网络分析确定了用于脓毒症诱导的急性呼吸窘迫综合征的基因生物标志物面板。

Network analysis identifies a gene biomarker panel for sepsis-induced acute respiratory distress syndrome.

机构信息

Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Army Medical University (Southwest Hospital), No.30 Gaotanyan Main Street, Chongqing, 400038, China.

Department of Biochemistry and Molecular Biology, Army Medical University, Chongqing, China.

出版信息

BMC Med Genomics. 2023 Jul 13;16(1):165. doi: 10.1186/s12920-023-01595-8.

DOI:10.1186/s12920-023-01595-8

PMID:37443002

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10339646/

Abstract

BACKGROUND

Acute respiratory distress syndrome (ARDS) is characterized by non-cardiogenic pulmonary edema caused by inflammation, which can lead to serious respiratory complications. Due to the high mortality of ARDS caused by sepsis, biological markers that enable early diagnosis are urgently needed for clinical treatment.

METHODS

In the present study, we used the public microarray data of whole blood from patients with sepsis-induced ARDS, patients with sepsis-alone and healthy controls to perform an integrated analysis based on differential expressed genes (DEGs) and co-expression network to identify the key genes and pathways related to the development of sepsis into ARDS that may be key targets for diagnosis and treatment.

RESULTS

Compared with controls, we identified 180 DEGs in the sepsis-alone group and 152 DEGs in the sepsis-induced ARDS group. About 70% of these genes were unique to the two groups. Functional analysis of DEGs showed that neutrophil-mediated inflammation and mitochondrial dysfunction are the main features of ARDS induced by sepsis. Gene network analysis identified key modules and screened out key regulatory genes related to ARDS. The key genes and their upstream regulators comprised a gene panel, including EOMES, LTF, CSF1R, HLA-DRA, IRF8 and MPEG1. Compared with the healthy controls, the panel had an area under the curve (AUC) of 0.900 and 0.914 for sepsis-alone group and sepsis-induced ARDS group, respectively. The AUC was 0.746 between the sepsis-alone group and sepsis-induced ARDS group. Moreover, the panel of another independent blood transcriptional expression profile dataset showed the AUC was 0.769 in diagnosing sepsis-alone group and sepsis-induced ARDS group.

CONCLUSIONS

Taken together, our method contributes to the diagnosis of sepsis and sepsis-induced ARDS. The biological pathway involved in this gene biomarker panel may also be a critical target in combating ARDS caused by sepsis.

摘要

背景

急性呼吸窘迫综合征（ARDS）的特征是炎症引起的非心源性肺水肿，可导致严重的呼吸并发症。由于脓毒症引起的 ARDS 死亡率高，因此迫切需要用于临床治疗的早期诊断的生物标志物。

方法

本研究我们使用脓毒症诱导的 ARDS 患者、单纯脓毒症患者和健康对照者的全血公共微阵列数据，基于差异表达基因（DEG）和共表达网络进行综合分析，以鉴定与脓毒症发展为 ARDS 相关的关键基因和途径，这些基因和途径可能是诊断和治疗的关键靶点。

结果

与对照组相比，我们在单纯脓毒症组中鉴定出 180 个 DEG，在脓毒症诱导的 ARDS 组中鉴定出 152 个 DEG。这些基因中有 70%左右是两组所特有的。DEG 的功能分析表明，中性粒细胞介导的炎症和线粒体功能障碍是脓毒症诱导的 ARDS 的主要特征。基因网络分析鉴定出关键模块，并筛选出与 ARDS 相关的关键调节基因。关键基因及其上游调节剂组成了一个基因面板，包括 EOMES、LTF、CSF1R、HLA-DRA、IRF8 和 MPEG1。与健康对照组相比，该面板在单纯脓毒症组和脓毒症诱导的 ARDS 组的 AUC 分别为 0.900 和 0.914。单纯脓毒症组和脓毒症诱导的 ARDS 组之间的 AUC 为 0.746。此外，另一个独立的血液转录表达谱数据集的面板在诊断单纯脓毒症组和脓毒症诱导的 ARDS 组时 AUC 为 0.769。