Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul 07441, Republic of Korea.
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea.
Int J Mol Sci. 2023 May 27;24(11):9362. doi: 10.3390/ijms24119362.
Although early recognition of sepsis is essential for timely treatment and can improve sepsis outcomes, no marker has demonstrated sufficient discriminatory power to diagnose sepsis. This study aimed to compare gene expression profiles between patients with sepsis and healthy volunteers to determine the accuracy of these profiles in diagnosing sepsis and to predict sepsis outcomes by combining bioinformatics data with molecular experiments and clinical information. We identified 422 differentially expressed genes (DEGs) between the sepsis and control groups, of which 93 immune-related DEGs were considered for further studies due to immune-related pathways being the most highly enriched. Key genes upregulated during sepsis, including S100A8, S100A9, and CR1, are responsible for cell cycle regulation and immune responses. Key downregulated genes, including CD79A, HLA-DQB2, PLD4, and CCR7, are responsible for immune responses. Furthermore, the key upregulated genes showed excellent to fair accuracy in diagnosing sepsis (area under the curve 0.747-0.931) and predicting in-hospital mortality (0.863-0.966) of patients with sepsis. In contrast, the key downregulated genes showed excellent accuracy in predicting mortality of patients with sepsis (0.918-0.961) but failed to effectively diagnosis sepsis.In conclusion, bioinformatics analysis identified key genes that may serve as biomarkers for diagnosing sepsis and predicting outcomes among patients with sepsis.
尽管早期识别脓毒症对于及时治疗至关重要，可以改善脓毒症的结局，但目前还没有一种标志物具有足够的区分能力来诊断脓毒症。本研究旨在比较脓毒症患者和健康志愿者之间的基因表达谱，以确定这些谱在诊断脓毒症中的准确性，并通过将生物信息学数据与分子实验和临床信息相结合来预测脓毒症的结局。我们在脓毒症组和对照组之间鉴定了 422 个差异表达基因（DEGs），其中有 93 个与免疫相关的 DEGs 被认为与免疫相关途径最相关，因此需要进一步研究。脓毒症期间上调的关键基因，包括 S100A8、S100A9 和 CR1，负责细胞周期调节和免疫反应。下调的关键基因，包括 CD79A、HLA-DQB2、PLD4 和 CCR7，负责免疫反应。此外，关键上调基因在诊断脓毒症（曲线下面积 0.747-0.931）和预测脓毒症患者住院死亡率（0.863-0.966）方面具有极好到中等的准确性。相比之下，关键下调基因在预测脓毒症患者死亡率方面具有极好的准确性（0.918-0.961），但未能有效地诊断脓毒症。总之，生物信息学分析确定了一些关键基因，这些基因可能作为诊断脓毒症和预测脓毒症患者结局的生物标志物。
