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.
目的： 利用生物信息学分析颅内动脉瘤（IA）的公共数据库中的表达谱芯片数据，为疾病机制研究提供重要信息。 方法： 基于数据集（GSE75436）进行加权基因共表达网络分析（WGCNA）构建其基因共表达网络，并识别枢纽基因；同时，使用在线工具DAVID对与IA具有高度相关的模块进行GO功能富集及KEGG通路分析。 结果： 筛选出三个与IA相关的模块，以及识别了棕色模块中的14个枢纽基因COL3A1、SPARC、CDH11、COL5A1、HOPX、CLEC11A、GALNT10、ADAMTS2、CEMIP、KIAA1755、COL11A1、ZIC2、CDKN2A和LINC00460；GO分析显示棕色模块主要富集在细胞外基质组织、胶原蛋白分解代谢过程、细胞黏附等生物过程；KEGG分析显示棕色模块参与了ECM-受体相互作用、局部粘连、蛋白质消化和吸收、PI3K-Akt信号通路等通路。 结论： 基于WGCNA，我们识别出了对IA发生发展至关重要的模块与枢纽基因，它们可能成为潜在的生物标志物和（或）治疗靶点。.
Keywords: Co-Expression Network; Gene; Intracranial Aneurysm; WGCNA.