Li Lin, Haijun Wang, Tianyu Chen, Wenjing Wang, Zi Wang, Yibing Cheng
Department of Emergency, Henan Province Engineering Research Center of Diagnosis and Treatment of Pediatric Infection and Critical Care, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, 450018, China.
JIUHE Diagnostics Co., Ltd, Zhengzhou, 450016, China.
Sci Rep. 2025 May 16;15(1):17090. doi: 10.1038/s41598-025-01895-2.
To study the differences in the urine metabolome between pediatric patients with severe Mycoplasma pneumoniae pneumonia (SMPP) and those with general Mycoplasma pneumoniae pneumonia (GMPP) via non-targeted metabolomics method, and potential biomarkers were explored through machine learning (ML) algorithms. The urine metabonomics data of 48 children with SMPP and 85 children with GMPP were collected via high performance liquid chromatography‒mass spectrometry (HPLC-MS/MS). The differential metabolites between the two groups were obtained via principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), and the significant metabolic pathways were screened via enrichment analysis. Potential biomarkers were identified using the random forest algorithm, and their relationships with clinical indicators were subsequently analyzed. A total of 136 significantly differential metabolites were identified in the urine samples from SMPP and GMPP. Of these, 68 metabolites were upregulated, and 68 were downregulated, predominantly belonging to the amino acid group. A total of 6 differential metabolic pathways were enriched, including Galactose metabolism, Pantothenate and CoA biosynthesis, Cysteine and methionine metabolism, Biotin metabolism, Glycine, serine and threonine metabolism, Arginine biosynthesis. Three significant potential biomarkers were identified through machine learning: 3-Hydroxyanthranilic acid (3-HAA), L-Kynurenine, and 16(R)-HETE. The area under the receiver operating characteristic curve (AUC) for this three-metabolite panel was 0.9142. There are great differences in the urine metabolome between SMPP and GMPP children, with multiple metabolic pathways being abnormally expressed. Three metabolites have been identified as potential biomarkers for the early detection of SMPP.
采用非靶向代谢组学方法研究重症支原体肺炎（SMPP）患儿与普通支原体肺炎（GMPP）患儿尿液代谢组的差异，并通过机器学习（ML）算法探索潜在生物标志物。通过高效液相色谱-质谱联用（HPLC-MS/MS）收集48例SMPP患儿和85例GMPP患儿的尿液代谢组学数据。通过主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）获得两组间的差异代谢物，并通过富集分析筛选显著代谢途径。使用随机森林算法识别潜在生物标志物，随后分析其与临床指标的关系。在SMPP和GMPP患儿的尿液样本中总共鉴定出136种显著差异代谢物。其中，68种代谢物上调，68种下调，主要属于氨基酸组。总共富集了6条差异代谢途径，包括半乳糖代谢、泛酸和辅酶A生物合成、半胱氨酸和甲硫氨酸代谢、生物素代谢、甘氨酸、丝氨酸和苏氨酸代谢、精氨酸生物合成。通过机器学习鉴定出三种显著潜在生物标志物：3-羟基邻氨基苯甲酸（3-HAA）、L-犬尿氨酸和16（R）-羟基二十碳四烯酸（16（R）-HETE）。这三种代谢物组合的受试者工作特征曲线（AUC）下面积为0.9142。SMPP和GMPP患儿的尿液代谢组存在很大差异，多条代谢途径异常表达。已鉴定出三种代谢物作为早期检测SMPP的潜在生物标志物。
