Supplementary Material for: Uncovering of Key Pathways and miRNAs for Intracranial Aneurysm Based on Weighted Gene Co-Expression Network Analysis

Background: Intracranial aneurysm (IA) is a serious cerebrovascular disease. The identification of key regulatory genes can provide research directions for early diagnosis and treatment of IA. Methods: Initially, the miRNA and mRNA data were downloaded from the Gene Expression Omnibus database. Subsequently, the limma package in R was used to screen for differentially expressed genes. In order to investigate the function of the differentially expressed genes, a functional enrichment analysis was performed. Moreover, weighted gene co-expression network analysis (WGCNA) was performed to identify the hub module and hub miRNAs. The correlations between miRNAs and mRNAs were assessed by constructing miRNA-mRNA regulatory networks. In addition, in vitro validation was performed. Finally, diagnostic analysis and electronic expression verification were performed on the GSE122897 dataset. Results: In the present study, 955 differentially expressed mRNAs (DEmRNAs, 480 with increased and 475 with decreased expression) and 46 differentially expressed miRNAs (DEmiRNAs, 36 with increased and 10 with decreased expression) were identified. WGCNA demonstrated that the yellow module was the hub module. Moreover, 16 hub miRNAs were identified. A total of 1,124 negatively regulated miRNA-mRNA relationship pairs were identified. Functional analysis demonstrated that DEmRNAs in the targeted network were enriched in vascular smooth muscle contraction and focal adhesion pathways. In addition, the area under the curve of 16 hub miRNAs was >0.8. It is implied that 16 hub miRNAs may be used as potential diagnostic biomarkers of IA. Conclusion: Hub miRNAs and key signaling pathways were identified by bioinformatics analysis. This evidence lays the foundation for understanding the underlying molecular mechanisms of IA and provided potential therapeutic targets for the treatment of this disease.背景：颅内动脉瘤（Intracranial aneurysm, IA）是一种严重的脑血管疾病。明确其关键调控基因可为颅内动脉瘤的早期诊断与治疗提供研究方向。方法：本研究首先从基因表达综合数据库（Gene Expression Omnibus, GEO）下载miRNA与mRNA表达谱数据；随后利用R语言中的limma包筛选差异表达基因。为探究差异表达基因的功能，开展功能富集分析。此外，通过加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）识别核心模块与核心miRNA；通过构建miRNA-mRNA调控网络，评估miRNA与mRNA之间的调控相关性。同时开展体外验证实验。最终针对GSE122897数据集开展诊断分析与电子表达验证。结果：本研究共鉴定出955个差异表达mRNA（DEmRNAs，其中表达上调480个、下调475个）以及46个差异表达miRNA（DEmiRNAs，其中表达上调36个、下调10个）。WGCNA分析显示黄色模块为核心模块，此外共鉴定出16个核心miRNA，最终筛选得到1124对负调控的miRNA-mRNA调控关系对。功能富集分析结果表明，靶向调控网络内的差异表达mRNA显著富集于血管平滑肌收缩与黏着斑通路。同时，16个核心miRNA的曲线下面积（area under the curve, AUC）均大于0.8，提示这16个核心miRNA可作为颅内动脉瘤潜在的诊断生物标志物。结论：本研究通过生物信息学分析鉴定出核心miRNA与关键信号通路，为阐明颅内动脉瘤潜在的分子机制奠定了理论基础，同时为该疾病的治疗提供了潜在的靶向治疗靶点。