Zeng Xiaoliang, Feng Jihua, Yang Yanli, Zhao Ruzhi, Yu Qiao, Qin Han, Wei Lile, Ji Pan, Li Hongyuan, Wu Zimeng, Zhang Jianfeng
Department of Emergency Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, People's Republic of China.
J Inflamm Res. 2021 Mar 11;14:829-841. doi: 10.2147/JIR.S301663. eCollection 2021.
Sepsis is a disease associated with high mortality. We performed bioinformatic analysis to identify key biomarkers associated with sepsis and septic shock.
The top 20% of genes showing the greatest variance between sepsis and controls in the GSE13904 dataset (children) were screened by co-expression network analysis. The differentially expressed genes (DEGs) were identified through analyzing differential gene expression between sepsis patients and control in the GSE13904 (children) and GSE154918 (adult) data sets. Intersection analysis of module genes and DEGs was performed to identify common DEGs for enrichment analysis, protein-protein interaction network (PPI network) analysis, and Short Time-series Expression Miner (STEM) analysis. The PPI network genes were ranked by degree of connectivity, and the top 100 sepsis-associated genes were identified based on the area under the receiver operating characteristic curve (AUC). In addition, we evaluated differences in immune cell infiltration between sepsis patients and controls in children (GSE13904, GSE25504) and adults (GSE9960, GSE154918). Finally, we analyzed differences in DNA methylation levels between sepsis patients and controls in GSE138074 (adults).
The common genes were associated mainly with up-regulated inflammatory and metabolic responses, as well as down-regulated immune responses. Sepsis patients showed lower infiltration by most types of immune cells. Genes in the PPI network with AUC values greater than 0.9 in both GSE13904 (children) and GSE154918 (adults) were screened as key genes for diagnosis. These key genes (MAPK14, FGR, RHOG, LAT, PRKACB, UBE2Q2, ITK, IL2RB, and CD247) were also identified in STEM analysis to be progressively dysregulated across controls, sepsis patients and patients with septic shock. In addition, the expression of MAPK14, FGR, and CD247 was modified by methylation.
This study identified several potential diagnostic genes and inflammatory and metabolic responses mechanisms associated with the development of sepsis.
脓毒症是一种死亡率很高的疾病。我们进行了生物信息学分析，以确定与脓毒症和脓毒性休克相关的关键生物标志物。
通过共表达网络分析筛选出GSE13904数据集（儿童）中脓毒症组与对照组之间差异最大的前20%的基因。通过分析GSE13904（儿童）和GSE154918（成人）数据集中脓毒症患者与对照组之间的基因表达差异，确定差异表达基因（DEG）。对模块基因和DEG进行交集分析，以识别用于富集分析、蛋白质-蛋白质相互作用网络（PPI网络）分析和短时序列表达挖掘器（STEM）分析的共同DEG。根据连接度对PPI网络基因进行排序，并基于受试者工作特征曲线（AUC）下的面积确定前100个与脓毒症相关的基因。此外，我们评估了儿童（GSE13904、GSE25504）和成人（GSE9960、GSE154918）中脓毒症患者与对照组之间免疫细胞浸润的差异。最后，我们分析了GSE138074（成人）中脓毒症患者与对照组之间DNA甲基化水平的差异。
共同基因主要与上调的炎症和代谢反应以及下调的免疫反应相关。脓毒症患者大多数类型免疫细胞的浸润较低。在GSE13904（儿童）和GSE154918（成人）中AUC值均大于0.9的PPI网络中的基因被筛选为诊断关键基因。这些关键基因（MAPK14、FGR、RHOG、LAT、PRKACB、UBE2Q2、ITK、IL2RB和CD247）在STEM分析中也被确定为在对照组、脓毒症患者和脓毒性休克患者中逐渐失调。此外，MAPK14、FGR和CD247的表达受到甲基化的影响。
本研究确定了几个与脓毒症发生发展相关的潜在诊断基因以及炎症和代谢反应机制。
