Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC

被引:34
|
作者
Liu, Xiaoyan [1 ]
Zhang, Mingxin [2 ,3 ]
Liu, Xiang [1 ]
Sun, Haidan [1 ]
Guo, Zhengguang [1 ]
Tang, Xiaoyue [1 ]
Wang, Zhan [2 ]
Li, Jing [1 ]
Li, Hanzhong [2 ]
Sun, Wei [1 ]
Zhang, Yushi [2 ]
机构
[1] Chinese Acad Med Sci, Peking Union Med Coll, Inst Basic Med Sci, Sch Basic Med, Beijing, Peoples R China
[2] Chinese Acad Med Sci, Peking Union Med Coll Hosp, Dept Urol, Beijing, Peoples R China
[3] Qingdao Univ, Affiliated Hosp, Dept Urol, Qingdao, Shandong, Peoples R China
来源
FRONTIERS IN ONCOLOGY | 2019年 / 9卷
基金
北京市自然科学基金; 中国国家自然科学基金;
关键词
renal cell carcinoma; metabolomics; benign tumors; biomarker; tryptophan metabolism; KYNURENINE PATHWAY; KIDNEY CANCER; INFLAMMATION; BIOMARKER; TUMOR; IDO;
D O I
10.3389/fonc.2019.00663
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Renal cell carcinoma (RCC) is the second most lethal urinary cancer. RCC is frequently asymptomatic and it is already metastatic at diagnosis. There is an urgent necessity for RCC specific biomarkers selection for diagnostic and prognostic purposes. In present study, we applied liquid chromatography-mass spectrometry (LC-MS) based metabolomics to analyze urine samples of 100 RCC, 34 benign kidney tumors and 129 healthy controls. Differential metabolites were analyzed to investigate if urine metabolites could differentiate RCC from non-RCC. A panel consisting of 9 metabolites showed the best predictive ability for RCC from the health controls with an area under the curve (AUC) values of 0.905 for the training dataset and 0.885 for the validation dataset. Separation was observed between the RCC and benign samples with an AUC of 0.816. RCC clinical stages (T1 and T2 vs. T3 and T4) could be separated using a panel of urine metabolites with an AUC of 0.813. One metabolite, N-formylkynurenine, was discovered to have potential value for RCC diagnosis from non-RCC subjects with an AUC of 0.808. Pathway enrichment analysis indicated that tryptophan metabolism was an important pathway in RCC. Our data concluded that urine metabolomics could be used for RCC diagnosis and would provide candidates for further targeted metabolomics analysis of RCC.
引用
收藏
页数:9
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