The Department of Emergency, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
The Department of SICU, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Genet Test Mol Biomarkers. 2024 Apr;28(4):133-143. doi: 10.1089/gtmb.2023.0143. Epub 2024 Mar 19.
Sepsis is a complex clinical syndrome caused by a dysregulated host immune response to infection. This study aimed to identify a competing endogenous RNA (ceRNA) network that can greatly contribute to understanding the pathophysiological process of sepsis and determining sepsis biomarkers. The GSE100159, GSE65682, GSE167363, and GSE94717 datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene coexpression network analysis was performed to find modules possibly involved in sepsis. A long noncoding RNA-microRNA-messenger RNA (lncRNA-miRNA-mRNA) network was constructed based on the findings. Single-cell analysis was performed. Human umbilical vein endothelial cells were treated with lipopolysaccharide (LPS) to create an model of sepsis for network verification. Reverse transcription-polymerase chain reaction, fluorescence hybridization, and luciferase reporter genes were used to verify the bioinformatic analysis. By integrating data from three GEO datasets, we successfully constructed a ceRNA network containing 18 lncRNAs, 7 miRNAs, and 94 mRNAs based on the ceRNA hypothesis. The lncRNA was found to be highly expressed in LPS-stimulated endothelial cells and may thus play a role in endothelial cell injury. Univariate and multivariate Cox analyses showed that only was an independent predictor of prognosis in sepsis. Overall, our findings indicated that the /hsa-miR-449c-5p/ ceRNA regulatory axis may play a role in the progression of sepsis. The sepsis ceRNA network, especially the /hsa-miR-449c-5p/ regulatory axis, is expected to reveal potential biomarkers and therapeutic targets for sepsis management.
脓毒症是一种复杂的临床综合征，由宿主对感染的免疫反应失调引起。本研究旨在确定一个竞争内源性 RNA（ceRNA）网络，该网络可以极大地帮助理解脓毒症的病理生理过程，并确定脓毒症的生物标志物。从基因表达综合数据库（GEO）中获取了 GSE100159、GSE65682、GSE167363 和 GSE94717 数据集。进行加权基因共表达网络分析以找到可能参与脓毒症的模块。基于研究结果构建长非编码 RNA-微小 RNA-信使 RNA（lncRNA-miRNA-mRNA）网络。进行单细胞分析。用人脐静脉内皮细胞（HUVEC）用脂多糖（LPS）处理以创建脓毒症模型用于网络验证。逆转录-聚合酶链反应（RT-PCR）、荧光杂交和荧光素酶报告基因用于验证生物信息学分析。通过整合来自三个 GEO 数据集的数据，我们成功地基于 ceRNA 假说构建了一个包含 18 个 lncRNA、7 个 miRNA 和 94 个 mRNA 的 ceRNA 网络。发现 lncRNA 在 LPS 刺激的内皮细胞中高表达，因此可能在内皮细胞损伤中发挥作用。单变量和多变量 Cox 分析显示，只有 是脓毒症预后的独立预测因子。总的来说，我们的研究结果表明，/hsa-miR-449c-5p/ceRNA 调控轴可能在脓毒症的进展中发挥作用。脓毒症 ceRNA 网络，特别是/hsa-miR-449c-5p/调控轴，有望揭示脓毒症管理的潜在生物标志物和治疗靶点。
