Department of Critical Medicine, Shanxi Bethune Hospital (Tongji Shanxi Hospital of Shanxi Academy of Medical Sciences), The Third Hospital of Shanxi Medical University, 030031 Taiyuan, Shanxi, China.
Department of Critical Medicine, Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology, 430030 Wuhan, Hubei, China.
J Healthc Eng. 2022 Feb 12;2022:6878495. doi: 10.1155/2022/6878495. eCollection 2022.
The purpose of the present study is to screen the hub genes associated with sepsis, comprehensively understand the occurrence and progress mechanism of sepsis, and provide new targets for clinical diagnosis and treatment of sepsis.
The microarray data of GSE9692 and GSE95233 were downloaded from the Gene Expression Omnibus (GEO) database. The dataset GSE9692 contained 29 children with sepsis and 16 healthy children, while the dataset GSE95233 included 102 septic subjects and 22 healthy volunteers. Differentially expressed genes (DEGs) were screened by GEO2R online analysis. The DAVID database was applied to conduct functional enrichment analysis of the DEGs. The STRING database was adopted to acquire protein-protein interaction (PPI) networks.
We identified 286 DEGs (217 upregulated DEGs and 69 downregulated DEGs) in the dataset GSE9692 and 357 DEGs (236 upregulated DEGs and 121 downregulated DEGs) in the dataset GSE95233. After the intersection of DEGs of the two datasets, a total of 98 co-DEGs were obtained. DEGs associated with sepsis were involved in inflammatory responses such as T cell activation, leukocyte cell-cell adhesion, leukocyte-mediated immunity, cytokine production, immune effector process, lymphocyte-mediated immunity, defense response to fungus, and lymphocyte-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that sepsis was connected to bacterial and viral infections. Through PPI network analysis, we screened the most important hub genes, including ITK, CD247, MMP9, CD3D, MMP8, KLRK1, and GZMK.
In conclusion, the present study revealed an unbalanced immune response at the transcriptome level of sepsis and identified genes for potential biomarkers of sepsis, such as ITK, CD247, MMP9, CD3D, MMP8, KLRK1, and GZMK.
本研究旨在筛选与脓毒症相关的枢纽基因，全面了解脓毒症的发生和发展机制，为脓毒症的临床诊断和治疗提供新的靶点。
从基因表达综合数据库（GEO）下载 GSE9692 和 GSE95233 微阵列数据集。数据集 GSE9692 包含 29 名脓毒症患儿和 16 名健康儿童，而数据集 GSE95233 则包括 102 名脓毒症患者和 22 名健康志愿者。通过 GEO2R 在线分析筛选差异表达基因（DEGs）。采用 DAVID 数据库进行 DEGs 的功能富集分析。利用 STRING 数据库获取蛋白质-蛋白质相互作用（PPI）网络。
我们在数据集 GSE9692 中鉴定出 286 个 DEGs（217 个上调 DEGs 和 69 个下调 DEGs），在数据集 GSE95233 中鉴定出 357 个 DEGs（236 个上调 DEGs和 121 个下调 DEGs）。对两个数据集的 DEGs 进行交集后，共获得 98 个共同 DEGs。与脓毒症相关的 DEGs 参与了 T 细胞激活、白细胞细胞-细胞黏附、白细胞介导的免疫、细胞因子产生、免疫效应过程、淋巴细胞介导的免疫、对真菌的防御反应和淋巴细胞介导的免疫等炎症反应。京都基因与基因组百科全书（KEGG）通路富集分析表明，脓毒症与细菌和病毒感染有关。通过 PPI 网络分析，筛选出最重要的枢纽基因，包括 ITK、CD247、MMP9、CD3D、MMP8、KLRK1 和 GZMK。
综上所述，本研究揭示了脓毒症转录组水平失衡的免疫反应，并确定了 ITK、CD247、MMP9、CD3D、MMP8、KLRK1 和 GZMK 等脓毒症潜在生物标志物的基因。
