ARG1作为脓毒症诊断和预后的生物标志物:来自WGCNA和PPI网络的证据

IF 2.7 3区 生物学
Jing-Xiang Zhang, Wei-Heng Xu, Xin-Hao Xing, Lin-Lin Chen, Qing-Jie Zhao, Yan Wang
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引用次数: 2

摘要

背景:脓毒症是由宿主对感染反应失调引起的危及生命的多器官功能障碍。脓毒症仍然是全球关注的一个主要问题,其死亡率和发病率很高,而脓毒症患者的管理在很大程度上依赖于早期识别和快速分层。本研究旨在确定脓毒症的关键基因和生物标志物,指导临床医生快速诊断和预后。方法:对170例脓毒症患者样本和110例健康对照样本的多个全球数据集进行初步分析,发现脓毒症患者外周血中存在共同差异表达基因(common differential expression genes, DEGs)。在基因肿瘤学(GO)和通路分析后,使用加权基因相关网络分析(WGCNA)筛选与临床诊断最相关的基因。基于DEGs构建了蛋白质-蛋白质相互作用网络(PPI Network),并找到了枢纽基因。比较了WGCNA和PPI网络的结果,发现了一个共享基因。然后利用728个实验样本和355个对照样本的更多数据集来证明该基因的诊断和预后价值。最后,利用实时荧光定量PCR对生物信息学结果进行验证。结果:在不同种族的脓毒症患者血液中鉴定出444个共同的差异表达基因。WGCNA共发现15个与临床诊断最相关的基因,PPI网络共发现24个节点度最多的枢纽基因。ARG1被证明是唯一的重叠基因。使用更多数据集的进一步分析表明,ARG1不仅在脓毒症中比健康对照组急剧上调,而且在脓毒症休克中比非脓毒症休克显著高表达,在严重或致死脓毒症中比非并发症脓毒症显著高表达,在早期治疗时无反应者比反应者显著高表达。这些都证明了ARG1作为一个关键的生物标志物的性能。最后,实验证实了血液中ARG1的上调。结论:我们通过WGCNA和PPI网络发现了可能在脓毒症中发挥重要作用的关键基因。ARG1是两个结果中唯一重叠的基因,可用于脓毒症的准确诊断、区分严重程度和预测治疗反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network.

ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network.

ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network.

ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network.

Background: Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication.

Methods: Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results.

Results: Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally.

Conclusions: We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis.

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来源期刊
Hereditas
Hereditas Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
3.80
自引率
3.70%
发文量
0
期刊介绍: For almost a century, Hereditas has published original cutting-edge research and reviews. As the Official journal of the Mendelian Society of Lund, the journal welcomes research from across all areas of genetics and genomics. Topics of interest include human and medical genetics, animal and plant genetics, microbial genetics, agriculture and bioinformatics.
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