Sepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes involved in the molecular mechanisms of sepsis and search for potential new biomarkers and treatment targets for sepsis using bioinformatics analyses. Three datasets (GSE95233, GSE57065, and GSE28750) associated with sepsis were downloaded from the public functional genomics data repository Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using R packages (Affy and limma). Functional enrichment of the DEGs was analyzed with the DAVID database. Protein-protein interaction networks were derived using the STRING database and visualized using Cytoscape software. Potential biomarker genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). The three datasets included 156 whole blood RNA samples from 89 sepsis patients and 67 healthy controls. Between the two groups, 568 DEGs were identified, among which 315 were upregulated and 253 were downregulated in the septic group. These genes were enriched for pathways mainly involved in the innate immune response, T-cell biology, antigen presentation, and natural killer cell function. ROC analyses identified nine genes—LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN—as potential new biomarkers for sepsis. Real-time PCR confirmed that the expression of seven of these genes was in accordance with the microarray results. This study revealed imbalanced immune responses at the transcriptomic level during early sepsis and identified nine genes as potential biomarkers for sepsis.

中文翻译：

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使用生物信息学分析鉴定脓毒症的潜在生物标志物和免疫特征

脓毒症仍然是一个主要的全球问题，尽管其管理有所改善，但仍与高死亡率和发病率有关。目前使用的标记物存在缺乏特异性和败血症早期检测失败等缺点。在这项研究中，我们旨在确定参与败血症分子机制的关键基因，并使用生物信息学分析寻找败血症的潜在新生物标志物和治疗靶点。从公共功能基因组学数据库 Gene Expression Omnibus 下载了与败血症相关的三个数据集（GSE95233、GSE57065 和 GSE28750）。使用 R 包（Affy 和 limma）鉴定差异表达基因 (DEG)。使用 DAVID 数据库分析了 DEG 的功能富集。蛋白质-蛋白质相互作用网络是使用 STRING 数据库得出的，并使用 Cytoscape 软件进行可视化。使用 R 包 (pROC) 中的受试者工作特征 (ROC) 曲线分析潜在的生物标志物基因。这三个数据集包括来自 89 名败血症患者和 67 名健康对照的 156 份全血 RNA 样本。在两组之间，鉴定出 568 个 DEG，其中脓毒症组中 315 个上调，253 个下调。这些基因被丰富的途径主要涉及先天免疫反应、T 细胞生物学、抗原呈递和自然杀伤细胞功能。ROC 分析确定了九个基因——LRG1、ELANE、TP53、LCK、TBX21、ZAP70、CD247、ITK 和 FYN——作为败血症的潜在新生物标志物。实时 PCR 证实其中 7 个基因的表达与微阵列结果一致。这项研究揭示了早期败血症期间转录组水平的免疫反应不平衡，并确定了九个基因作为败血症的潜在生物标志物。