Diagnostic value of procalcitonin and hemocyte parameters in neonates with bloodstream infection: Role of activated hemocyte‐related genes

Yiyi Tao, Qian Li, Huidi Peng, Ningshu Huang
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Abstract

This study aimed to evaluate the diagnostic potential of hemocyte parameters and procalcitonin (PCT) in detecting bloodstream infections (BSI) in neonates and explore the contribution of hemocyte activation‐related genes to pediatric sepsis through bioinformatics analysis. A cohort of 419 neonates, categorized as BSI (positive blood culture) and control, underwent comparative analysis of PCT and hemocyte parameters. A predictive model for neonatal BSI was established, demonstrating an impressive area under the receiver ROC curve of 0.968 with remarkable sensitivity (92%) and specificity (87.3%). Hemocyte parameters, including lymphocyte and neutrophil percentages, platelet distribution width (PDW), platelet to lymphocyte ratio (PLR), and PCT, emerged as independent predictors of neonatal BSI. Furthermore, bioinformatics analysis utilizing Gene Expression Omnibus (GEO) datasets yielded significant insights. Differential gene expression (DEGs), gene ontology (GO), pathway enrichment, gene set enrichment analysis (GSEA), and protein–protein interaction (PPI) networks were explored. The differentially expressed genes and hub genes were notably enriched in the activation of neutrophils, lymphocytes, and platelets. Notably, elevated expression levels of SPI1, TYROBP, and FCER1G were observed in pediatric sepsis or septic shock, with positive correlations between SPI1, FCER1G, and TYROBP. In summary, the combination of lymphocyte, PDW, PLR, and PCT effectively diagnosed neonatal BSI. Bioinformatics analysis underscored the pivotal role of activated hemocytes in diagnosing pediatric sepsis, with SPI1, TYROBP, and FCER1G co‐expression influencing the disease's pathophysiology by modulating neutrophil and platelet activity.
新生儿血流感染中降钙素原和血细胞参数的诊断价值:活化的血细胞相关基因的作用
本研究旨在评估血细胞参数和降钙素原(PCT)在检测新生儿血流感染(BSI)方面的诊断潜力,并通过生物信息学分析探讨血细胞活化相关基因对小儿败血症的贡献。一组 419 名新生儿被分为 BSI(血培养阳性)和对照组,他们接受了 PCT 和血细胞参数的比较分析。建立的新生儿 BSI 预测模型显示,接收器 ROC 曲线下的面积为 0.968,灵敏度(92%)和特异性(87.3%)均十分显著。淋巴细胞和中性粒细胞百分比、血小板分布宽度(PDW)、血小板与淋巴细胞比值(PLR)和 PCT 等血细胞参数成为新生儿 BSI 的独立预测因子。此外,利用基因表达总库(GEO)数据集进行的生物信息学分析也获得了重要启示。研究人员探索了差异基因表达(DEGs)、基因本体(GO)、通路富集、基因组富集分析(GSEA)和蛋白-蛋白相互作用(PPI)网络。差异表达基因和枢纽基因明显富集于中性粒细胞、淋巴细胞和血小板的活化中。值得注意的是,在小儿败血症或脓毒性休克中观察到 SPI1、TYROBP 和 FCER1G 表达水平升高,SPI1、FCER1G 和 TYROBP 之间呈正相关。总之,淋巴细胞、PDW、PLR 和 PCT 的组合能有效诊断新生儿 BSI。生物信息学分析强调了活化血细胞在诊断小儿败血症中的关键作用,SPI1、TYROBP和FCER1G的共同表达通过调节中性粒细胞和血小板的活性影响了疾病的病理生理学。
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