Bioinformatics analysis of gene expression profile data to screen key genes involved in intracranial aneurysms

Guo,Tie; Hou,Dan; Yu,Dan

doi:10.3892/mmr.2019.10696

Bioinformatics analysis of gene expression profile data to screen key genes involved in intracranial aneurysms

Authors:
- Tie Guo
- Dan Hou
- Dan Yu
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Affiliations: Department of Neurology, Haikou Hospital Affiliated to Xiangya School of Medicine, Central South University, Haikou, Hainan 570208, P.R. China
Published online on: September 23, 2019 https://doi.org/10.3892/mmr.2019.10696
Pages: 4415-4424
Copyright: © Guo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Intracranial aneurysm (IA) is a cerebrovascular disease with a high mortality rate. The pathogenesis of IA remains unclear and the treatment limited. The purpose of the present study was to identify the key genes expressed in IAs and provide the basis for further research and treatment. The raw dataset GSE75436 was downloaded from Gene Expression Omnibus, including 15 IA samples and 15 matched superficial temporal artery (STA) samples. Then, differentially expressed genes (DEGs) were identified using the limma package in R software. Hierarchical clustering analysis was performed on the DEGs using the gplot2 package in R. Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tools were used to perform gene ontology (GO) functional enrichment analysis. DAVID and gene set enrichment analysis were separately used to perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The intersections of the two results were selected as common KEGG pathways. Protein‑protein interaction (PPI) analysis among the DEGs involved in the common KEGG pathways was performed using Search Tool for the Retrieval of Interacting Genes online tools, and visualized with Cytoscape software. A total of 782 DEGs were identified, comprising 392 upregulated and 390 downregulated DEGs. Hierarchical clustering demonstrated that the DEGs could precisely distinguish the IAs from the STAs. The GO enrichment analysis demonstrated that the upregulated DEGs were mainly involved in the inflammatory response and the management of extracellular matrix, and the downregulated DEGs were mainly involved in the process of vascular smooth muscle contraction. The KEGG pathway enrichment analysis demonstrated that the common pathways were ‘leishmaniasis’, ‘Toll‑like receptor signaling pathway’ and ‘vascular smooth muscle contraction’. In the PPI network, tumor necrosis factor (TNF), interleukin 8 and Toll‑like receptor 4 had the highest degrees; they were associated with the inflammatory response. The Toll‑like receptor signaling pathway and TNF gene may serve as targets for future research and treatment.

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