[Construction and analysis of gene co-expression networks in intracranial aneurysm]

Objective: To analyze the expression microarray data in the public databases of intracranial aneurysms (IA) using bioinformatics, and to provide important information for the study of disease mechanisms. Methods: Gene co-expression network was constructed by weighted gene co-expression network analysis (WGCNA) based on the dataset (GSE75436) and pivot genes were identified. Using the online tool DAVID (Database for Annotation, Visualization, and Integrated Discovery) to perform GO function enrichment and KEGG path analysis on modules highly related to IA. Results: Three IA-related modules were screened out, and 14 pivot genes (COL3A1, SPARC, CDH11, COL5A1, HOPX, CLEC11A, GALNT10, ADAMTS2, CEMIP, KIAA1755, COL11A1, ZIC2, CDKN2A, and LINC00460) in the brown module were identified; the analysis of GO showed that the brown module was mainly enriched in extracellular matrix organization, extracellular matrix organization, cell adhesion and other biological processes; the analysis of KEGG indicated that the brown module involved in ECM-receptor interaction, Focal adhesion, protein digestion and absorption, PI3K-Akt signaling pathway. Conclusion: Based on WGCNA, we identified modular and pivotal genes that are critical to the development of IA, and they may become potential biomarkers and/or therapeutic targets.