Yang Yang, Zhang Genhao
Department of Nuclear Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Hospital of Henan University, People'sZhengzhou, 450003, Henan, China.
Department of Blood Transfusion, Zhengzhou University First Affiliated Hospital, Zhengzhou, 450052, Henan, China.
Eur J Pediatr. 2025 Jun 25;184(7):441. doi: 10.1007/s00431-025-06267-6.
Bone marrow stromal cell antigen-1 (BST1) expression is elevated in a variety of human diseases, but its relationship with pediatric sepsis is unclear. This study aimed to investigate the expression of BST1 in pediatric sepsis patients and its value for diagnosing pediatric sepsis. Key neutrophil extracellular traps (NETs)-related genes that accurately diagnose pediatric sepsis patients were identified using various machine learning methods, including LASSO, RF, XGBoost, LightGBM, and Boruta algorithms. GSE13904, GSE26378, and GSE26440 datasets were used to examine BST1 expression in pediatric sepsis and to evaluate the suitability of BST1 for the diagnosis of pediatric sepsis using receiver operating characteristic curves. Additionally, the usefulness of diagnostic BST1 for identifying pediatric sepsis in actual clinical samples was evaluated by measuring serum BST1 levels using the enzyme-linked immunosorbent assay (ELISA) on 30 normal samples and 35 pediatric sepsis samples. Furthermore, a more thorough analysis of the connection between immune cells and BST1 expression was conducted utilizing the CIBERSORT database. Finally, we used ELISA to quantify myeloperoxidase (MPO)-DNA complex levels to assess the establishment of NETs. Integrating machine learning approaches, we identified BST1 as the most robust NET-associated diagnostic biomarker for pediatric sepsis. Comparative analysis revealed a significant elevation of BST1 expression in septic patients versus healthy controls, with pronounced discriminative capacity in a 65 clinical samples cohort (AUC value = 0.873), outperforming the effects of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell count (WBC), and C-reactive protein (CRP). Lastly, we discovered a substantial correlation between the BST1 level and the MPO-DNA complex level in the serum of pediatric sepsis children. Conclusion: Serum BST1 has the potential to be an effective diagnostic biomarker for pediatric sepsis. What is Known: • Pediatric sepsis is a major cause of morbidity, death, financial burden, and negative effects on health systems throughout the world. What is New: • Serum BST1 has the potential to be an effective diagnostic biomarker for pediatric sepsis.
骨髓基质细胞抗原-1（BST1）在多种人类疾病中表达升高，但其与小儿脓毒症的关系尚不清楚。本研究旨在探讨BST1在小儿脓毒症患者中的表达及其对小儿脓毒症的诊断价值。使用包括LASSO、随机森林（RF）、极端梯度提升（XGBoost）、轻量级梯度提升机（LightGBM）和博鲁塔算法在内的各种机器学习方法，确定准确诊断小儿脓毒症患者的关键中性粒细胞胞外陷阱（NETs）相关基因。利用GSE13904、GSE26378和GSE26440数据集检测小儿脓毒症中BST1的表达，并使用受试者工作特征曲线评估BST1对小儿脓毒症诊断的适用性。此外，通过对30份正常样本和35份小儿脓毒症样本进行酶联免疫吸附测定（ELISA）来检测血清BST1水平，评估诊断性BST1在实际临床样本中识别小儿脓毒症的有效性。此外，利用CIBERSORT数据库对免疫细胞与BST1表达之间的联系进行了更深入的分析。最后，我们使用ELISA对髓过氧化物酶（MPO）-DNA复合物水平进行定量，以评估NETs的形成。通过整合机器学习方法，我们确定BST1是小儿脓毒症最可靠的NETs相关诊断生物标志物。比较分析显示，脓毒症患者的BST1表达明显高于健康对照，在一个65例临床样本队列中具有显著的鉴别能力（AUC值 = 0.873），优于降钙素原（PCT）、白细胞计数（WBC）和C反应蛋白（CRP）等传统炎症指标。最后，我们发现小儿脓毒症患儿血清中BST1水平与MPO-DNA复合物水平之间存在显著相关性。结论：血清BST1有可能成为小儿脓毒症的有效诊断生物标志物。已知信息：• 小儿脓毒症是全球发病、死亡、经济负担以及对卫生系统产生负面影响的主要原因。新发现：• 血清BST1有可能成为小儿脓毒症的有效诊断生物标志物。
