揭示枢纽代谢相关基因的潜在作用及其与免疫细胞在急性缺血性中风中的相关性。

IF 1.9 4区 生物学 Q4 CELL BIOLOGY IET Systems Biology Pub Date : 2024-06-08 DOI:10.1049/syb2.12095
Xianjing Zhang, Tengxiao Xu, Chen Wang, Yueyue Lin, Weimi Hu, Maokui Yue, Hao Li
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引用次数: 0

摘要

目的:急性缺血性脑卒中(AIS)是由于血管内血栓形成导致脑缺血引起的。本研究的目的是确定与代谢相关的关键基因,以帮助 AIS 的机制研究和治疗:基因表达数据从基因表达总库数据库下载。加权基因共表达网络分析、基因本体和京都基因和基因组百科全书分析用于识别可能参与调控AIS的代谢相关基因。基于 STRING 数据库,使用 Cytoscape 绘制了蛋白质相互作用网络图。随后,基于Cytoscape-CytoNCA和Cytoscape-MCODE插件确定了枢纽代谢相关基因。采用最小绝对收缩和选择算子算法以及差异表达分析。此外,还进行了药物预测、分子对接、ceRNA 网络构建以及与免疫细胞浸润的相关性分析,以探索其在 AIS 中的潜在分子作用机制。最后,通过实时 PCR 验证了枢纽基因的表达:结果:发现了代谢相关基因FBL、HEATR1、HSPA8、MTMR4、NDUFC1、NDUFS8和SNU13。FBL、HEATR1、HSPA8、MTMR4、NDUFS8和SNU13的AUC值均大于0.8,表明它们具有良好的诊断准确性。相关分析发现,它们的表达水平还与活化的 CD8 T 细胞和活化的树突状细胞等多种免疫细胞的浸润水平有关。研究发现,只有 HSPA8 与有文献支持的药物成功匹配,这些药物是对乙酰氨基酚、布比卡因、地塞米松、庆大霉素、曲替诺和顺铂。此外,还发现ENSG000000218510-hsa-miR-330-3p-HEATR1轴可能参与调控AIS:结论:FBL、HEATR1、HSPA8、MTMR4、NDUFC1、NDUFS8和SNU13的鉴定为探索AIS的分子机制提供了新的研究方向,有助于临床管理和诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Revealing the potential role of hub metabolism-related genes and their correlation with immune cells in acute ischemic stroke

Objectives

Acute ischemic stroke (AIS) is caused by cerebral ischemia due to thrombosis in the blood vessel. The purpose of this study is to identify key genes related to metabolism to aid in the mechanism research and management of AIS.

Materials and Methods

Gene expression data were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis, Gene Ontology and kyoto encyclopedia of genes and genomes analysis were used to identify metabolism-related genes that may be involved in the regulation of AIS. A protein protein interaction network was mapped using Cytoscape based on the STRING database. Subsequently, hub metabolism-related genes were identified based on Cytoscape-CytoNCA and Cytoscape-MCODE plug-ins. Least absolute shrinkage and selection operator algorithm and differential expression analysis. In addition, drug prediction, molecular docking, ceRNA network construction, and correlation analysis with immune cell infiltration were performed to explore their potential molecular mechanisms of action in AIS. Finally, the expression of hub gene was verified by real-time PCR.

Results

Metabolism-related genes FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 were identified. The AUC values of FBL, HEATR1, HSPA8, MTMR4, NDUFS8 and SNU13 were all greater than 0.8, suggesting that they had good diagnostic accuracy. Correlation analysis found that their expression levels were also related to the infiltration levels of multiple immune cells, such as Activated.CD8.T.cell and Activated.dendritic.cell. It was found that only HSPA8 was successfully matched to drugs with literature support, and these drugs were acetaminophen, bupivacaine, dexamethasone, gentamicin, tretinoin and cisplatin. Moreover, it was also identified that the ENSG000000218510-hsa-miR-330-3p-HEATR1 axis may be involved in regulating AIS.

Conclusions

The identification of FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 provides a new research direction for exploring the molecular mechanisms of AIS, which can help in clinical management and diagnosis.

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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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