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颅内动脉瘤病例中差异表达自噬相关基因的鉴定:生物信息学分析。

Identification of differentially expressed autophagy-related genes in cases of intracranial aneurysm: Bioinformatics analysis.

机构信息

Department of Neurosurgery, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Shinan District, Qingdao, Shandong 266000, China.

Department of Colorectal Surgery, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510000, China; Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, Shandong 266000, China.

出版信息

J Stroke Cerebrovasc Dis. 2024 Jun;33(6):107687. doi: 10.1016/j.jstrokecerebrovasdis.2024.107687. Epub 2024 Mar 21.

Abstract

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.

摘要

目的

最近的研究表明,自噬对于颅内动脉瘤(IA)的破裂是必不可少的。本研究旨在通过生物信息学分析,检查和验证 IA 病例中的潜在自噬相关基因(ARGs)。

方法

从基因表达综合数据库中获取两个表达谱(GSE54083 和 GSE75436)。使用 GSE75436 筛选 IA 病例中的差异表达 ARGs(DEARGs),并进行富集分析和蛋白质-蛋白质相互作用(PPI)网络分析,以确定枢纽基因和相关途径。此外,构建了基于枢纽基因的 IA 新型预测诊断特征。使用 GSE75436 中的接收器操作特征曲线(AUC)下面积评估特征的性能。

结果

总共在 GSE75436 和 GSE54083 数据集(28 个上调和 47 个下调基因)中鉴定出 75 个共表达的 DEARGs。DEARGs 的富集分析显示,与癌症和人类免疫缺陷病毒 1 感染相关的几个富含糖蛋白的术语。PPI 分析显示这些基因之间存在相互作用。枢纽 DEARGs 包括胰岛素样生长因子 1、分化簇 4、半胱氨酸天冬氨酸蛋白酶 8、Bcl-2 样蛋白 11、双突变鼠 2 同源物、Toll 样受体 4、生长因子受体结合蛋白 2、Jun 原癌基因、AP-1 转录因子亚基、缺氧诱导因子 1α和红细胞生成素 B-2。值得注意的是,该特征在区分 IA 时表现出良好的性能(AUC=0.87)。信号校准曲线显示出良好的校准。

结论

生物信息学分析鉴定出 IA 病例中的 75 个潜在 DEARGs。本研究表明,IA 受自噬影响,这可以解释 IA 的发病机制,并有助于其诊断和治疗。然而,需要进一步的实验验证研究来识别 IA 病例中的潜在 DEARGs。

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