Department of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Branch, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China.
Department of Infectious Disease, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, 350001, People's Republic of China.
BMC Pulm Med. 2022 Sep 2;22(1):334. doi: 10.1186/s12890-022-02130-8.
Currently, the rate of morbidity and mortality in acute respiratory distress syndrome (ARDS) remains high. One of the potential reasons for the poor and ineffective therapies is the lack of early and credible indicator of risk prediction that would help specific treatment of severely affected ARDS patients. Nevertheless, assessment of the clinical outcomes with transcriptomics of ARDS by alveolar macrophage has not been performed.
The expression data GSE116560 was obtained from the Gene Expression Omnibus databases (GEO) in NCBI. This dataset consists of 68 BAL samples from 35 subjects that were collected within 48 h of ARDS. Differentially expressed genes (DEGs) of different outcomes were analyzed using R software. The top 10 DEGs that were up- or down-regulated were analyzed using receiver operating characteristic (ROC) analysis. Kaplan-Meier survival analysis within two categories according to cut-off and the value of prediction of the clinical outcomes via DEGs was verified. GO enrichment, KEGG pathway analysis, and protein-protein interaction were also used for functional annotation of key genes.
24,526 genes were obtained, including 235 up-regulated and 292 down-regulated DEGs. The gene ADORA3 was chosen as the most obvious value to predict the outcome according to the ROC and survival analysis. For functional annotation, ADORA3 was significantly augmented in sphingolipid signaling pathway, cGMP-PKG signaling pathway, and neuroactive ligand-receptor interaction. Four genes (ADORA3, GNB1, NTS, and RHO), with 4 nodes and 6 edges, had the highest score in these clusters in the protein-protein interaction network.
Our results show that the prognostic prediction of early biomarkers of transcriptomics as identified in alveolar macrophage in ARDS can be extended for mechanically ventilated critically ill patients. In the long term, generalizing the concept of biomarkers of transcriptomics in alveolar macrophage could add to improving precision-based strategies in the ICU patients and may also lead to identifying improved strategy for critically ill patients.
目前，急性呼吸窘迫综合征（ARDS）的发病率和死亡率仍然很高。治疗效果不佳的一个潜在原因是缺乏早期、可靠的风险预测指标，无法对重症 ARDS 患者进行针对性治疗。然而，尚未通过肺泡巨噬细胞对 ARDS 的转录组学进行临床结局评估。
从 NCBI 的基因表达综合数据库（GEO）中获取表达谱数据 GSE116560。该数据集包含 35 名受试者的 68 个 BAL 样本，这些样本是在 ARDS 发生后 48 小时内采集的。使用 R 软件分析不同结局的差异表达基因（DEGs）。使用受试者工作特征（ROC）分析对上调或下调的前 10 个 DEG 进行分析。根据截点值和 DEGs 对临床结局的预测值，对两个类别进行 Kaplan-Meier 生存分析。还进行了 GO 富集、KEGG 通路分析和蛋白质-蛋白质相互作用，以对关键基因进行功能注释。
共获得 24526 个基因，包括 235 个上调和 292 个下调的 DEGs。根据 ROC 和生存分析，ADORA3 基因被选为预测结局最明显的基因。功能注释表明，ADORA3 基因在神经活性配体-受体相互作用、cGMP-PKG 信号通路和鞘脂信号通路中显著上调。在蛋白质-蛋白质相互作用网络中，ADORA3、GNB1、NTS 和 RHO 这四个基因（ADORA3、GNB1、NTS 和 RHO）具有 4 个节点和 6 个边，在这些聚类中得分最高。
我们的研究结果表明，对 ARDS 患者肺泡巨噬细胞转录组学早期生物标志物的预后预测可扩展至机械通气危重症患者。从长远来看，将肺泡巨噬细胞转录组学生物标志物的概念推广，可以提高 ICU 患者的精准治疗策略，也可能为危重症患者确定更好的治疗策略。
