School of Clinical Medicine, Tsinghua University, Beijing, China.
Department of General Medicine, Beijing Tsinghua Changgung Hospital affiliated to Tsinghua University, Beijing, China.
Front Immunol. 2023 Feb 2;14:1105399. doi: 10.3389/fimmu.2023.1105399. eCollection 2023.
Sepsis is an organ dysfunction syndrome caused by the body's dysregulated response to infection. Yet, due to the heterogeneity of this disease process, the diagnosis and definition of sepsis is a critical issue in clinical work. Existing methods for early diagnosis of sepsis have low specificity.
This study evaluated the diagnostic and predictive values of pyroptosis-related genes in normal and sepsis patients and their role in the immune microenvironment using multiple bioinformatics analyses and machine-learning methods.
Pediatric sepsis microarray datasets were screened from the GEO database and the differentially expressed genes (DEGs) associated with pyroptosis were analyzed. DEGs were then subjected to multiple bioinformatics analyses. The differential immune landscape between sepsis and healthy controls was explored by screening diagnostic genes using various machine-learning models. Also, the diagnostic value of these diagnosis-related genes in sepsis (miRNAs that have regulatory relationships with genes and related drugs that have regulatory relationships) were analyzed in the internal test set and external test.
Eight genes (CLEC5A, MALT1, NAIP, NLRC4, SERPINB1, SIRT1, STAT3, and TLR2) related to sepsis diagnosis were screened by multiple machine learning algorithms. The CIBERSORT algorithm confirmed that these genes were significantly correlated with the infiltration abundance of some immune cells and immune checkpoint sites (all <0.05). SIRT1, STAT3, and TLR2 were identified by the DGIdb database as potentially regulated by multiple drugs. Finally, 7 genes were verified to have significantly different expressions between the sepsis group and the control group (<0.05).
The pyroptosis-related genes identified and verified in this study may provide a useful reference for the prediction and assessment of sepsis.
脓毒症是一种器官功能障碍综合征，由机体对感染的失调反应引起。然而，由于该病过程的异质性，脓毒症的诊断和定义是临床工作中的一个关键问题。现有的脓毒症早期诊断方法特异性低。
本研究通过多种生物信息学分析和机器学习方法，评估了正常和脓毒症患者中与细胞焦亡相关的基因的诊断和预测价值及其在免疫微环境中的作用。
从 GEO 数据库筛选儿科脓毒症微阵列数据集，分析与细胞焦亡相关的差异表达基因（DEGs）。然后对 DEGs 进行多种生物信息学分析。通过筛选不同的机器学习模型，分析诊断基因，探讨脓毒症与健康对照之间的差异免疫图谱。还在内部测试集和外部测试中分析了这些诊断相关基因在脓毒症中的诊断价值（与基因有调控关系的 miRNA 和有调控关系的相关药物）。
通过多种机器学习算法筛选出 8 个与脓毒症诊断相关的基因（CLEC5A、MALT1、NAIP、NLRC4、SERPINB1、SIRT1、STAT3 和 TLR2）。CIBERSORT 算法证实这些基因与某些免疫细胞浸润丰度和免疫检查点显著相关（均<0.05）。DGIdb 数据库确定 SIRT1、STAT3 和 TLR2 可能受多种药物调控。最后，在脓毒症组和对照组之间，有 7 个基因被验证存在显著差异表达（<0.05）。
本研究中鉴定和验证的细胞焦亡相关基因可能为脓毒症的预测和评估提供有用的参考。
