Department of Anesthesiology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China.
Department of Anesthesiology, Zhuzhou Central Hospital, Zhuzhou, Hunan Province, China.
Ann Med. 2024 Dec;56(1):2411015. doi: 10.1080/07853890.2024.2411015. Epub 2024 Oct 10.
This study investigated the molecular mechanism of quercetin in the treatment of sepsis using network pharmacological prediction and experimentation.
Hub genes were identified by intersecting the differentially expressed genes (DEGs) of the GSE131761 and GSE9960 databases with genes from the hub modules of Weighted Gene Co-Expression Network Analysis (WGCNA), targets of quercetin, and ferroptosis. Subsequently, in order to determine the functional characteristics and molecular link of hub gene obtained above, we redetermined the hub-DEGs in GSE131761 according to high or low hub gene expression. Afterward, the main pathways of enrichment analysis were validated using these hub-DEGs. Finally, an experiment was conducted to validate the findings.
By intersecting 1415 DEGs in GSE131761, 543 DEGs in GSE9960, 5784 key modular genes, 470 ferroptosis-related genes, and 154 quercetin-related genes, we obtained one quercetin-related gene, . Subsequently, 340 hub-DEGs were further validated according to high or low expression. The results of the enrichment analysis revealed that hub-DEGs were mainly associated with inflammation and the immune response. Immune infiltration analysis showed that higher expression of was related to macrophage infiltration and could be a predictor of diagnosis in patients with sepsis. The expression pattern of was then depicted and the upregulation of in the vital organs of septic mice was further demonstrated. and experiments showed that upregulation of and inflammation-related cytokines induced by sepsis could be inhibited by quercetin ( < 0.05).
may be involved in the occurrence and development of multi-organ functional disturbances in sepsis and is a reliable target of quercetin against sepsis.
本研究采用网络药理学预测和实验方法研究槲皮素治疗脓毒症的分子机制。
通过将 GSE131761 和 GSE9960 数据库中的差异表达基因（DEGs）与加权基因共表达网络分析（WGCNA）的枢纽模块基因、槲皮素的靶基因和铁死亡相关基因进行交集，确定枢纽基因。然后，为了确定上述获得的枢纽基因的功能特征和分子联系，我们根据高或低的枢纽基因表达重新确定了 GSE131761 中的枢纽-DEGs。之后，使用这些枢纽-DEGs 验证富集分析的主要途径。最后，进行了一项实验来验证这些发现。
通过交集 GSE131761 中的 1415 个 DEGs、GSE9960 中的 543 个 DEGs、5784 个关键模块基因、470 个铁死亡相关基因和 154 个槲皮素相关基因，我们获得了一个槲皮素相关基因 。随后，根据 表达的高低进一步验证了 340 个枢纽-DEGs。富集分析的结果表明，枢纽-DEGs 主要与炎症和免疫反应有关。免疫浸润分析表明， 较高的表达与巨噬细胞浸润有关，可能是脓毒症患者诊断的预测因子。然后描绘了 的表达模式，并进一步证明了 在脓毒症小鼠重要器官中的上调。 和 实验表明，脓毒症引起的 上调和炎症相关细胞因子可以被槲皮素抑制（ < 0.05）。
可能参与脓毒症多器官功能障碍的发生发展，是槲皮素治疗脓毒症的可靠靶点。
