Related Experiment Video
Updated: Jun 30, 2025
A Visual Description of the Dissection of the Cerebral Surface Vasculature and Associated Meninges and the Choroid Plexus from Rat Brain
Published on: November 14, 2012
Identification of differentially expressed autophagy-related genes in cases of intracranial aneurysm: Bioinformatics
Han Zhou1, Yancheng Song2, Chao Wang1
1Department of Neurosurgery, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Shinan District, Qingdao, Shandong 266000, China.
Objective:
Recent research indicates that autophagy is essential for the rupture of intracranial aneurysm (IA). This study aimed to examine and validate potential autophagy-related genes (ARGs) in cases of IA using bioinformatics analysis.
Methods:
Two expression profiles (GSE54083 and GSE75436) were obtained from the Gene Expression Omnibus database. Differentially expressed ARGs (DEARGs) in cases of IA were screened using GSE75436, and enrichment analysis and Protein-Protein Interaction (PPI) networks were used to identify the hub genes and related pathways. Furthermore, a novel predictive diagnostic signature for IA based on the hub genes was constructed. The area under the Receiver Operating Characteristic curve (AUC) was used to evaluate the signature performance in GSE75436.
Results:
In total, 75 co-expressed DEARGs were identified in the GSE75436 and GSE54083 dataset (28 upregulated and 47 downregulated genes). Enrichment analysis of DEARGs revealed several enriched terms associated with proteoglycans in cancer and human immunodeficiency virus 1 infection. PPI analysis revealed interactions between these genes. Hub DEARGs included insulin-like growth factor 1, clusters of differentiation 4, cysteine-aspartic acid protease 8, Bcl-2-like protein 11, mouse double mutant 2 homolog, toll-like receptor 4, growth factor receptor-bound protein 2, Jun proto-oncogene, AP-1 transcription factor subunit, hypoxia inducible factor 1 alpha, and erythroblastic oncogene B-2. Notably, the signature showed good performance in distinguishing IA (AUC = 0.87). The sig calibration curves showed good calibration.
Conclusion:
Bioinformatic analysis identified 75 potential DEARGs in cases of IA. This study revealed that IA is affected by autophagy, which could explain the pathogenesis of IA and aid in its diagnosis and treatment. However, future research with experimental validation is necessary to identify potential DEARGs in cases of IA.
