells. We hypothesize that monocytes and neutrophils are the major inflammatory components in the neurological deterioration after aSAH. In the acute phase of aSAH (0–24 h Post Hemorrhage), there is a dynamic infiltration of neutrophils into the CNS(60). In addition, neutrophils may have increased locally prior to the rupture of IA and may have contributed to the rupture by mediating the MMP9 production of cathepsin(67). Xu et al. found that blood-derived monocytes did not enter the CNS until at least 48 hours after SAH, before which the activated microglia take effect(68). Interestingly, lipid-accumulating foam cells were associated with IA rupture(69). This suggests a potential role for macrophages in aSAH initiation and the need to distinguish between central and peripheral sources of macrophages that form foam cells. Additionally, immune correlation analysis reveals that neutrophils are negatively correlated with most immune cells. There has been considerable interest in the predictive role of neutrophil count and neutrophil-to-lymphocyte ratio in aSAH occurrence(70, 71).

In conclusion, neutrophils, lymphocytes, and monocytes may be the initiating factors for the formation of SAH after intracranial aneurysm rupture. Accordingly, neuroinflammation, which is regulated by hub genes, may be pivotal to the progression of aSAH. The potential mechanisms of the above key genes also provide a theoretical basis for predicting the rupture of intracranial aneurysms at the molecular level. To predict the risk of aSAH occurrence in a more multidimensional way, we randomly selected 3 out of 22 immune-related genes to build a risk prediction model for aSAH occurrence. Secretory leucoprotease inhibitor (SLPI) is a type of acute phase protein expressed constitutively in inflammatory cells(72–74). Taggart et al demonstrated that SLPI can enter the nucleus of monocytes to compete for P65 binding to NF-κB sites, thereby inhibiting NF-κB pathway activation and inhibiting inflammatory responses(75, 76). In addition, there is evidence that SLPI-mediated regulation of monocyte function can inhibit T lymphocyte proliferation(77). We hypothesized that SLPI might play a protective role in monocytes during neuroinflammation during the initial phase of aSAH initiation and after aSAH. Therefore, the increased expression of SLPI is a potential risk marker of aSAH. CD6 is specifically expressed in lymphocytes, but its regulatory effect on lymphocytes has two sides(78, 79). The potential mechanism of CD6-mediated lymphocyte initiation of aSAH needs further investigation. The predictive performance and accuracy of the model were verified by using an external validation set. Results demonstrate that our aSAH occurrence-risk model has a favorable risk predictive capability.

In summary, we identified 22 key genes causing aSAH and developed and validated a novel immune-related model for predicting the risk of intracranial aneurysm rupture. Our analysis reveals potential regulatory mechanisms of pivotal genes regulating neutrophils and monocytes in the formation of SAH from ruptured intracranial aneurysms. This provided a reference and future research perspective for aSAH occurrence risk assessment. Our risk model provides a reference for guiding whether patients with UIAs should be intervened to prevent SAH, and combined with current clinical imaging and hemodynamics will provide more accurate clinical guidance.

aSAH

aneurysmal subarachnoid hemorrhage

SAH

subarachnoid hemorrhage

computed tomography

cerebral vasospasm

DCI

delayed cerebral ischemia

DCs

dendritic cells

DEGs

differentially expressed genes

Disease Ontology

EBI

early brain injury

GEO

Gene Expression Omnibus

Gene Ontology

intracranial aneurysm

KEGG

Kyoto Encyclopedia of Genes and Genomes

MRI

magnetic resonance imaging

pDCs

plasmacytoid dendritic cells

PPI

protein-protein interaction

ROC

receiver operating characteristic

SAH

subarachnoid hemorrhage

ssGSEA

single sample gene set enrichment analysis

transcription factor

TOM

topological overlap matrix

UIAs

unruptured intracranial aneurysms

WGCNA

weighted gene co-expression network analysis

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The datasets generated and analyzed during the current study are available in Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), accession number: GSE36791; GSE73378.

Competing interests

The authors declare that there is no conflict of interest regarding the publication of this article.

Funding

The present study was supported by the National Natural Science Foundation of China (No.81201991), and the Basic Research Program of Shanxi Province (No.202103021224400).

Authors' contributions

Ji Jin, Ren Li, Yang Chen contributed equally to this work. JJ, RL, and YC contributed to the conception of the study and made substantial contributions to data analysis and manuscript preparation. JJ, RL, YC, ZAL, and YQW analyzed the bioinformatics data. JJ, JZZ, and GG confirmed the authenticity of all the raw data. All authors read and approved the final manuscript.

Acknowledgements

The authors are particularly grateful for the funding from the Shanxi Institute of Intelligence Big Data Technology and Innovation Research Fund (SIBD-2020-YL0052).

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No competing interests reported.

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

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