基于气相色谱质谱技术的肾细胞癌患者尿液代谢组学分析

目的对肾细胞癌（renal cell cancer, RCC）患者的尿液进行代谢组学分析，建立数据模型并筛选特征代谢标志物。方法采
用气相色谱质谱联用技术（gas chromatography mass spectrometry, GC-MS）分析27例RCC患者，26例泌尿系其它肿瘤患者及
26例健康人的尿液，利用SIMCA-P+12.0.1.0软件进行主成分分析（principal component analysis, PCA）及正交偏最小二乘法-判
别分析（orthogonal partial least-squares discriminant analysis,0PLS-DA），并筛选特征代谢产物。结果构建了PCA（R2X=0.846，
Q2=0.575）和OPLS-DA（R2X=0.736，R2Y=0.974，Q2Y=0.897）模型，筛选出14种差异代谢产物，主要是有机酸、马尿酸、色氨酸
及其降解产物，其中戊酸、丙二酸、戊二酸、己二酸、吲哚乙酸、氨基喹啉、喹啉及色氨酸在RCC患者尿液中的含量显著高于正常
人（P<0.01），同时RCC组的尿液中戊酸、苯丙氨酸、6-甲氧基-硝基喹啉的含量显著高于泌尿系其它肿瘤患者（P<0.01）。结论
基于GC-MS的代谢组学方法可以区分RCC患者，筛选出的代谢产物可能是RCC的诊断标志物，可进行深入研究。

Urinary metabolomics study of renal cell carcinoma based on gas chromatography-mass spectrometry

Objective To identify the biomarkers of renal cell cancer (RCC) through urine metabolic analysis. Methods Urine
samples of 27 RCC patients, 26 patients with other urinary cancers and 26 healthy volunteers were examined with gas
chromatography-mass spectrometry (GC-MS). SIMCA-P+12.0.1.0 software was used for principal component analysis (PCA)
and orthogonal partial least-squares discriminant analysis (OPLS-DA) to screen for the differential metabolites. Results PCA
(R2X=0.846, Q2=0.575) and OPLS-DA (R2X=0.736, R2Y=0.974, Q2Y=0.897) model were established for the RCC patients and
control subjects. Fourteen metabolites were selected as the characteristic metabolites, including pentanoic acid, malonic acid,
glutaric acid, adipic acid, amino quinoline, quinoline, indole acetic acid, and tryptophan, whose levels in the urine were
significantly higher in the RCC patients than in the normal subjects (P<0.01); the RCC patients showed significantly higher
urine contents of pentanoic acid, phenylalanine, and 6-methoxy-nitro quinoline than those with other urinary tumors (P<0.01).
Conclusion The urine metabolites identified based on GC-MS analysis can distinguish RCC patients from patients with other
urinary cancers and healthy subjects, suggesting their potential as diagnostic markers for RCC.