| 摘要: |
| 目的:通过分析原发性肾病综合征(PNS)患儿血浆代谢物,构建与PNS相关的代谢途径网络,发现潜在的生物标志物以辅助临床诊断。方法:采用气相色谱质谱联用(GC-MS)技术分析22例PNS患儿及22例健康儿童血浆代谢物,利用预处理软件XCMS进行数据预处理,主成分分析(PCA)与偏最小二乘判别分析(PLS-DA)对两组差异性代谢产物进行分析。结果:构建了PCA与PLS-LDA模型,PCA模型中变量的可解释率和可预测率为43.6% 和12.9%,PLS鄄LDA 模型中变量的可解释率和可预测率分别为79.8%和59.2%,共筛选出15种差异性代谢物,主要有胆固醇、柠檬酸、亚油酸及氨基酸等,其中乳酸盐、亚油酸、软
脂酸、丙酮酸、胆固醇含量升高,其余10种代谢物含量降低。结论:筛选出的15种差异性代谢物变化导致糖酵解、糖异生、脂类代谢和蛋白质代谢减弱,三羧酸循环紊乱。该研究为进一步明确PNS发病机制提供了新的科学依据,并有助于疾病诊断和预后评估。 |
| 关键词: 原发性肾病综合征 气相色谱质谱联用技术 代谢组学 |
| DOI：doi: 10.13407/j.cnki.jpp.1672-108X.2017.11.001 |
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| 基金项目: |
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| Application of Gas Chromatography Mass Spectrometry Based Metabonomics on Primary Nephritic Syndrome in Children |
| Wang Fangjie 1, Liu Shao 2, Wang Ting 1, Luo Fangmei 1, Xiang Xiaoqin 1, He Zhoukang 1 |
| (1. Hunan Children's Hospital, Hunan Changsha 410007, China; 2. Xiangya Hospital of Central South University, Hunan Changsha 410008, China) |
| Abstract: |
| Objective: To build a network of metabolic pathways associated with primary nephritic syndrome(PNS) and to explore the biomarkers to help the diagnosis of the disease by analyzing plasma metabolites change in children with PNS. Methods: The plasma metabolites of 22 PNS children and 22 healthy children were analyzed by gas chromatography mass spectrometry ( GC-MS). Preprocessing software XCMS was used to the data preprocessing, the differential metabolites between PNS children and control subjects were analyzed by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Results: PCA and PLS-DA model were established. The interpretation ratio and prediction ratio of variables in PCA model were 43.6% and 12.9% respectively, while the interpretation ratio and prediction ratio of variables in PLS-LDA model were 79.8% and 59.2%. Fifteen kinds of metabolites were selected as the characteristic biomarkers, including cholesterol, citric acid, linoleic acid and amino acids. While the lactate, linoleic acid, hexadecanoic acid, pyruvic acid, cholesterol were increased, the others 10 kinds of metabolites were reduced obviously. Conclusion: The changes of 15 metabolites lead to the weak of glycolysis, glyconeogenesis and lipid metabolism, protein metabolism, tricarboxylic acid cycle disorders. These characteristic biomarkers provide new scientific basis for the pathogenesis of PNS and help the diagnosis and prognosis evaluation of the disease. |
| Key words: primary nephritic syndrome gas chromatography mass spectrometry metabonomics |
