Suppr超能文献

鉴定一种预测脓毒症28天死亡率的三基因特征。

Identification of a 3-gene signature predicting 28-day mortality for sepsis.

作者信息

Zeng Yiqian, Liao Yutian, Wang Yang, Peng Suna

机构信息

Department of Trauma Intensive Care Unit, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, China.

Department of Trauma Center, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, China.

出版信息

Medicine (Baltimore). 2025 Sep 5;104(36):e44088. doi: 10.1097/MD.0000000000044088.

Abstract

Sepsis often leads to unpredictable consequences. The prognosis of sepsis has not been largely improved. We tried to construct a prognostic gene model related to the 28-day mortality of sepsis to identify the risk of mortality and improve the outcome early. We identified the modules associated with 28-day mortality by weighted gene co-expression network analysis from the microarray data of GSE65682. Protein-protein interaction network analysis and univariate Cox regression were conducted to identify hub genes for constructing a prognostic model. Finally, the model was evaluated for robustness. The correlation between the model and immune cells was investigated. The cyan module has a significant negative relationship with 28-day mortality. A risk model was developed to predict prognosis, utilizing macrophage expressed gene 1, CX3C chemokine receptor 1, and human leukocyte antigen-DRB1. The model's expression was found to be higher in the group with lower risk, while the group with higher risk had a higher 28-day mortality rate. These findings were validated using both the test and whole sets. Three genes were positively associated with monocyte expression. We constructed a septic prognostic model with 3 genes, including macrophage expressed gene 1, CX3C chemokine receptor 1, and human leukocyte antigen-DRB1. The expression of them had a significant negative relationship with the 28-day mortality and may influenced monocyte function.

摘要

脓毒症常常导致不可预测的后果。脓毒症的预后在很大程度上并未得到改善。我们试图构建一个与脓毒症28天死亡率相关的预后基因模型,以识别死亡风险并尽早改善预后。我们通过对GSE65682芯片数据进行加权基因共表达网络分析,确定了与28天死亡率相关的模块。进行蛋白质-蛋白质相互作用网络分析和单变量Cox回归以识别用于构建预后模型的枢纽基因。最后,对该模型的稳健性进行了评估。研究了该模型与免疫细胞之间的相关性。蓝色模块与28天死亡率呈显著负相关。利用巨噬细胞表达基因1、CX3C趋化因子受体1和人类白细胞抗原-DRB1开发了一个预测预后的风险模型。发现该模型在低风险组中的表达较高,而高风险组的28天死亡率较高。这些发现通过测试集和全集均得到了验证。三个基因与单核细胞表达呈正相关。我们构建了一个包含巨噬细胞表达基因1、CX3C趋化因子受体1和人类白细胞抗原-DRB1这三个基因的脓毒症预后模型。它们的表达与28天死亡率呈显著负相关,并且可能影响单核细胞功能。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验