Research Article

Screening of key genes of unruptured intracranial aneurysms by using DNA microarray data analysis techniques

Published: January 31, 2014
Genet. Mol. Res. 13 (1) : 758-767 DOI: 10.4238/2014.January.31.2

Abstract

This study aimed to identify differentially expressed genes (DEGs) of unruptured intracranial aneurysms (IAs) and provide beneficial information for early diagnosis and treatment of IAs. The gene expression profile GSE26969 from the Gene Expression Omnibus database was downloaded, which included six human IA samples: three intracranial arterial aneurysm samples and three normal superficial temporal artery samples (control). Based on these data, we identified the DEGs between normal and disease samples with packages in the R language. The selected DEGs were further analyzed using bioinformatic methods. First, the STRING software was used to search co-expression relationships among DEGs, and the most important hub gene was found. We then used the plugins of the Cytoscape software, Mcode and Bingo, to conduct a module analysis. Next, pathways of the module genes were annotated using the FuncAssociate program. Compared with the control group, we obtained 169 DEGs in total, and by mining a module with the hub gene MYH11, we retrieved the ACTA2, MYH11, MYLK, and MYL9 genes, which were all in the module and were most significantly related to vascular smooth muscle contraction. We hypothesize that the genes identified here can be beneficial for early diagnosis and treatment of IAs.

This study aimed to identify differentially expressed genes (DEGs) of unruptured intracranial aneurysms (IAs) and provide beneficial information for early diagnosis and treatment of IAs. The gene expression profile GSE26969 from the Gene Expression Omnibus database was downloaded, which included six human IA samples: three intracranial arterial aneurysm samples and three normal superficial temporal artery samples (control). Based on these data, we identified the DEGs between normal and disease samples with packages in the R language. The selected DEGs were further analyzed using bioinformatic methods. First, the STRING software was used to search co-expression relationships among DEGs, and the most important hub gene was found. We then used the plugins of the Cytoscape software, Mcode and Bingo, to conduct a module analysis. Next, pathways of the module genes were annotated using the FuncAssociate program. Compared with the control group, we obtained 169 DEGs in total, and by mining a module with the hub gene MYH11, we retrieved the ACTA2, MYH11, MYLK, and MYL9 genes, which were all in the module and were most significantly related to vascular smooth muscle contraction. We hypothesize that the genes identified here can be beneficial for early diagnosis and treatment of IAs.

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