Bioinformatic Identification of Differentially Expressed Genes and Pathways in Intracranial Aneurysm

Research Article

Austin J Cerebrovasc Dis & Stroke. 2021; 8(1): 1087.

Bioinformatic Identification of Differentially Expressed Genes and Pathways in Intracranial Aneurysm

Tian Q, Han S, Zhang W, Gong P, Xu Z, Chen Q and Li M*

Department of Neurosurgery, Renmin Hospital of Wuhan University, China

*Corresponding author: Mingchang Li, Department of Neurosurgery, Renmin Hospital of Wuhan University, 99-Ziyang Road, Wuhan, Huibei Province, 430060, China

Received: February 27, 2021; Accepted: March 24, 2021; Published: March 31, 2021


Background: Intracranial Aneurysm (IA) is a serious disease with high mortality and high morbidity rates, but the pathophysiological mechanisms of IA remain unclear. This study aimed to identify the Differentially Expressed Genes (DEGs) between IA tissues and Superficial Temporal Artery (STA) tissues using bioinformatic analysis.

Methods: To investigate the key genes that are important for IAs, we analyzed microarray datasets (GSE75436) from the Gene Expression Omnibus (GEO) database, including 15 IA samples and 15 normal STA samples. First, we used the GEO2R tool to screen for DEGs (P-value<0.01 and |log2 FC| ≥2) between IA and STA tissues. Subsequently, the Database for Annotation, Visualization, and Integrated Discover software was used to perform function and pathway enrichment analyses. Finally, protein-protein interaction network analysis was performed using the Search Tool for Retrieval of Interacting Genes and Cytoscape software. Real-Time Quantitative Polymerase Chain Reaction (RT-QPCR) was performed to prove our assumption.

Results: A total of 829 DEGs, of which 399 were upregulated and 430 were downregulated, were identified. The upregulated genes were mostly associated with Staphylococcus aureus infection, amoebiasis, rheumatoid arthritis, phagocytosis, and tuberculosis. The downregulated genes were mainly involved in vascular smooth muscle contraction, calcium signaling, histidine metabolism, cGMP-PKG signaling, and cAMP signaling. From the DEGs, five genes were selected as hub genes on the basis of the connection degree, which is one of 12 calculation methods from a plugin of Cytoscape called cytoHubba. The PCR results demonstrated that the expression levels of the top five hub genes, namely, Tumor Necrosis Factor (TNF), interleukin 8 (IL-8), Protein Tyrosine Phosphatase Receptor Type C (PTPRC), interleukin 1β (IL-1β), and Toll-like receptor 4 (TLR 4), were significantly higher in the IA samples than in the STA samples.

Conclusion: TNF showed higher expression in the IA samples than in the STA samples. Thus, this gene may be involved in the occurrence and development of IA. The immune response and inflammation play important roles in the progression of IA. However, the specific pathophysiological mechanism needs further study.

Keywords: Bioinformatics; differentially expressed genes; pathway; intracranial aneurysm


Intracranial Aneurysms (IAs) are abnormal protrusions occurring on the wall of the intracranial arteries, with a prevalence of 3% to 5% in the general population; the risk of rupture is nearly 1% [1,2]. The rupture of IAs causes aneurysmal Subarachnoid Hemorrhage (aSAH), which has high mortality and high morbidity rates. Despite immediate treatment upon diagnosis, aSAH is still fatal in 30-40 % of cases [3,4]. Treatment of IAs before rupture has been advocated for reducing its morbidity and mortality. With the development of neuroimaging techniques, patients with incidental IAs are identified more frequently. While the mechanism of aneurysm formation is still poorly understood by researchers, many factors, such as older age, hypertension, smoking, larger size of aneurysm, family history of IAs and irregular morphology, have been demonstrated to be involved in the increased formation of aneurysms [5]. However, there are still some limitations to those predictive models due to the incomplete understanding of the true molecular mechanisms responsible for aneurysm formation. More insight into the pathomechanism of aneurysm formation and development will facilitate clinical management choices and provide more treatment options in the future. Therefore, the pathogenesis of IAs must be elucidated, and molecular markers for the diagnosis and screening of IAs must be identified to provide a new target for the prevention and treatment of IAs.

Several studies have indicated that many genes are involved in the progression of IAs. For example, in humans, genes related to the muscle system and cell adhesion in IA are downregulated compared with those in normal intracranial arteries. The immune/ inflammatory system has been reported to be associated with IAs, but the exact mechanism remains to be elucidated [3]. Another study provided strong evidence for MHC class II gene overexpression in human IA tissue and demonstrated that antigen-presenting cells (macrophages, monocytes) play a key role in IA formation [6]. The regulation of apoptosis-related genes, such as NOL1; immune system development-associated genes, such as CD40LG and CD40; and neuron projection development-associated genes, such as STRN, in vascular smooth muscle cells may be involved in the formation of IAs [7]. However, the current study only partially reported the mechanism of IA, and more research on IA is required.

In the present study, we analyzed a microarray dataset (GSE75436) containing 15 IA tissues and 15 STA tissues using a series of bioinformatic methods to discover key IA-related genes, which will provide a potential therapeutic strategy and offer a direction for further research.

Materials and Methods

Source of gene expression data

We downloaded the GSE75436 gene expression profile of IA from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO, GSE75436 was based on the GPL570 platform ([HG-U-133_PLUS_2] Affymetrix Human Genome U133 Plus 2. 0 Array), and 30 samples were obtained from the database, including 15 IA samples and 15 STA samples. All samples were utilized in our study.

Data processing of Differentially Expressed Genes (DEGs). GEO2R ( is an online analysis tool that allows users to compare multiple groups of samples in a GEO series. It was used to analyze most GEO series data with gene symbols to identify the DEGs between IA and STA samples. Adjusted P-value<0.01 and |log2fold change| >2 were set as screening thresholds in this study.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs

The Database for Annotation, Visualization, and Integrated Discovery (DAVID), which is a common tool for online biological analysis, was used to analyze GO annotation and KEGG pathway enrichment of the DEGs in the study. The GO and KEGG pathway analyses of DEGs utilized a cut-off criterion of P<0.01. DAVID was used to analyze the core Biological Processes (BPs), Molecular Functions (MFs), Cellular Components (CCs), and pathways among these DEGs.

Network construction of a Protein-Protein Interaction (PPI) network

PPI network analysis is helpful for studying the molecular mechanism of disease and discovering new drug targets from a systematic perspective. The interaction among proteins encoded by DEGs in this study was analyzed with the Search Tool for the Retrieval of Interacting Genes (STRING; tool. An interaction score >0.4 was used to select upregulated and downregulated genes. Subsequently, Cytoscape software was used to visualize the data downloaded from the STRING database. For further analysis, Cytoscape software was utilized to calculate the protein nodes. In this study, 10 genes were selected from the DEGs as hub genes on the basis of the connection degree, which is one of 12 calculation methods from a plugin of Cytoscape called cytoHubba.

Sample preparation and RNA isolation

This study was approved by the Ethics Committee of Renmin Hospital of Wuhan University (Ethical Application Ref: WDRY2019-K090). IA tissues were acquired from patients who underwent microneurosurgery at Renmin Hospital of Wuhan University from 2018 to 2019. Matched Superficial Temporal Artery (STA) tissues were obtained from each patient as a control. Informed consent was signed by all patients. The Institutional Ethics Committee of Renmin Hospital of Wuhan University approved this study in accordance with the principles presented in the Declaration of Helsinki. The tissue samples comprised three IA tissues and three STA tissues. Collected tissues were immediately washed with saline and then transferred to a liquid nitrogen tank and stored at -80ºC until RNA extraction. RNA was isolated from IA and STA tissues by using a TRIzol-based RNA isolation protocol.

Quantification of RNAs by quantitative polymerase chain reaction

RNA was quantified using a NanoDrop spectrophotometer, and 10 ng of the total RNA was reverse transcribed using the TaqMan RNA Reverse Transcription Kit (Applied Biosystems) in accordance with the manufacturer’s protocol. The top five genes (TNF, IL-8, PTPRC, IL-1β, and TLR4) were detected using TaqMan RNA assays (Applied Biosystems) on a 7500 HT quantification real-time polymerase chain reaction instrument (Applied Biosystems). GAPDH was used as an internal control. For all RNAs, a Ct value >35 was defined as undetectable. Relative mRNA expression levels were analyzed by the 2-ΔΔcycle threshold method.


Data of DEGs

On the basis of the previous standard (adjusted P-value <0.01 and |log2fold change| >2), 829 DEGs, of which 399 were upregulated and 430 were downregulated, were identified between the IA and STA samples in our study. The volcano plots (Figure 1A) showed the DEGs were able to distinguish between the IA and STA samples. The top 10 upregulated and downregulated genes are shown in Table 1.