LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma

被引:41
作者
Liu, Xiang [1 ]
Zhang, Mingxin [2 ,3 ]
Cheng, Xiangming [2 ]
Liu, Xiaoyan [1 ]
Sun, Haidan [1 ]
Guo, Zhengguang [1 ]
Li, Jing [1 ]
Tang, Xiaoyue [1 ]
Wang, Zhan [2 ]
Sun, Wei [1 ]
Zhang, Yushi [2 ]
Ji, Zhigang [2 ]
机构
[1] Chinese Acad Med Sci, Peking Union Med Coll, Sch Basic Med, Inst Basic Med Sci, Beijing, Peoples R China
[2] Chinese Acad Med Sci, Peking Union Med Coll Hosp, Dept Urol, Beijing, Peoples R China
[3] Qingdao Univ, Dept Urol, Affiliated Hosp, Qingdao, Peoples R China
基金
中国国家自然科学基金;
关键词
bladder cancer; renal cell carcinoma; metabolomics; lipidomics; biomarker; PROSTATE-CANCER; LINOLENIC ACID; FATTY-ACIDS; BIOMARKER; KIDNEY; URINARY; PATHWAY; MARKERS; DISCOVERY; PROFILES;
D O I
10.3389/fonc.2020.00717
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Bladder cancer (BC) and Renal cell carcinoma(RCC) are the two most frequent genitourinary cancers in China. In this study, a comprehensive liquid chromatography-mass spectrometry (LC-MS) based method, which utilizes both plasma metabolomics and lipidomics platform, has been carried out to discriminate the global plasma profiles of 64 patients with BC, 74 patients with RCC, and 141 healthy controls. Apparent separation was observed between cancer (BC and RCC) plasma samples and controls. The area under the receiving operator characteristic curve (AUC) was 0.985 and 0.993 by plasma metabolomics and lipidomics, respectively (external validation group: AUC was 0.944 and 0.976, respectively). Combined plasma metabolomics and lipidomics showed good predictive ability with an AUC of 1 (external validation group: AUC = 0.99). Then, separation was observed between the BC and RCC samples. The AUC was 0.862, 0.853 and 0.939, respectively, by plasma metabolomics, lipidomics and combined metabolomics and lipidomics (external validation group: AUC was 0.802, 0.898, and 0.942, respectively). Furthermore, we also found eight metabolites that showed good predictive ability for BC, RCC and control discrimination. This study indicated that plasma metabolomics and lipidomics may be effective for BC, RCC and control discrimination, and combined plasma metabolomics and lipidomics showed better predictive performance. This study would provide a reference for BC and RCC biomarker discovery, not only for early detection and screening, but also for differential diagnosis.
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页数:14
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