Feng Anlin, Pokharel Marissa D, Liang Ying, Ma Wenli, Aggarwal Saurabh, Black Stephen M, Wang Ting
Center for Translational Science, Florida International University, Port Saint Lucie, FL 34987, USA.
Department of Environmental Health Sciences, Florida International University, Miami, FL 33199, USA.
Int J Mol Sci. 2024 Apr 22;25(8):4574. doi: 10.3390/ijms25084574.
Sepsis continues to overwhelm hospital systems with its high mortality rate and prevalence. A strategy to reduce the strain of sepsis on hospital systems is to develop a diagnostic/prognostic measure that identifies patients who are more susceptible to septic death. Current biomarkers fail to achieve this outcome, as they only have moderate diagnostic power and limited prognostic capabilities. Sepsis disrupts a multitude of pathways in many different organ systems, making the identification of a single powerful biomarker difficult to achieve. However, a common feature of many of these perturbed pathways is the increased generation of reactive oxygen species (ROS), which can alter gene expression, changes in which may precede the clinical manifestation of severe sepsis. Therefore, the aim of this study was to evaluate whether ROS-related circulating molecular signature can be used as a tool to predict sepsis survival. Here we created a ROS-related gene signature and used two Gene Expression Omnibus datasets from whole blood samples of septic patients to generate a 37-gene molecular signature that can predict survival of sepsis patients. Our results indicate that peripheral blood gene expression data can be used to predict the survival of sepsis patients by assessing the gene expression pattern of free radical-associated -related genes in patients, warranting further exploration.
脓毒症因其高死亡率和高发病率持续给医院系统带来巨大压力。减轻脓毒症对医院系统压力的一种策略是开发一种诊断/预后指标，以识别更易发生脓毒症死亡的患者。目前的生物标志物未能实现这一目标，因为它们仅具有中等诊断能力和有限的预后能力。脓毒症会扰乱许多不同器官系统中的多种通路，使得难以找到单一的强效生物标志物。然而，这些受干扰通路中的许多都有一个共同特征，即活性氧（ROS）生成增加，ROS可改变基因表达，而这种改变可能在严重脓毒症临床表现之前就已发生。因此，本研究的目的是评估与ROS相关的循环分子特征是否可作为预测脓毒症患者生存的工具。在此，我们创建了一个与ROS相关的基因特征，并利用来自脓毒症患者全血样本的两个基因表达综合数据集生成了一个可预测脓毒症患者生存的37基因分子特征。我们的结果表明，通过评估患者体内自由基相关基因的表达模式，外周血基因表达数据可用于预测脓毒症患者的生存情况，值得进一步探索。
