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.


Introduction
The most common cause of spontaneous subarachnoid hemorrhage (SAH) is a rupture of an intracranial aneurysm (IA). This form of hemorrhagic stroke comprises about 5% of all strokes. Despite overall improvements in patients' care, SAH is burdened with high mortality (approximately 50%) and disability rates-only 25% of patients who survived are likely to live independently. Most SAH patients have permanent neurological and cognitive deficits and remain dependent [1,2]. The prognosis is heavily influenced by the development of vasospasm and delayed cerebral ischemia (DCI). Vasospasm, which can be detected in approximately two thirds of SAH patients, may lead to DCI and subsequent neurological impairment. The incidence of spontaneous SAH is 8 persons in 100,000 personyears. The prevalence of unruptured IAs (UAs) in the general population is estimated at 3%. Most of the UAs remain unruptured since the risk of aneurysmal rupture is about 1% per year [3,4]. Unfortunately, it is still impossible to predict the fate of a particular IA. So far, only some risk factors of IA presence, growth, and rupture have been identified (for instance: female sex, hypertension, smoking, IA location, IA size), and based on them, a risk of an IA rupture can be estimated [4].
Molecular mechanisms underlying IA formation and rupture remain not fully recognized. Similarly, the knowledge about molecular drivers of systemic responses to the rupture of an IA is incomplete.
One of the approaches to investigate these aspects of IA pathobiology is to analyze alterations in RNA expression associated with the presence of IAs, their status (ruptured vs. unruptured), and sequels of SAH. The first studies focused on mRNA as a molecule containing the genetic information which is translated into proteins. However, over time, non-coding RNAs and RNA regulatory networks drew attention as well. Depending on the underlying question, RNA expression was analyzed in various samples, such as: IA wall, peripheral blood cells, and serum/plasma. The first broad gene expression profiling was performed by Peters et al. by means of the SAGE-Lite method in a single patient [5]. Afterwards, a microarray approach was used, subsequently replaced by RNA sequencing (RNAseq). In addition, by developing new bioinformatics tools, there is an increasing number of studies in which available original data are re-analyzed and/or existing datasets are combined.
In this review, we will focus on transcriptomics studies conducted on human-derived samples obtained from patients with IA. Firstly, a concise overview of original transcriptomics studies will be presented. Then, a brief summary of studies in which existing datasets were used (secondary studies) will be provided. A literature review was performed using PubMed and Web of Science. The search terms were "intracranial aneurysms", "cerebral aneurysm", "brain aneurysm" AND "gene expression", and "RNA expression". The identified reports were manually checked to select only transcriptomics studies on human-derived samples.

Original Studies
We identified 27 original studies which investigated RNA expression in the aneurysmal wall. Seven studies were focused on the mechanisms associated with IA rupture, eighteen on aneurysm formation, and in two reports alterations in RNA expression were analyzed both in present IAs and after their rupture. In studies on blood-derived samples, the corresponding numbers were the following: 24 studies, among them: 9 focused on the rupture-related changes (and complications of SAH in 2 studies), 10 on the IA presence, and in 5 studies, markers of IA formation and rupture were investigated.
The first published analysis of global gene expression profiles in aneurysmal tissue was performed using the SAGE-Lite technique. Samples were obtained from a single patient-a 3-year-old girl with SAH: walls of a ruptured IA (RA) and a control vessel-the superficial temporal artery (STA). The analysis comprised 4924 and 3552 genes in the RA and STA samples, respectively, and revealed an overexpression of genes related to extracellular matrix, cell adhesion, and cell migration [5]. In subsequent studies, these two elements, i.e., differential expression of RNAs and their functional annotation, remained the core of the performed analyses. From non-coding RNAs, miRNAs were the most investigated class with or without a concomitant profiling of mRNAs [8,[14][15][16]25,26]. In four studies, expression of lncRNAs was analyzed [16,17,20,30], and in two, circular RNA (circRNA) [22,31]. When mRNA expression was not directly measured, mRNA target prediction analysis was provided. Attempts to compare results of expression data on a single RNA molecule level are rather disappointing. For instance, Roder et al. in their meta-analysis of 5 microarraybased IA studies found that only 57 out of 507 reported differentially expressed genes (DEGs) were identified in more than 2 studies [32]. However, while looking at the functional annotations of differentially expressed RNAs, categories related to inflammatory reaction, immune system, cellular adhesion, extracellular matrix, muscles, apoptosis, and cellular signaling were identified as key players in the pathophysiology of IAs. Details are summarized in Table 1.

Studies Based on Existing Datasets
We identified 27 secondary studies which used datasets with RNA expression in the aneurysmal wall. Eighteen studies were focused on the mechanisms associated with IA rupture, twelve on aneurysm formation, and in nine, alterations in RNA expression were analyzed both in present IAs and after their rupture. In studies on datasets with bloodderived samples, the corresponding numbers were following: nine studies, among them: eight focused on the rupture-related changes, and one focused on the IA presence.

Transcriptomics in IA Samples
Along with the development of bioinformatic tools appeared a new type of study presenting re-analyzed data from available datasets, including expression data from the Gene Expression Omnibus (GEO). Approximately one third of these published secondary analyses utilized a single dataset [58][59][60][61][62][63][64][65][66] and two thirds leveraged data from two to eight datasets [67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85]. These studies did not provide any new additional clinical data but rather aimed to deepen the insight into molecular mechanisms of the IA pathophysiology by revealing key regulatory networks and interactions between investigated molecules. Although differential expression and functional annotation were examined, further analyses of co-expression networks with identification of hub RNA molecules, competing endogenous RNA (ceRNA) networks, or protein-protein interaction (PPI) networks became a standard approach. In some of these studies, specific areas of interest were predefined, such as: epithelial-mesenchymal transition [78], endoplasmic reticulum stress [81], immune environment [79,83], or ferroptosis [84,85]. In three studies, an attempt was made to identify potential therapeutic targets [71,82,83]. Sun et al. investigated expression profiles and networks in various aneurysms, including thoracic and abdominal aorta aneurysms [77]. More detailed information about this group of studies is provided in Table 3.         ; GO-BP up: immune response, immune system process, cell activation, leukocyte degranulation, secretion by cell; GO-MF up: cargo receptor activity, Toll-like receptor binding, identical protein binding, Rac GTPase binding, cytokine binding; GO-CC up: plasma membrane, cell periphery, secretory granule, vesicle, membrane; GO-BP down: multicellular organismal process, system development; IPA networks: Cellular development, cellular function and maintenance, cellular growth and proliferation; carbohydrate metabolism, cellular movement, hematological disease; cell-to-cell signaling and interaction, hematological system development and function, hypersensitivity response; carbohydrate metabolism, cell morphology, inflammatory response; cellular development, cellular growth and proliferation, hematological system development and function; in validation-common genes/protein: ALDH1A1, HMOX1, PPIF, TYMP    ITLN1, RBPMS2, MYOT, ACTAI); GO-BP: inflammatory response, defense response, immune response, muscle system process, muscle contraction; KEGG: chemokine signaling pathway, rheumatoid arthritis, glycine, serine and threonine metabolism, Toll-like receptor signaling pathway; WGCNA modules (pathways): blue (immune response, inflammatory response, leukocyte activation, chemokine signaling pathway, Toll-like receptor signaling pathway), pink (inflammatory response, defense response, myeloid leukocyte cytokine production, cytokine production involved in immune response, regulation of mast cell cytokine production, regulation of inflammatory response, positive regulation of immune system process, leukocyte-mediated immunity, mast cell cytokine production, regulation of intreleukin-10 production, complement and coagulation cascades); Hub genes: blue module: CCR5, CCL20, pink module:

Conclusions
In the last decade, the number of studies focused on different aspects of transcriptomics in IAs significantly increased (Figure 1). This is associated with the technology development and bioinformatics allowing to analyze big data. OT, original studies using IA wall; OB, original studies using blood-derived samples; ST, secondary studies using tissue-derived data; SB, secondary studies using blood-derived data.
However, there are so many open questions regarding the pathophysiology of IAs and molecular mechanisms underlying the consequences of IA rupture. After more than 20 years of studies on the expression of coding and non-coding RNAs, it is obvious that there is not one single pathway responsible for IA formation or rupture. However, there are some networks, some groups of genes, that seem to play important role, such as immune/inflammatory response, extracellular matrix-or focal adhesion-related, cellular signaling, regulated cell death, and muscles. These terms are consistently repeated in presented studies, although in studies on blood-derived samples the most common identified pathways are those related to the immune/inflammatory response, cell death, or cellular metabolic processes. Secondary studies based on existing datasets echo these findings.
The existing expression studies are burdened with several limitations. These are human studies and not all factors that can affect gene expression are controllable and comparable between studied groups, including comorbidities, medications, and lifestyle habits. Next, time between sampling and placement of the sample on ice or transportation/storage solutions may impact expression measurements. Furthermore, the quality of the sample is important-what is the composition of the vessel/aneurysmal wall? For instance, there are acellular or hypocellular areas in some ruptured aneurysms. Moreover, the presence of even residual amounts of blood elements on the tissue will influence the results of expression analyses. Another important issue is the choice of the control tissue. In most studies, IAs and controls were obtained from different individuals. Some researchers used intracranial vessels (e.g., cortical arteries or AVM feders), whereas others used extracranial arteries. The anatomical differences between these vessels may affect the results of expression analyses. In 2019, Laarman et al. published results of their search for optimal controls in gene expression studies on IAs [95]. In blood-derived samples, a background cell count may play an important role for the analytical output. All these elements increase the heterogeneity of analyzed samples, including the RNA types. The secondary studies that use the existing datasets rarely pay much attention to clinical variables and focus on raw expression data.
With the progress of our knowledge about the gene expression, the regulatory mechanisms of transcription, and roles played by different classes of RNA, accompanied by the development of available research tools, researchers have started to analyze the alterations in other (not mRNA) types of RNA. However, it seems that we are still at the beginning of understanding the processes underlying the pathophysiology of IAs. Very little is known about the role of small noncoding RNAs other than microRNA. We do not even understand what the significance is of altered expression of gene isoforms. Further studies are needed to explain the role of gene expression and RNA molecules in the pathobiology of IAs and the consequences of their rupture. These studies cannot be limited to a pure transcriptomic analysis. Functional analyses using experimental approaches both in vitro and in vivo are needed to test the results from expression studies in a more complex environment of living cells or whole organisms.