用于识别脓毒症发生和进展新生物标志物的基因共表达网络分析的逐步构建
Step-by-Step Construction of Gene Co-Expression Network Analysis for Identifying Novel Biomarkers of Sepsis Occurrence and Progression.
作者信息
Yu Xianqiang, Qu Cheng, Ke Lu, Tong Zhihui, Li Weiqin
机构信息
Medical School, Southeast University, Nanjing, People's Republic of China.
Department of Critical Care Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China.
出版信息
Int J Gen Med. 2021 Sep 24;14:6047-6057. doi: 10.2147/IJGM.S328076. eCollection 2021.
BACKGROUND
Sepsis is the leading cause of death in critically ill patients. Although it is well known that the immune system plays a key role in sepsis, exactly how it works remains unknown.
METHODS
In our study, we used weighted gene co-expression network analysis (WGCNA) to screen out the immune-related genes that may play a critical role in the process of sepsis.
RESULTS
A total of three sepsis-related hub genes were screened for further verification. Subsequent analysis of immune subtypes suggested their potential predictive effect in the clinic.
CONCLUSION
Our study shows that three immune-related genes CHMP1A, MED15 and MGAT1 are important biomarkers of sepsis. The screened genes may help to distinguish normal individuals from patients with different degrees of sepsis.
背景
脓毒症是危重症患者死亡的主要原因。尽管免疫系统在脓毒症中起关键作用已为人所知，但其具体作用机制仍不清楚。
方法
在我们的研究中，我们使用加权基因共表达网络分析（WGCNA）筛选出可能在脓毒症过程中起关键作用的免疫相关基因。
结果
共筛选出三个与脓毒症相关的核心基因进行进一步验证。随后对免疫亚型的分析表明它们在临床上具有潜在的预测作用。
结论
我们的研究表明，三个免疫相关基因CHMP1A、MED15和MGAT1是脓毒症的重要生物标志物。筛选出的基因可能有助于区分正常个体和不同程度脓毒症患者。
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