Xiao Yulong, Zhang Genhao
Department of Medical Laboratory, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
Department of Blood Transfusion, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
J Inflamm Res. 2024 Apr 4;17:2063-2071. doi: 10.2147/JIR.S447588. eCollection 2024.
Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis.
Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model's ability to diagnose pediatric sepsis in 65 actual clinical samples.
Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%).
We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.
儿童脓毒症的发病率和死亡率非常高。本研究的目的是评估儿童脓毒症的诊断生物标志物和免疫细胞浸润情况。
从基因表达综合数据库（GEO）下载了三个数据集（GSE13904、GSE26378和GSE26440）。在识别出GSE26378数据集中差异表达基因（DEGs）和通过加权基因共表达网络（WGCNA）选择的模块性脓毒症基因中的重叠基因后，通过LASSO回归和随机森林分析进一步鉴定关键基因，以构建诊断模型。采用受试者工作特征曲线（ROC）分析来验证该诊断模型对儿童脓毒症的诊断效能。此外，我们使用qRT-PCR检测关键基因的表达水平，并在65份实际临床样本中验证该诊断模型诊断儿童脓毒症的能力。
在DEGs和模块性脓毒症基因的294个重叠基因中，筛选出五个关键基因（STOM、MS4A4A、CD177、MMP8和MCEMP1）来构建儿童脓毒症的诊断模型。脓毒症组中这五个关键基因的表达高于正常组。该诊断模型显示出良好的诊断能力，曲线下面积（AUC）分别为1、0.986和0.968。更重要的是，该诊断模型在65份临床样本中的AUC为0.937，显示出良好的诊断能力，并且与降钙素原（PCT）、白细胞（WBC）计数、C反应蛋白（CRP）和中性粒细胞百分比（NEU%）等传统炎症指标相比，具有更好的诊断效能。
我们开发并测试了一种五基因诊断模型，该模型能够可靠地识别儿童脓毒症，还为儿童脓毒症患者外周血诊断检测提供了潜在的候选基因。
