LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma
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 discrimina...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2020.00717/full |
| _version_ | 1818430688838287360 |
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| author | Xiang Liu Mingxin Zhang Mingxin Zhang Xiangming Cheng Xiaoyan Liu Haidan Sun Zhengguang Guo Jing Li Xiaoyue Tang Zhan Wang Wei Sun Yushi Zhang Zhigang Ji |
| author_facet | Xiang Liu Mingxin Zhang Mingxin Zhang Xiangming Cheng Xiaoyan Liu Haidan Sun Zhengguang Guo Jing Li Xiaoyue Tang Zhan Wang Wei Sun Yushi Zhang Zhigang Ji |
| author_sort | Xiang Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T15:37:24Z |
| format | Article |
| id | doaj.art-fe119622f5b74c71a93d1c15180b65dc |
| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-12-14T15:37:24Z |
| publishDate | 2020-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-fe119622f5b74c71a93d1c15180b65dc2022-12-21T22:55:41ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-05-011010.3389/fonc.2020.00717494802LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell CarcinomaXiang Liu0Mingxin Zhang1Mingxin Zhang2Xiangming Cheng3Xiaoyan Liu4Haidan Sun5Zhengguang Guo6Jing Li7Xiaoyue Tang8Zhan Wang9Wei Sun10Yushi Zhang11Zhigang Ji12Institute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaDepartment of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, ChinaDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, ChinaInstitute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaInstitute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaInstitute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaInstitute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaInstitute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaDepartment of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, ChinaInstitute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, ChinaDepartment of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, ChinaDepartment of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, ChinaBladder 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.https://www.frontiersin.org/article/10.3389/fonc.2020.00717/fullbladder cancerrenal cell carcinomametabolomicslipidomicsbiomarker |
| spellingShingle | Xiang Liu Mingxin Zhang Mingxin Zhang Xiangming Cheng Xiaoyan Liu Haidan Sun Zhengguang Guo Jing Li Xiaoyue Tang Zhan Wang Wei Sun Yushi Zhang Zhigang Ji LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma Frontiers in Oncology bladder cancer renal cell carcinoma metabolomics lipidomics biomarker |
| title | LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma |
| title_full | LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma |
| title_fullStr | LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma |
| title_full_unstemmed | LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma |
| title_short | LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma |
| title_sort | lc ms based plasma metabolomics and lipidomics analyses for differential diagnosis of bladder cancer and renal cell carcinoma |
| topic | bladder cancer renal cell carcinoma metabolomics lipidomics biomarker |
| url | https://www.frontiersin.org/article/10.3389/fonc.2020.00717/full |
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