Department of Anesthesiology, The First People's Hospital of Lianyungang, Lianyungang City, 222002 Jiangsu Province, China.
Department of pediatrics, The First People's Hospital of Lianyungang, Lianyungang City, 222002 Jiangsu Province, China.
Biomed Res Int. 2020 Dec 1;2020:7170464. doi: 10.1155/2020/7170464. eCollection 2020.
Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock.
We downloaded the mRNA profiles GSE13904 and GSE4607, of which GSE13904 includes 106 blood samples of pediatric patients with septic shock and 18 health control samples; GSE4607 includes 69 blood samples of pediatric patients with septic shock and 15 health control samples. The differentially expressed lncRNAs were identified through the limma R package; meanwhile, GO terms and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. The protein-protein interaction (PPI) network was constructed based on the STRING database using the targets of differently expressed lncRNAs. The MCODE plug-in of Cytoscape was used to screen significant clustering modules composed of key genes. Finally, stepwise regression analysis was performed to screen the optimal lncRNAs and construct the logistic regression model, and the ROC curve was applied to evaluate the accuracy of the model.
A total of 13 lncRNAs which simultaneously exhibited significant differences in the septic shock group compared with the control group from two sets were identified. According to the 18 targets of differentially expressed lncRNAs, we identified some inflammatory and immune response-related pathways. In addition, several target mRNAs were predicted to be potentially involved in the occurrence of septic shock. The logistic regression model constructed based on two optimal lncRNAs THAP9-AS1 and TSPOAP1-AS1 could efficiently separate samples with septic shock from normal controls.
In summary, a predictive model based on the lncRNAs THAP9-AS1 and TSPOAP1-AS1 provided novel lightings on diagnostic research of septic shock.
脓毒症是一种由感染引起的全身炎症综合征，发病率和死亡率均较高。尽管长链非编码 RNA 已被确定与许多炎症性疾病密切相关，但关于 lncRNA 在儿科感染性休克中的作用知之甚少。
我们下载了 mRNA 图谱 GSE13904 和 GSE4607，其中 GSE13904 包括 106 例儿科感染性休克患者和 18 例健康对照者的 106 份血液样本；GSE4607 包括 69 例儿科感染性休克患者和 15 例健康对照者的 69 份血液样本。通过 limma R 包鉴定差异表达的 lncRNA；同时，通过 clusterProfiler R 包进行 GO 术语和 KEGG 通路富集分析。基于 STRING 数据库，使用差异表达 lncRNA 的靶点构建蛋白质-蛋白质相互作用（PPI）网络。使用 Cytoscape 的 MCODE 插件筛选由关键基因组成的显著聚类模块。最后，通过逐步回归分析筛选最佳 lncRNA 并构建逻辑回归模型，并应用 ROC 曲线评估模型的准确性。
从两组中鉴定出同时在脓毒症组与对照组之间差异显著的 13 个 lncRNA。根据差异表达 lncRNA 的 18 个靶点，我们鉴定出一些与炎症和免疫反应相关的途径。此外，一些靶 mRNA 被预测可能参与脓毒症的发生。基于两个最佳 lncRNA THAP9-AS1 和 TSPOAP1-AS1 构建的逻辑回归模型能够有效地将脓毒症患者样本与正常对照者样本区分开。
总之，基于 lncRNA THAP9-AS1 和 TSPOAP1-AS1 的预测模型为脓毒症的诊断研究提供了新的思路。
