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加权基因共表达网络分析揭示了与 COVID-19 患者年龄相关表型相关的 T 细胞分化。

Weighted gene co-expression network analysis revealed T cell differentiation associated with the age-related phenotypes in COVID-19 patients.

机构信息

Medical Laboratory Centre, Life Sciences Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China.

Department of Biochemistry and Molecular Biology, School of Basic Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.

出版信息

BMC Med Genomics. 2023 Mar 25;16(1):59. doi: 10.1186/s12920-023-01490-2.

Abstract

The risk of severe condition caused by Corona Virus Disease 2019 (COVID-19) increases with age. However, the underlying mechanisms have not been clearly understood. The dataset GSE157103 was used to perform weighted gene co-expression network analysis on 100 COVID-19 patients in our analysis. Through weighted gene co-expression network analysis, we identified a key module which was significantly related with age. This age-related module could predict Intensive Care Unit status and mechanical-ventilation usage, and enriched with positive regulation of T cell receptor signaling pathway biological progress. Moreover, 10 hub genes were identified as crucial gene of the age-related module. Protein-protein interaction network and transcription factors-gene interactions were established. Lastly, independent data sets and RT-qPCR were used to validate the key module and hub genes. Our conclusion revealed that key genes were associated with the age-related phenotypes in COVID-19 patients, and it would be beneficial for clinical doctors to develop reasonable therapeutic strategies in elderly COVID-19 patients.

摘要

新型冠状病毒病 2019(COVID-19)引起的严重病情的风险随着年龄的增长而增加。然而,其潜在机制尚未得到明确理解。本研究使用数据集 GSE157103 对 100 例 COVID-19 患者进行加权基因共表达网络分析。通过加权基因共表达网络分析,我们确定了一个与年龄显著相关的关键模块。这个与年龄相关的模块可以预测重症监护病房状态和机械通气使用,并富含 T 细胞受体信号通路的正向调节等生物学进程。此外,还鉴定出 10 个枢纽基因作为年龄相关模块的关键基因。建立了蛋白质-蛋白质相互作用网络和转录因子-基因相互作用。最后,使用独立数据集和 RT-qPCR 验证了关键模块和枢纽基因。我们的结论表明,关键基因与 COVID-19 患者的年龄相关表型相关,这有助于临床医生为老年 COVID-19 患者制定合理的治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a757/10040098/b7453599e217/12920_2023_1490_Fig1_HTML.jpg

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