Wang Xiaowan, Fei Aihua
Department of Emergency, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China.
Department of General Practice, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China.
Comb Chem High Throughput Screen. 2023;26(4):789-800. doi: 10.2174/1386207325666220509180737.
Acute respiratory distress syndrome (ARDS) caused by sepsis presents a high mortality rate; therefore, identification of susceptibility genes of sepsis to ARDS at an early stage is particularly critical.
The GSE66890 dataset was downloaded and analyzed by WGCNA to obtain modules. Then, GO and KEGG analyses of the module genes were performed. Then, the PPI network and LASSO model were constructed to identify the key genes. Finally, expression levels of the screened genes were validated in clinical subjects.
We obtained 17 genes merged modules via WGCNA, and the dark module and tan module were the most positively and negatively correlated with sepsis-induced ARDS, respectively. Based on gene intersections of the module genes, 11 hub genes were identified in the dark module, and 5 hub genes were identified in the tan module. Finally, the six key genes were identified by constructing the LASSO model. We further detected the screened genes expression in clinical samples, and as the bioinformatics analysis revealed, the expressions of NANOG, RAC1, TWIST1, and SNW1 were significantly upregulated in the ARDS group compared to the sepsis group, and IMP3 and TUBB4B were significantly downregulated.
We identified six genes as the potential biomarkers in sepsis-related ARDS. Our findings may enhance the knowledge of the molecular mechanisms behind the development of sepsisinduced ARDS.
脓毒症所致急性呼吸窘迫综合征（ARDS）死亡率高；因此，早期识别脓毒症致ARDS的易感基因尤为关键。
下载GSE66890数据集并通过加权基因共表达网络分析（WGCNA）进行分析以获得模块。然后，对模块基因进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析。接着，构建蛋白质-蛋白质相互作用（PPI）网络和套索（LASSO）模型以识别关键基因。最后，在临床受试者中验证筛选出的基因的表达水平。
通过WGCNA我们获得了17个基因合并模块，其中黑色模块和棕褐色模块分别与脓毒症诱导的ARDS呈最强正相关和负相关。基于模块基因的基因交集，在黑色模块中鉴定出11个枢纽基因，在棕褐色模块中鉴定出5个枢纽基因。最后，通过构建LASSO模型确定了6个关键基因。我们进一步检测了临床样本中筛选出的基因的表达，正如生物信息学分析所显示的，与脓毒症组相比，ARDS组中NANOG、RAC1、TWIST1和SNW1的表达显著上调，而IMP3和TUBB4B则显著下调。
我们确定了6个基因作为脓毒症相关ARDS的潜在生物标志物。我们的研究结果可能会增加对脓毒症诱导的ARDS发生发展背后分子机制的认识。
