Table3_A Five-Genes Based Diagnostic Signature for Sepsis-Induced ARDS.xlsx

Background: Acute respiratory distress syndrome (ARDS) is a frequent and serious complication of sepsis without specific and sensitive diagnostic signatures.Methods: The mRNA profiles, including 60 blood samples with sepsis-induced ARDS and 86 blood samples with sepsis alone, were obtained from the Gene Expression Omnibus (GEO). The differently expressed genes (DEGs) were analyzed by limma package of R language. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out using the clusterProfiler package of R. Eventually, multivariate logistic regression model was established through the glm function of R, and support vector machine (SVM) model was constructed via the e1071 package of R.Results: A total of 242 DEGs in GSE32707 and 102 DEGs in GSE66890 were identified. Notably, five genes exhibited significant differences between the two datasets and were considered to be closely associated with the occurrence of ARDS induced by sepsis. Furthermore, functional enrichment analysis based on the DEGs showed there were 80 overlapped GO terms and one KEGG pathway which were significantly enriched in the two datasets. The logistic regression model and SVM model constructed could efficiently distinguish sepsis patients with or without ARDS.Conclusion: In brief, our study suggested that NKG7, SPTA1, FGL2, RGS2, and IFI27 might be potential diagnostic signatures for sepsis-induced ARDS, which contributed to the future exploration in mechanism of ARDS occurrence and development.

背景：急性呼吸窘迫综合征（ARDS）是脓毒症的一种常见且严重的并发症，缺乏特异性及敏感性的诊断标志。方法：从基因表达综合数据库（GEO）获取了包括60份由脓毒症引起的ARDS血液样本和86份仅脓毒症的血液样本的mRNA谱。通过R语言的limma包分析了差异表达基因（DEGs）。利用R语言的clusterProfiler包进行了基因本体（GO）分析和京都基因与基因组百科全书（KEGG）通路富集分析。最终，通过R的glm函数建立了多元逻辑回归模型，并通过e1071包构建了支持向量机（SVM）模型。结果：在GSE32707中鉴定出242个DEGs，在GSE66890中鉴定出102个DEGs。值得注意的是，五个基因在两个数据集中表现出显著差异，被认为与脓毒症引起的ARDS的发生密切相关。此外，基于DEGs的功能富集分析显示，有两个数据集中存在80个重叠的GO术语和一个显著富集的KEGG通路。构建的逻辑回归模型和支持向量机模型能够有效地区分患有或未患有ARDS的脓毒症患者。结论：简而言之，本研究表明NKG7、SPTA1、FGL2、RGS2和IFI27可能成为脓毒症引起的ARDS的潜在诊断标志，这有助于对ARDS发生和发展的机制进行未来探索。