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Dong X, Qu Y, Sheng T, Fan Y, Chen S, Yuan Q, Ma G, Ge Y. HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers. Aging (Albany NY) 2024; 16:7487-7504. [PMID: 38683118 PMCID: PMC11087094 DOI: 10.18632/aging.205779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/03/2024] [Indexed: 05/01/2024]
Abstract
Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.
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Affiliation(s)
- Xun Dong
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yaoyao Qu
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Tongtong Sheng
- School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yuanming Fan
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Silu Chen
- School of Public Health, Nanjing Medical University, Nanjing, China
| | - Qinbo Yuan
- Department of Urology, Wuxi Fifth People’s Hospital, Wuxi, China
| | - Gaoxiang Ma
- State Key Laboratory of Natural Medicines, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, China
- The Clinical Metabolomics Center, China Pharmaceutical University, Nanjing, China
- Deparment of Oncology, Pukou Hospital of Chinese Medicine affiliated to China Pharmaceutical University, Nanjing, China
| | - Yuqiu Ge
- Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, China
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2
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Fernandez Requena B, Gonzalez-Riano C, Barbas C. Addressing the untargeted lipidomics challenge in urine samples: Comparative study of extraction methods by UHPLC-ESI-QTOF-MS. Anal Chim Acta 2024; 1299:342433. [PMID: 38499427 DOI: 10.1016/j.aca.2024.342433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/20/2024]
Abstract
Urine analysis has remained a fundamental and widely used method in clinical diagnostics for over a century. With its minimal invasive nature and comprehensive range of analytes, urine has established itself as a clinical diagnostic tool for various disorders, including renal, urological, metabolic, and endocrine diseases. Furthermore, urine's unique attributes make it an attractive matrix for biomarker discovery, as well as in assessing the metabolic and physiological states of patients and healthy individuals alike. However, limitations in our knowledge of average values and sources of urinary lipids decrease the wider clinical application of urinary lipidomics. In this context, untargeted lipidomics analysis relies heavily on the extraction and analysis of lipids in biological samples. Nevertheless, this type of analysis presents challenges in lipid identification due to the diverse nature of lipids. Therefore, proper sample treatment before analysis is crucial to obtain robust and reproducible lipidomic profiles. To address this gap, we conducted a comparative study of a urine pool sample collected from twenty healthy volunteers using four different lipid extraction methods: one biphasic and three monophasic protocols. The extracted lipids were then analyzed using UHPLC-MS and MS/MS, and the semi-quantification of all the accurately annotated lipid species was performed for each extraction method.
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Affiliation(s)
- Belen Fernandez Requena
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, España
| | - Carolina Gonzalez-Riano
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, España
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, España.
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3
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Lin L, Tang Y, Ning K, Li X, Hu X. Investigating the causal associations between metabolic biomarkers and the risk of kidney cancer. Commun Biol 2024; 7:398. [PMID: 38561482 PMCID: PMC10984917 DOI: 10.1038/s42003-024-06114-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024] Open
Abstract
Metabolic reprogramming plays an important role in kidney cancer. We aim to investigate the causal effect of 249 metabolic biomarkers on kidney cancer from population-based data. This study extracts data from previous genome wide association studies with large sample size. The primary endpoint is random-effect inverse variance weighted (IVW). After completing 249 times of two-sample Mendelian randomization analysis, those significant metabolites are included for further sensitivity analysis. According to a strict Bonferrion-corrected level (P < 2e-04), we only find two metabolites that are causally associated with renal cancer. They are lactate (OR:3.25, 95% CI: 1.84-5.76, P = 5.08e-05) and phospholipids to total lipids ratio in large LDL (low density lipoprotein) (OR: 0.63, 95% CI: 0.50-0.80, P = 1.39e-04). The results are stable through all the sensitivity analysis. The results emphasize the central role of lactate in kidney tumorigenesis and provide novel insights into possible mechanism how phospholipids could affect kidney tumorigenesis.
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Affiliation(s)
- Lede Lin
- Department of Urology and Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yaxiong Tang
- Department of Urology and Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kang Ning
- Department of Head and Neck Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Xiang Li
- Department of Urology and Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Xu Hu
- Department of Urology and Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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4
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Singh A, Kinnebrew G, Hsu PC, Weng DY, Song MA, Reisinger SA, McElroy JP, Keller-Hamilton B, Ferketich AK, Freudenheim JL, Shields PG. Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis. Metabolites 2023; 13:899. [PMID: 37623843 PMCID: PMC10456720 DOI: 10.3390/metabo13080899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/26/2023] Open
Abstract
Obesity in children and adolescents has increased globally. Increased body mass index (BMI) during adolescence carries significant long-term adverse health outcomes, including chronic diseases such as cardiovascular disease, stroke, diabetes, and cancer. Little is known about the metabolic consequences of changes in BMI in adolescents outside of typical clinical parameters. Here, we used untargeted metabolomics to assess changing BMI in male adolescents. Untargeted metabolomic profiling was performed on urine samples from 360 adolescents using UPLC-QTOF-MS. The study includes a baseline of 235 subjects in a discovery set and 125 subjects in a validation set. Of them, a follow-up of 81 subjects (1 year later) as a replication set was studied. Linear regression analysis models were used to estimate the associations of metabolic features with BMI z-score in the discovery and validation sets, after adjusting for age, race, and total energy intake (kcal) at false-discovery-rate correction (FDR) ≤ 0.1. We identified 221 and 16 significant metabolic features in the discovery and in the validation set, respectively. The metabolites associated with BMI z-score in validation sets are glycylproline, citrulline, 4-vinylsyringol, 3'-sialyllactose, estrone sulfate, carnosine, formiminoglutamic acid, 4-hydroxyproline, hydroxyprolyl-asparagine, 2-hexenoylcarnitine, L-glutamine, inosine, N-(2-Hydroxyphenyl) acetamide glucuronide, and galactosylhydroxylysine. Of those 16 features, 9 significant metabolic features were associated with a positive change in BMI in the replication set 1 year later. Histidine and arginine metabolism were the most affected metabolic pathways. Our findings suggest that obesity and its metabolic outcomes in the urine metabolome of children are linked to altered amino acids, lipid, and carbohydrate metabolism. These identified metabolites may serve as biomarkers and aid in the investigation of obesity's underlying pathological mechanisms. Whether these features are associated with the development of obesity, or a consequence of changing BMI, requires further study.
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Affiliation(s)
- Amarnath Singh
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210-1240, USA; (A.S.); (D.Y.W.)
| | - Garrett Kinnebrew
- Department of Biomedical Informatics, Biomedical Informatics Shared Resources (BISR), The Ohio State University, Columbus, OH 43210-1240, USA;
| | - Ping-Ching Hsu
- Department of Environmental Health Sciences, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Daniel Y. Weng
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210-1240, USA; (A.S.); (D.Y.W.)
| | - Min-Ae Song
- College of Public Health, The Ohio State University, Columbus, OH 43210-1240, USA; (M.-A.S.); (A.K.F.)
| | - Sarah A. Reisinger
- Center for Tobacco Research, Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210-1240, USA; (S.A.R.); (B.K.-H.)
| | - Joseph P. McElroy
- Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210-1240, USA;
| | - Brittney Keller-Hamilton
- Center for Tobacco Research, Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210-1240, USA; (S.A.R.); (B.K.-H.)
- Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210-1240, USA
| | - Amy K. Ferketich
- College of Public Health, The Ohio State University, Columbus, OH 43210-1240, USA; (M.-A.S.); (A.K.F.)
| | - Jo L. Freudenheim
- Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY 14214, USA;
| | - Peter G. Shields
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210-1240, USA; (A.S.); (D.Y.W.)
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Zapater-Moros A, Díaz-Beltrán L, Gámez-Pozo A, Trilla-Fuertes L, Lumbreras-Herrera MI, López-Camacho E, González-Olmedo C, Espinosa E, Zamora P, Sánchez-Rovira P, Fresno Vara JÁ. Metabolomics unravels subtype-specific characteristics related to neoadjuvant therapy response in breast cancer patients. Metabolomics 2023; 19:60. [PMID: 37344702 DOI: 10.1007/s11306-023-02024-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/09/2023] [Indexed: 06/23/2023]
Abstract
INTRODUCTION Breast cancer is the most diagnosed tumor and the leading cause of cancer death in women worldwide. Metabolomics allows the quantification of the entire set of metabolites in blood samples, making it possible to study differential metabolomics patterns related to neoadjuvant treatment in the breast cancer neoadjuvant setting. OBJECTIVES Characterizing metabolic differences in breast cancer blood samples according to their response to neoadjuvant treatment. METHODS One hundred and three plasma samples of breast cancer patients, before receiving neoadjuvant treatment, were analyzed through UPLC-MS/MS metabolomics. Then, metabolomics data were analyzed using probabilistic graphical models and biostatistics methods. RESULTS Metabolomics data allowed the identification of differences between groups according to response to neoadjuvant treatment. These differences were specific to each breast cancer subtype. Patients with HER2+ tumors showed differences in metabolites related to amino acids and carbohydrates pathways between the two pathological response groups. However, patients with triple-negative tumors showed differences in metabolites related to the long-chain fatty acids pathway. Patients with Luminal B tumors showed differences in metabolites related to acylcarnitine pathways. CONCLUSIONS It is possible to identify differential metabolomics patterns between complete and partial responses to neoadjuvant therapy, being this metabolomic profile specific for each breast cancer subtype.
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Affiliation(s)
- Andrea Zapater-Moros
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
- Biomedica Molecular Medicine SL, C/Faraday 7, 28049, Madrid, Spain
- Biomedical Research Networking Center On Oncology-CIBERONC, ISCIII, Av. Monforte de Lemos 5, 28029, Madrid, Spain
| | - Leticia Díaz-Beltrán
- Systems Biology Unit, Department of Experimental Biology, University of Jaén, Campus Las Lagunillas s/n, 23071, Jaén, Spain
- Medical Oncology Department, Hospital Universitario de Jaén, Av. del Ejército Español, 10, 23007, Jaén, Spain
| | - Angelo Gámez-Pozo
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
- Biomedica Molecular Medicine SL, C/Faraday 7, 28049, Madrid, Spain
| | - Lucía Trilla-Fuertes
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - María Isabel Lumbreras-Herrera
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | | | - Carmen González-Olmedo
- Medical Oncology Department, Hospital Universitario de Jaén, Av. del Ejército Español, 10, 23007, Jaén, Spain
| | - Enrique Espinosa
- Medical Oncology Service, La Paz University Hospital-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
- Biomedical Research Networking Center On Oncology-CIBERONC, ISCIII, Av. Monforte de Lemos 5, 28029, Madrid, Spain
| | - Pilar Zamora
- Medical Oncology Service, La Paz University Hospital-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
- Biomedical Research Networking Center On Oncology-CIBERONC, ISCIII, Av. Monforte de Lemos 5, 28029, Madrid, Spain
| | - Pedro Sánchez-Rovira
- Medical Oncology Department, Hospital Universitario de Jaén, Av. del Ejército Español, 10, 23007, Jaén, Spain
| | - Juan Ángel Fresno Vara
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain.
- Biomedica Molecular Medicine SL, C/Faraday 7, 28049, Madrid, Spain.
- Biomedical Research Networking Center On Oncology-CIBERONC, ISCIII, Av. Monforte de Lemos 5, 28029, Madrid, Spain.
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Suri GS, Kaur G, Carbone GM, Shinde D. Metabolomics in oncology. Cancer Rep (Hoboken) 2023; 6:e1795. [PMID: 36811317 PMCID: PMC10026298 DOI: 10.1002/cnr2.1795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/15/2023] [Accepted: 02/10/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Oncogenic transformation alters intracellular metabolism and contributes to the growth of malignant cells. Metabolomics, or the study of small molecules, can reveal insight about cancer progression that other biomarker studies cannot. Number of metabolites involved in this process have been in spotlight for cancer detection, monitoring, and therapy. RECENT FINDINGS In this review, the "Metabolomics" is defined in terms of current technology having both clinical and translational applications. Researchers have shown metabolomics can be used to discern metabolic indicators non-invasively using different analytical methods like positron emission tomography, magnetic resonance spectroscopic imaging etc. Metabolomic profiling is a powerful and technically feasible way to track changes in tumor metabolism and gauge treatment response across time. Recent studies have shown metabolomics can also predict individual metabolic changes in response to cancer treatment, measure medication efficacy, and monitor drug resistance. Its significance in cancer development and treatment is summarized in this review. CONCLUSION Although in infancy, metabolomics can be used to identify treatment options and/or predict responsiveness to cancer treatments. Technical challenges like database management, cost and methodical knowhow still persist. Overcoming these challenges in near further can help in designing new treatment régimes with increased sensitivity and specificity.
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Affiliation(s)
- Gurparsad Singh Suri
- Department of Biological Sciences, California Baptist University, Riverside, California, USA
| | - Gurleen Kaur
- Department of Biological Sciences, California Baptist University, Riverside, California, USA
| | - Giuseppina M Carbone
- Institute of Oncology Research (IOR), Universita' della Svizzera Italiana (USI), Bellinzona, Switzerland
| | - Dheeraj Shinde
- Institute of Oncology Research (IOR), Universita' della Svizzera Italiana (USI), Bellinzona, Switzerland
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Wu R, Wang K, Gai Y, Li M, Wang J, Wang C, Zhang Y, Xiao Z, Jiang D, Gao Z, Xia X. Nanomedicine for renal cell carcinoma: imaging, treatment and beyond. J Nanobiotechnology 2023; 21:3. [PMID: 36597108 PMCID: PMC9809106 DOI: 10.1186/s12951-022-01761-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 12/26/2022] [Indexed: 01/04/2023] Open
Abstract
The kidney is a vital organ responsible for maintaining homeostasis in the human body. However, renal cell carcinoma (RCC) is a common malignancy of the urinary system and represents a serious threat to human health. Although the overall survival of RCC has improved substantially with the development of cancer diagnosis and management, there are various reasons for treatment failure. Firstly, without any readily available biomarkers, timely diagnosis has been greatly hampered. Secondly, the imaging appearance also varies greatly, and its early detection often remains difficult. Thirdly, chemotherapy has been validated as unavailable for treating renal cancer in the clinic due to its intrinsic drug resistance. Concomitant with the progress of nanotechnological methods in pharmaceuticals, the management of kidney cancer has undergone a transformation in the recent decade. Nanotechnology has shown many advantages over widely used traditional methods, leading to broad biomedical applications ranging from drug delivery, prevention, diagnosis to treatment. This review focuses on nanotechnologies in RCC management and further discusses their biomedical translation with the aim of identifying the most promising nanomedicines for clinical needs. As our understanding of nanotechnologies continues to grow, more opportunities to improve the management of renal cancer are expected to emerge.
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Affiliation(s)
- Ruolin Wu
- grid.33199.310000 0004 0368 7223Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022 Hubei People’s Republic of China ,grid.412839.50000 0004 1771 3250Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, China
| | - Keshan Wang
- grid.33199.310000 0004 0368 7223Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Yongkang Gai
- grid.33199.310000 0004 0368 7223Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022 Hubei People’s Republic of China ,grid.412839.50000 0004 1771 3250Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, China
| | - Mengting Li
- grid.33199.310000 0004 0368 7223Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022 Hubei People’s Republic of China ,grid.412839.50000 0004 1771 3250Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, China
| | - Jingjing Wang
- grid.33199.310000 0004 0368 7223Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022 Hubei People’s Republic of China ,grid.412839.50000 0004 1771 3250Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, China
| | - Chenyang Wang
- grid.33199.310000 0004 0368 7223Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022 Hubei People’s Republic of China ,grid.412839.50000 0004 1771 3250Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, China
| | - Yajing Zhang
- grid.33199.310000 0004 0368 7223Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022 Hubei People’s Republic of China ,grid.412839.50000 0004 1771 3250Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, China
| | - Zhiwei Xiao
- grid.413247.70000 0004 1808 0969Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Dawei Jiang
- grid.33199.310000 0004 0368 7223Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022 Hubei People’s Republic of China ,grid.412839.50000 0004 1771 3250Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, China
| | - Zairong Gao
- grid.33199.310000 0004 0368 7223Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022 Hubei People’s Republic of China ,grid.412839.50000 0004 1771 3250Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, China
| | - Xiaotian Xia
- grid.33199.310000 0004 0368 7223Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022 Hubei People’s Republic of China ,grid.412839.50000 0004 1771 3250Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China ,grid.419897.a0000 0004 0369 313XKey Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, China
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McClain KM, Sampson JN, Petrick JL, Mazzilli KM, Gerszten RE, Clish CB, Purdue MP, Lipworth L, Moore SC. Metabolomic Analysis of Renal Cell Carcinoma in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Metabolites 2022; 12:metabo12121189. [PMID: 36557227 PMCID: PMC9785244 DOI: 10.3390/metabo12121189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 12/02/2022] Open
Abstract
Background: In the US in 2021, 76,080 kidney cancers are expected and >80% are renal cell carcinomas (RCCs). Along with excess fat, metabolic dysfunction is implicated in RCC etiology. To identify RCC-associated metabolites, we conducted a 1:1 matched case−control study nested within the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Methods: We measured 522 serum metabolites in 267 cases/control pairs. Cases were followed for a median 7.1 years from blood draw to diagnosis. Using conditional logistic regression, we computed adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing risk between 90th and 10th percentiles of log metabolite intensity, with the significance threshold at a false discovery rate <0.20. Results: Four metabolites were inversely associated with risk of RCC during follow-up—C38:4 PI, C34:0 PC, C14:0 SM, and C16:1 SM (ORs ranging from 0.33−0.44). Two were positively associated with RCC risk—C3-DC-CH3 carnitine and C5 carnitine (ORs = 2.84 and 2.83, respectively). These results were robust when further adjusted for metabolic risk factors (body mass index (BMI), physical activity, diabetes/hypertension history). Metabolites associated with RCC had weak correlations (|r| < 0.2) with risk factors of BMI, physical activity, smoking, alcohol, and diabetes/hypertension history. In mutually adjusted models, three metabolites (C38:4 PI, C14:0 SM, and C3-DC-CH3 carnitine) were independently associated with RCC risk. Conclusions: Serum concentrations of six metabolites were associated with RCC risk, and three of these had independent associations from the mutually adjusted model. These metabolites may point toward new biological pathways of relevance to this malignancy.
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Affiliation(s)
- Kathleen M. McClain
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
- Correspondence: ; Tel.: +240-276-6317
| | - Joshua N. Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | | | - Kaitlyn M. Mazzilli
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Clary B. Clish
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mark P. Purdue
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Loren Lipworth
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Steven C. Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
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9
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Nizioł J, Ossoliński K, Płaza-Altamer A, Kołodziej A, Ossolińska A, Ossoliński T, Ruman T. Untargeted ultra-high-resolution mass spectrometry metabolomic profiling of blood serum in bladder cancer. Sci Rep 2022; 12:15156. [PMID: 36071106 PMCID: PMC9452537 DOI: 10.1038/s41598-022-19576-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/31/2022] [Indexed: 11/21/2022] Open
Abstract
Bladder cancer (BC) is a common urological cancer of high mortality and recurrence rates. Currently, cystoscopy is performed as standard examination for the diagnosis and subsequent monitoring for recurrence of the patients. Frequent expensive and invasive procedures may deterrent patients from regular follow-up screening, therefore it is important to look for new non-invasive methods to aid in the detection of recurrent and/or primary BC. In this study, ultra-high-performance liquid chromatography coupled with ultra-high-resolution mass spectrometry was employed for non-targeted metabolomic profiling of 200 human serum samples to identify biochemical signatures that differentiate BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation revealed twenty-seven metabolites that differentiate between BC patients from NCs. Abundances of these metabolites displayed statistically significant differences in two independent training and validation sets. Twenty-three serum metabolites were also found to be distinguishing between low- and high-grade of BC patients and controls. Thirty-seven serum metabolites were found to differentiate between different stages of BC. The results suggest that measurement of serum metabolites may provide more facile and less invasive diagnostic methodology for detection of bladder cancer and recurrent disease management.
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Affiliation(s)
- Joanna Nizioł
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave., 35-959, Rzeszow, Poland.
| | - Krzysztof Ossoliński
- Department of Urology, John Paul II Hospital, Grunwaldzka 4 St., 36-100, Kolbuszowa, Poland
| | - Aneta Płaza-Altamer
- Doctoral School of Engineering and Technical Sciences at the Rzeszów University of Technology, 8 Powstańców Warszawy Ave., 35-959, Rzeszow, Poland
| | - Artur Kołodziej
- Doctoral School of Engineering and Technical Sciences at the Rzeszów University of Technology, 8 Powstańców Warszawy Ave., 35-959, Rzeszow, Poland
| | - Anna Ossolińska
- Department of Urology, John Paul II Hospital, Grunwaldzka 4 St., 36-100, Kolbuszowa, Poland
| | - Tadeusz Ossoliński
- Department of Urology, John Paul II Hospital, Grunwaldzka 4 St., 36-100, Kolbuszowa, Poland
| | - Tomasz Ruman
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave., 35-959, Rzeszow, Poland
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10
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Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis. Biochem Biophys Rep 2022; 31:101318. [PMID: 35967759 PMCID: PMC9363947 DOI: 10.1016/j.bbrep.2022.101318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/05/2022] [Accepted: 07/19/2022] [Indexed: 12/30/2022] Open
Abstract
Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC. Multiplatform untargeted metabolomics analysis was applied for selection of tentative diagnostic indicators of RCC. LC-MS, GC-MS and CE-MS techniques were utilized for analysis of urine samples collected from RCC and ccRCC patients. Alterations in amino acid, purine, and pyrimidine metabolism, as well as TCA cycle and energy processes, were observed.
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11
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Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines. Int J Mol Sci 2022; 23:ijms23073867. [PMID: 35409231 PMCID: PMC8998886 DOI: 10.3390/ijms23073867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/24/2022] [Accepted: 03/29/2022] [Indexed: 02/01/2023] Open
Abstract
The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).
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12
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Wang R, Kang H, Zhang X, Nie Q, Wang H, Wang C, Zhou S. Urinary metabolomics for discovering metabolic biomarkers of bladder cancer by UPLC-MS. BMC Cancer 2022; 22:214. [PMID: 35220945 PMCID: PMC8883652 DOI: 10.1186/s12885-022-09318-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 02/21/2022] [Indexed: 12/24/2022] Open
Abstract
Bladder cancer (BC) is one of the most frequent cancer in the world, and its incidence is rising worldwide, especially in developed countries. Urine metabolomics is a powerful approach to discover potential biomarkers for cancer diagnosis. In this study, we applied an ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) method to profile the metabolites in urine from 29 bladder cancer patients and 15 healthy controls. The differential metabolites were extracted and analyzed by univariate and multivariate analysis methods. Together, 19 metabolites were discovered as differently expressed biomarkers in the two groups, which mainly related to the pathways of phenylacetate metabolism, propanoate metabolism, fatty acid metabolism, pyruvate metabolism, arginine and proline metabolism, glycine and serine metabolism, and bile acid biosynthesis. In addition, a subset of 11 metabolites of those 19 ones were further filtered as potential biomarkers for BC diagnosis by using logistic regression model. The results revealed that the area under the curve (AUC) value, sensitivity and specificity of receiving operator characteristic (ROC) curve were 0.983, 95.3% and 100%, respectively, indicating an excellent discrimination power for BC patients from healthy controls. It was the first time to reveal the potential diagnostic markers of BC by metabolomics, and this will provide a new sight for exploring the biomarkers of the other disease in the future work.
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Affiliation(s)
- Rui Wang
- Zibo Municipal Hospital, Zibo, Shandong, 255400, China
| | - Huaixing Kang
- Department of clinical laboratory, Central Hospital of Xiangtan, Xiangtan, Hunan, 411100, China
| | - Xu Zhang
- Department of Urology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, China
| | - Qing Nie
- Yaneng Bioscience, Co., Ltd, Shenzhen, Guangdong, 518100, China
| | - Hongling Wang
- Zibo Municipal Hospital, Zibo, Shandong, 255400, China.
| | - Chaojun Wang
- Department of Urology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, China.
| | - Shujun Zhou
- Yaneng Bioscience, Co., Ltd, Shenzhen, Guangdong, 518100, China.
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13
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Han J, Li Q, Chen Y, Yang Y. Recent Metabolomics Analysis in Tumor Metabolism Reprogramming. Front Mol Biosci 2021; 8:763902. [PMID: 34901157 PMCID: PMC8660977 DOI: 10.3389/fmolb.2021.763902] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/08/2021] [Indexed: 12/15/2022] Open
Abstract
Metabolic reprogramming has been suggested as a hallmark of cancer progression. Metabolomic analysis of various metabolic profiles represents a powerful and technically feasible method to monitor dynamic changes in tumor metabolism and response to treatment over the course of the disease. To date, numerous original studies have highlighted the application of metabolomics to various aspects of tumor metabolic reprogramming research. In this review, we summarize how metabolomics techniques can help understand the effects that changes in the metabolic profile of the tumor microenvironment on the three major metabolic pathways of tumors. Various non-invasive biofluids are available that produce accurate and useful clinical information on tumor metabolism to identify early biomarkers of tumor development. Similarly, metabolomics can predict individual metabolic differences in response to tumor drugs, assess drug efficacy, and monitor drug resistance. On this basis, we also discuss the application of stable isotope tracer technology as a method for the study of tumor metabolism, which enables the tracking of metabolite activity in the body and deep metabolic pathways. We summarize the multifaceted application of metabolomics in cancer metabolic reprogramming to reveal its important role in cancer development and treatment.
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Affiliation(s)
- Jingjing Han
- Department of Anesthesiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qian Li
- Department of Anesthesiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Chen
- Department of Anesthesiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yonglin Yang
- Division of Infectious Diseases, Taizhou Clinical Medical School of Nanjing Medical University (Taizhou People's Hospital), Taizhou, China
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Sun Q, Zhao H, Liu Z, Wang F, He Q, Xiu C, Guo L, Tian Q, Fan L, Sun J, Sun D. Identifying potential metabolic tissue biomarkers for papillary thyroid cancer in different iodine nutrient regions. Endocrine 2021; 74:582-591. [PMID: 34075541 DOI: 10.1007/s12020-021-02773-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 05/19/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE To investigate the applicability of metabolomics to select thyroid cancer-associated biomarkers and discover the effects of iodine on metabolic changes in thyroid cancer. METHODS In this study, a total of 33 papillary thyroid cancer (PTC) patients from areas with iodine excess and 32 PTC patients from areas with adequate iodine were recruited, and their cancerous tissue and paracancerous tissue were collected. These specimens were analyzed by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/QTOF/MS) in conjunction with multivariate statistical analysis. RESULTS Good separations were obtained for PTC tissue vs. paracancerous tissue, and 15 metabolites, including L-octanoylcarnitine, N-arachidonoylglycine, and others were found to be disturbed in PTC tissue. Moreover, the metabolic profile presented considerable separation between PTC tissue from different iodine areas, and 15 metabolomic biomarkers were found to be differentially expressed. Among them, 10 metabolites, including arachidonoylcarnitine and LysoPCs, were related to thyroid cancer and excess iodine. These biomarkers play a role in arachidonic acid metabolism pathways and others. In addition, biomarkers such as 3,5-tetradecadiencarnitine and oxidized glutathione were significantly correlated with thyroid function, and biomarkers such as L-octanoylcarnitine and arachidonic acid were significantly correlated with the clinical characteristics of PTC. CONCLUSIONS Distinct differences in metabolic profiles were found to exist between PTCs from areas with different levels of iodine nutrition. The identified biomarkers have significant potential for diagnosing PTC and investigating its underlying mechanisms.
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Affiliation(s)
- Qihao Sun
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongjian Zhao
- General Surgery Department, People's Hospital of Chengwu County, Heze, Shandong, China
| | - Zhiyong Liu
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
| | - Fengqian Wang
- Public Health College, Harbin Medical University, Harbin, Heilongjiang, China
| | - Qian He
- Shandong First Medical University, Tai'an, Shandong, China
| | - Cheng Xiu
- Department of Head and Neck Oncology, The Third Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Lunhua Guo
- Department of Head and Neck Oncology, The Third Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Qiushi Tian
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China
| | - Lijun Fan
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China.
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA.
| | - Ji Sun
- Department of Head and Neck Oncology, The Third Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
| | - Dianjun Sun
- Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, Heilongjiang, China.
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15
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Morozumi K, Kawasaki Y, Maekawa M, Takasaki S, Sato T, Shimada S, Kawamorita N, Yamashita S, Mitsuzuka K, Mano N, Ito A. Predictive model for recurrence of renal cell carcinoma by comparing pre- and postoperative urinary metabolite concentrations. Cancer Sci 2021; 113:182-194. [PMID: 34710258 PMCID: PMC8748223 DOI: 10.1111/cas.15180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 11/29/2022] Open
Abstract
To improve treatment outcomes in real practice, useful biomarkers are desired when predicting postoperative recurrence for renal cell carcinoma (RCC). We collected data from patients who underwent definitive surgery for RCC and for benign urological tumor at our department between November 2016 and December 2019. We evaluated the differences in pre‐ and postoperative urinary metabolites with our precise quantitative method and identified predictive factors for RCC recurrence. Additionally, to clarify the significance of metabolites, we measured the intracellular metabolite concentration of three RCC cell lines. Among the 56 patients with RCC, nine had a recurrence (16.0%). When comparing 27 patients with T1a RCC and 10 with benign tumor, a significant difference was observed between pre‐ and postoperative concentrations among 10 urinary metabolites. In these 10 metabolites, multiple logistic regression analysis identified five metabolites (lactic acid, glycine, 2‐hydroxyglutarate, succinic acid, and kynurenic acid) as factors to build our recurrence prediction model. The values of area under the receiver operating characteristic curve, sensitivity, and specificity in this predictive model were 0.894%, 88.9%, and 88.0%, respectively. When stratified into low and high risk groups of recurrence based on this model, we found a significant drop of recurrence‐free survival rates among the high risk group. In in vitro studies, intracellular metabolite concentrations of metastatic tumor cell lines were much higher than those of primary tumor cell lines. By using our quantitative evaluation of urinary metabolites, we could predict postoperative recurrence with high sensitivity and specificity. Urinary metabolites could be noninvasive biomarkers to improve patient outcome.
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Affiliation(s)
- Kento Morozumi
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Yoshihide Kawasaki
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Masamitsu Maekawa
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - Shinya Takasaki
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - Tomonori Sato
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Shuichi Shimada
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Naoki Kawamorita
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Shinichi Yamashita
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Koji Mitsuzuka
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Nariyasu Mano
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - Akihiro Ito
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
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16
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Guida F, Tan VY, Corbin LJ, Smith-Byrne K, Alcala K, Langenberg C, Stewart ID, Butterworth AS, Surendran P, Achaintre D, Adamski J, Amiano P, Bergmann MM, Bull CJ, Dahm CC, Gicquiau A, Giles GG, Gunter MJ, Haller T, Langhammer A, Larose TL, Ljungberg B, Metspalu A, Milne RL, Muller DC, Nøst TH, Pettersen Sørgjerd E, Prehn C, Riboli E, Rinaldi S, Rothwell JA, Scalbert A, Schmidt JA, Severi G, Sieri S, Vermeulen R, Vincent EE, Waldenberger M, Timpson NJ, Johansson M. The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium. PLoS Med 2021; 18:e1003786. [PMID: 34543281 PMCID: PMC8496779 DOI: 10.1371/journal.pmed.1003786] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 10/07/2021] [Accepted: 08/27/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). METHODS AND FINDINGS We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. CONCLUSIONS This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.
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Affiliation(s)
- Florence Guida
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Vanessa Y. Tan
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Laura J. Corbin
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Karl Smith-Byrne
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Karine Alcala
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Isobel D. Stewart
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Adam S. Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - David Achaintre
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Chair of Experimental Genetics, School of Life Science, Weihenstephan, Technische Universität München, Freising, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Pilar Amiano
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastián, Spain
- Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | | | - Caroline J. Bull
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
- Bristol Renal, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | | | - Audrey Gicquiau
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Graham G. Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
| | - Marc J. Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Toomas Haller
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Arnulf Langhammer
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Tricia L. Larose
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Community Medicine and Global Health, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Börje Ljungberg
- Department of Surgical and Perioperative Sciences, Urology and Andrology, Umeå University, Umeå, Sweden
| | | | - Roger L. Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Australia
| | - David C. Muller
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Therese H. Nøst
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway
| | - Elin Pettersen Sørgjerd
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Cornelia Prehn
- Metabolomics and Proteomics Core (MPC), Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Joseph A. Rothwell
- Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Équipe “Exposome et Hérédité”, CESP UMR1018, Inserm, Villejuif, France
| | - Augustin Scalbert
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Julie A. Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Gianluca Severi
- Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Équipe “Exposome et Hérédité”, CESP UMR1018, Inserm, Villejuif, France
- Department of Statistics, Computer Science and Applications (DISIA), University of Florence, Florence, Italy
| | - Sabina Sieri
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Emma E. Vincent
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
- Bristol Renal, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Nicholas J. Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
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17
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Arendowski A, Ossoliński K, Ossolińska A, Ossoliński T, Nizioł J, Ruman T. Serum and urine analysis with gold nanoparticle-assisted laser desorption/ionization mass spectrometry for renal cell carcinoma metabolic biomarkers discovery. Adv Med Sci 2021; 66:326-335. [PMID: 34273747 DOI: 10.1016/j.advms.2021.07.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 06/02/2021] [Accepted: 07/06/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Renal cell carcinoma (RCC) is a very aggressive and often fatal heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes. MATERIALS/METHODS Serum and urine samples were collected from patients with diagnosed kidney cancer and assessed with gold nanoparticle enhanced target (AuNPET) surface assisted-laser desorption/ionization mass spectrometry (SALDI MS) based metabolomics and statistical analysis. RESULTS A database search allowed providing assignment of signals for the most promising features with a satisfactory value of the area under the curve and accuracy. Four potential biomarkers were found in urine and serum samples to distinguish clear cell renal cell carcinoma (ccRCC) from controls, 4 for the ccRCC with and without metastases, and 6 metabolites to distinguish low and high stages or grades. CONCLUSIONS This pilot study suggests that serum and urine metabolomics based on AuNPET-LDI MS may be useful in distinguishing types, grades and stages of human RCC.
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18
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Liu G, Lin CJ, Yates CR, Prasad GL. Metabolomic Analysis Identified Reduced Levels of Xenobiotics, Oxidative Stress, and Improved Vitamin Metabolism in Smokers Switched to Vuse Electronic Nicotine Delivery System. Nicotine Tob Res 2021; 23:1133-1142. [PMID: 33165576 PMCID: PMC8274285 DOI: 10.1093/ntr/ntaa225] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 11/02/2020] [Indexed: 12/01/2022]
Abstract
Introduction Switching to noncombustible tobacco products presents an opportunity for
cigarette smokers to potentially reduce the health risks associated with
smoking. Electronic Nicotine Delivery Systems (ENDS) are one such product
because the vapor produced from ENDS contains far fewer toxicants than
cigarette smoke. To investigate the biochemical effects of switching from
smoking to an ENDS, we assessed global metabolomic profiles of smokers in a
7-day confinement clinical study. Methods In the first 2 days of this clinical study, the subjects used their usual
brand of cigarettes and then switched to exclusive ENDS ad libitum use for 5
days. Urine and plasma samples were collected at baseline and 5 days after
switching. The samples were analyzed using a mass spectrometry-based
metabolomic platform. Results Random forest analyses of urine and plasma metabolomic data revealed
excellent predictive accuracy (>97%) of a 30-metabolite signature that
can differentiate smokers from 5-day ENDS switchers. In these signatures,
most biomarkers are nicotine-derived metabolites or xenobiotics. They were
significantly reduced in urine and plasma, suggesting a decreased xenobiotic
load on subjects. Our results also show significantly decreased levels of
plasma glutathione metabolites after switching, which suggests reduced
levels of oxidative stress. In addition, increased urinary and plasma levels
of vitamins and antioxidants were identified, suggesting enhanced
bioavailability due to discontinuation of cigarette smoking and switching to
Vuse ENDS use. Conclusions Our results suggest reduced toxicant exposure, reduced oxidative stress, and
potential beneficial changes in vitamin metabolism within 5 days in smokers
switching to Vuse ENDS. Implications Switching from smoking to exclusive ENDS use in clinical confinement settings
results in significant reduction of nicotine metabolites and other
cigarette-related xenobiotics in urine and plasma of subjects. Significantly
decreased oxidative stress-related metabolites and increased urinary and
plasma levels of vitamin metabolites and antioxidants in 5-day short-term
ENDS switchers suggest less toxic physiological environment for consumers of
ENDS products and potential health benefits if such changes persist.
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Affiliation(s)
- Gang Liu
- RAI Services Company, Winston-Salem, NC
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19
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Nabi MM, Mamun MA, Islam A, Hasan MM, Waliullah ASM, Tamannaa Z, Sato T, Kahyo T, Setou M. Mass spectrometry in the lipid study of cancer. Expert Rev Proteomics 2021; 18:201-219. [PMID: 33793353 DOI: 10.1080/14789450.2021.1912602] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Cancer is a heterogeneous disease that exploits various metabolic pathways to meet the demand for increased energy and structural components. Lipids are biomolecules that play essential roles as high energy sources, mediators, and structural components of biological membranes. Accumulating evidence has established that altered lipid metabolism is a hallmark of cancer.Areas covered: Mass spectrometry (MS) is a label-free analytical tool that can simultaneously identify and quantify hundreds of analytes. To date, comprehensive lipid studies exclusively rely on this technique. Here, we reviewed the use of MS in the study of lipids in various cancers and discuss its instrumental limitations and challenges.Expert opinion: MS and MS imaging have significantly contributed to revealing altered lipid metabolism in a variety of cancers. Currently, a single MS approach cannot profile the entire lipidome because of its lack of sensitivity and specificity for all lipid classes. For the metabolic pathway investigation, lipid study requires the integration of MS with other molecular approaches. Future developments regarding the high spatial resolution, mass resolution, and sensitivity of MS instruments are warranted.
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Affiliation(s)
- Md Mahamodun Nabi
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.,Institute of Food and Radiation Biology, Atomic Energy Research Establishment, Ganakbari, Savar, Dhaka, Bangladesh
| | - Md Al Mamun
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Ariful Islam
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Md Mahmudul Hasan
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - A S M Waliullah
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Zinat Tamannaa
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Tomohito Sato
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Tomoaki Kahyo
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.,International Mass Imaging Center, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Mitsutoshi Setou
- Department of Cellular & Molecular Anatomy, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.,International Mass Imaging Center, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan.,Department of Systems Molecular Anatomy, Institute for Medical Photonics Research, Preeminent Medical Photonics Education & Research Center, Hamamatsu, Shizuoka, Japan
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20
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De Matteis S, Bonafè M, Giudetti AM. Urinary Metabolic Biomarkers in Cancer Patients: An Overview. Methods Mol Biol 2021; 2292:203-212. [PMID: 33651364 DOI: 10.1007/978-1-0716-1354-2_18] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
The pathogenesis of cancer involves multiple molecular alterations at the level of genome, epigenome, and stromal environment, resulting in several deregulated signal transduction pathways. Metabolites are not only end products of gene and protein expression but also a consequence of the mutual relationship between the genome and the internal environment. Considering that metabolites serve as a comprehensive chemical fingerprint of cell metabolism, metabolomics is emerging as the method able to discover metabolite biomarkers that can be developed for early cancer detection, prognosis, and response to treatment. Urine represents a noninvasive source, available and rich in metabolites, useful for cancer diagnosis, prognosis, and treatment monitoring. In this chapter, we reported the main published evidences on urinary metabolic biomarkers in the studied cancers related to hepatopancreatic and urinary tract with the aim at discussing their promising role in clinical practice.
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Affiliation(s)
- Serena De Matteis
- Department of Medicine, Section of Oncology, University of Verona, Verona, Italy.
| | - Massimiliano Bonafè
- Department of Experimental, Diagnostic and Specialty Medicine, AlmaMater Studiorum, University of Bologna, Bologna, Italy
| | - Anna Maria Giudetti
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
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21
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Kozar N, Kruusmaa K, Dovnik A, Bitenc M, Argamasilla R, Adsuar A, Goswami N, Takač I, Arko D. Identification of novel diagnostic biomarkers in endometrial cancer using targeted metabolomic profiling. Adv Med Sci 2021; 66:46-51. [PMID: 33360772 DOI: 10.1016/j.advms.2020.12.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/07/2020] [Accepted: 12/10/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE Endometrial cancer (EC) is the most common gynecological malignancy with high disease burden especially in advanced stages of the disease. Our study investigated the metabolomic profile of EC patient's serum with the aim of identifying novel diagnostic biomarkers that could be used especially in early disease detection. MATERIAL AND METHODS Using targeted metabolomic serum profiling based on HPLC-TQ/MS, women with EC (n = 15) and controls (n = 21) were examined for 232 endogenous metabolites. RESULTS Top performing biomarkers included ceramides, acylcarnitines and 1-methyl adenosine. Top 4 biomarkers combined achieved 94% sensitivity with 75% specificity with AUC 92.5% (CI 90.5-94.5%). Individual markers also provided significant predictive values: C16-ceramide achieved sensitivity 73%, specificity 81%, AUC 0.83, C22-ceramide sensitivity 67%, specificity 81%, AUC 0.77, hydroxyhexadecenoylcarnitine sensitivity 60%, specificity 96%, AUC 0.76 and 1-methyladenosine sensitivity 67%, specificity 81%, AUC 0.75. The individual markers, however, did not reach the high sensitivity and specificity of the 4-biomarker combination. CONCLUSIONS Using mass spectrometry targeted metabolomic profiling, ceramides, acylcarnitines and 1-methyladenosine were identified as potential diagnostic biomarkers for EC. Additionally, these identified metabolites may provide additional insight into cancer cell metabolism.
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Affiliation(s)
- Nejc Kozar
- Division of Gynaecology and Perinatology, University Medical Centre Maribor, Maribor, Slovenia; Faculty of Medicine, University of Maribor, Maribor, Slovenia.
| | - Kristi Kruusmaa
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia; Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Seville, Spain
| | - Andraž Dovnik
- Division of Gynaecology and Perinatology, University Medical Centre Maribor, Maribor, Slovenia; Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Marko Bitenc
- Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Seville, Spain
| | - Rosa Argamasilla
- Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Seville, Spain
| | - Antonio Adsuar
- Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Seville, Spain
| | - Nandu Goswami
- Physiology Division, Otto Loewi Research Center for Vascular Biology, Immunology and Inflammation, Medical University of Graz, Graz, Austria
| | - Iztok Takač
- Division of Gynaecology and Perinatology, University Medical Centre Maribor, Maribor, Slovenia; Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Darja Arko
- Division of Gynaecology and Perinatology, University Medical Centre Maribor, Maribor, Slovenia; Faculty of Medicine, University of Maribor, Maribor, Slovenia
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22
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Arendowski A, Ossolinski K, Niziol J, Ruman T. Screening of Urinary Renal Cancer Metabolic Biomarkers with Gold Nanoparticles-assisted Laser Desorption/Ionization Mass Spectrometry. ANAL SCI 2020; 36:1521-1525. [PMID: 32830161 DOI: 10.2116/analsci.20p226] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 08/13/2020] [Indexed: 08/09/2023]
Abstract
Renal cell carcinoma is a very aggressive and often fatal disease for which there are no specific biomarkers found to date. The purpose of this work was to find features that differentiate urine metabolic profiles of healthy people and cancer patients. Laser desorption/ionization mass spectrometry on gold nanostructures-based techniques were used for the metabolic analysis of urine of 50 patients with kidney cancer. Comparison with data from 50 healthy volunteers led to the discovery of several compounds that may be considered potential renal cell carcinoma (RCC) biomarkers. Statistical analysis of data allowed for the discovery of m/z values that had the greatest impact on group differentiation. A database search enabled the assignment of signals for the most promising 15 features among them: serine, heptanol, 3-methylene-indolenine, 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, phosphodimethylethanolamine, 4-methoxyphenylacetic acid, N-acetylglutamine, 3,5-dihydroxyphenylvaleric acid, hydroxyhexanoylglycine, valyl-leucine, leucyl-histidine, oleamide, 9,12,13-trihydroxyoctadecenoic acid, stearidonyl carnitine and squalene. Differences of metabolite profiles of human urine could be identified by gold nanoparticle-enhanced target (AuNPET) LDI MS method and used for the detection of renal cancer.
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Affiliation(s)
- Adrian Arendowski
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave, 35-959, Rzeszów, Poland
| | - Krzysztof Ossolinski
- Department of Urology, John Paul II District Hospital, Grunwaldzka 4 St, 36-100, Kolbuszowa, Poland
| | - Joanna Niziol
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave, 35-959, Rzeszów, Poland
| | - Tomasz Ruman
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave, 35-959, Rzeszów, Poland
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23
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Manzi M, Palazzo M, Knott ME, Beauseroy P, Yankilevich P, Giménez MI, Monge ME. Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma. J Proteome Res 2020; 20:841-857. [PMID: 33207877 DOI: 10.1021/acs.jproteome.0c00663] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.
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Affiliation(s)
- Malena Manzi
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD CABA, Argentina.,Departamento de Química Biológica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires. Junín 956, C1113AAD Buenos Aires, Argentina
| | - Martín Palazzo
- LM2S, Université de Technologie de Troyes, 12 rue Marie-Curie, CS42060 Troyes, France.,Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA), CONICET, Instituto Partner de la Sociedad Max Planck, Godoy Cruz 2390, C1425FQD CABA, Argentina
| | - María Elena Knott
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD CABA, Argentina
| | - Pierre Beauseroy
- LM2S, Université de Technologie de Troyes, 12 rue Marie-Curie, CS42060 Troyes, France
| | - Patricio Yankilevich
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA), CONICET, Instituto Partner de la Sociedad Max Planck, Godoy Cruz 2390, C1425FQD CABA, Argentina
| | - María Isabel Giménez
- Departamento de Diagnóstico y Tratamiento, Hospital Italiano de Buenos Aires, Tte. Gral. Juan Domingo Perón 4190, C1199ABB CABA, Argentina
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD CABA, Argentina
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24
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Nizioł J, Ossoliński K, Tripet BP, Copié V, Arendowski A, Ruman T. Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based metabolome profiling of urine samples from kidney cancer patients. J Pharm Biomed Anal 2020; 193:113752. [PMID: 33197834 DOI: 10.1016/j.jpba.2020.113752] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/25/2020] [Accepted: 11/01/2020] [Indexed: 12/13/2022]
Abstract
Kidney cancer is one of the most frequently diagnosed cancers of the urinary tract in the world. Despite significant advances in kidney cancer treatment, no urine specific biomarker is currently used to guide therapeutic interventions. In an effort to address this knowledge gap, metabolic profiling of urine samples from 50 patients with kidney cancer and 50 healthy volunteers was undertaken using high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR) and silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry (109AgNPET LDI MS). Twelve potential urine biomarkers of kidney cancer were identified and quantified using one-dimensional (1D) 1H NMR metabolomics. Seven mass spectral features which differed significantly in abundance (p < 0.05) between kidney cancer patients and healthy volunteers were also detected using 109AgNPET-based laser desorption/ionization mass spectrometry (LDI MS). This work provides a framework to expand biomarker discovery that could be used as useful diagnostic or prognostic of kidney cancer progression.
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Affiliation(s)
- Joanna Nizioł
- Rzeszów University of Technology, Faculty of Chemistry, 6 Powstańców Warszawy Ave., 35-959 Rzeszów, Poland.
| | - Krzysztof Ossoliński
- Department of Urology, John Paul II Hospital, Grunwaldzka 4 St., 36-100 Kolbuszowa, Poland
| | - Brian P Tripet
- The Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana 59717, United States
| | - Valérie Copié
- The Department of Chemistry and Biochemistry, Montana State University, Bozeman, Montana 59717, United States
| | - Adrian Arendowski
- Rzeszów University of Technology, Faculty of Chemistry, 6 Powstańców Warszawy Ave., 35-959 Rzeszów, Poland
| | - Tomasz Ruman
- Rzeszów University of Technology, Faculty of Chemistry, 6 Powstańców Warszawy Ave., 35-959 Rzeszów, Poland
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25
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Martinefski MR, Elguero B, Knott ME, Gonilski D, Tedesco L, Gurevich Messina JM, Pollak C, Arzt E, Monge ME. Mass Spectrometry-Based Metabolic Fingerprinting Contributes to Unveil the Role of RSUME in Renal Cell Carcinoma Cell Metabolism. J Proteome Res 2020; 20:786-803. [PMID: 33124415 DOI: 10.1021/acs.jproteome.0c00655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Clear cell renal cell carcinoma (ccRCC) is a heterogeneous disease with 50-80% patients exhibiting mutations in the von Hippel-Lindau (VHL) gene. RSUME (RWD domain (termed after three major RWD-containing proteins: RING finger-containing proteins, WD-repeat-containing proteins, and yeast DEAD (DEXD)-like helicases)-containing protein small ubiquitin-related modifier (SUMO) enhancer) acts as a negative regulator of VHL function in normoxia. A discovery-based metabolomics approach was developed by means of ultraperformance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (MS) for fingerprinting the endometabolome of a human ccRCC cell line 786-O and three other transformed cell systems (n = 102) with different expressions of RSUME and VHL. Cross-validated orthogonal projection to latent structures discriminant analysis models were built on positive, negative, and a combination of positive- and negative-ion mode MS data sets. Discriminant feature panels selected by an iterative multivariate classification allowed differentiating cells with different expressions of RSUME and VHL. Fifteen identified discriminant metabolites with level 1, including glutathione, butyrylcarnitine, and acetylcarnitine, contributed to understand the role of RSUME in ccRCC. Altered pathways associated with the RSUME expression were validated by biological and bioinformatics analyses. Combined results showed that in the absence of VHL, RSUME is involved in the downregulation of the antioxidant defense system, whereas in the presence of VHL, it acts in rerouting energy-related pathways, negatively modulating the lipid utilization, and positively modulating the fatty acid synthesis, which may promote deposition in droplets.
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Affiliation(s)
- Manuela R Martinefski
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina.,Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, C1113AAD Ciudad de Buenos Aires, Argentina
| | - Belén Elguero
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA-CONICET), Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina
| | - María Elena Knott
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina
| | - David Gonilski
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA-CONICET), Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina
| | - Lucas Tedesco
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA-CONICET), Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina
| | - Juan M Gurevich Messina
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina
| | - Cora Pollak
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA-CONICET), Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina
| | - Eduardo Arzt
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA-CONICET), Partner Institute of the Max Planck Society, Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina.,Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, C1428EGA Ciudad de Buenos Aires, Argentina
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD Ciudad de Buenos Aires, Argentina
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26
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Chen YY, Hu HH, Wang YN, Liu JR, Liu HJ, Liu JL, Zhao YY. Metabolomics in renal cell carcinoma: From biomarker identification to pathomechanism insights. Arch Biochem Biophys 2020; 695:108623. [PMID: 33039388 DOI: 10.1016/j.abb.2020.108623] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 09/14/2020] [Accepted: 10/04/2020] [Indexed: 12/27/2022]
Abstract
Renal cell carcinoma (RCC) is a frequently diagnosed cancer with high prevalence, which is inversely associated with survival benefit. Although myriad studies have shed light on disease causality, unfortunately, thus far, RCC diagnosis is faced with numerous obstacles partly due to the insufficient knowledge of effective biomarkers, hinting deeper mechanistic understanding are urgently needed. Metabolites are recognized as final proxies for gene-environment interactions and physiological homeostasis as they reflect dynamic processes that are ongoing or have been taken place, and metabolomics may therefore offer a far more productive and cost-effective route to disease discovery, particularly within the arena for new biomarker identification. In this review, we primarily expatiate recent advances in metabolomics that may be amenable to novel biomarkers or therapeutic targets for RCC, which may expand our armaments to win more bettles against RCC.
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Affiliation(s)
- Yuan-Yuan Chen
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China
| | - He-He Hu
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China
| | - Yan-Ni Wang
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China
| | - Jing-Ru Liu
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China
| | - Hai-Jing Liu
- Shaanxi Institute for Food and Drug Control, Xi'an, Shaanxi, 710065, China.
| | - Jian-Ling Liu
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China.
| | - Ying-Yong Zhao
- Faculty of Life Science & Medicine, Northwest University, No. 229 Taibai North Road, Xi'an, Shaanxi, 710069, China.
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27
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Hu L, Liu J, Zhang W, Wang T, Zhang N, Lee YH, Lu H. FUNCTIONAL METABOLOMICS DECIPHER BIOCHEMICAL FUNCTIONS AND ASSOCIATED MECHANISMS UNDERLIE SMALL-MOLECULE METABOLISM. MASS SPECTROMETRY REVIEWS 2020; 39:417-433. [PMID: 31682024 DOI: 10.1002/mas.21611] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 10/08/2019] [Accepted: 10/10/2019] [Indexed: 06/10/2023]
Abstract
Metabolism is the collection of biochemical reactions enabled by chemically diverse metabolites, which facilitate different physiological processes to exchange substances and synthesize energy in diverse living organisms. Metabolomics has emerged as a cutting-edge method to qualify and quantify the metabolites in different biological matrixes, and it has the extraordinary capacity to interrogate the biological significance that underlies metabolic modification and modulation. Liquid chromatography combined with mass spectrometry (LC/MS), as a robust platform for metabolomics analysis, has increased in popularity over the past 10 years due to its excellent sensitivity, throughput, and versatility. However, metabolomics investigation currently provides us with only phenotype data without revealing the biochemical functions and associated mechanisms. This limitation indeed weakens the core value of metabolomics data in a broad spectrum of the life sciences. In recent years, the scientific community has actively explored the functional features of metabolomics and translated this cutting-edge approach to be used to solve key multifaceted questions, such as disease pathogenesis, the therapeutic discovery of drugs, nutritional issues, agricultural problems, environmental toxicology, and microbial evolution. Here, we are the first to briefly review the history and applicable progression of LC/MS-based metabolomics, with an emphasis on the applications of metabolic phenotyping. Furthermore, we specifically highlight the next era of LC/MS-based metabolomics to target functional metabolomes, through which we can answer phenotype-related questions to elucidate biochemical functions and associated mechanisms implicated in dysregulated metabolism. Finally, we propose many strategies to enhance the research capacity of functional metabolomics by enabling the combination of contemporary omics technologies and cutting-edge biochemical techniques. The main purpose of this review is to improve the understanding of LC/MS-based metabolomics, extending beyond the conventional metabolic phenotype toward biochemical functions and associated mechanisms, to enhance research capability and to enlarge the applicable scope of functional metabolomics in small-molecule metabolism in different living organisms.
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Affiliation(s)
- Longlong Hu
- Laboratory for Functional Metabolomics Science, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jingjing Liu
- Laboratory for Functional Metabolomics Science, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wenhua Zhang
- Laboratory for Functional Metabolomics Science, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
- Department of Pharmacognosy, College of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Tianyu Wang
- Laboratory for Functional Metabolomics Science, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Ning Zhang
- Department of Pharmacognosy, College of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
- Department of Pharmaceutical Analysis, College of Jiamusi, Heilongjiang University of Chinese Medicine, Harbin, 121000, China
| | - Yie Hou Lee
- Translational 'Omics and Biomarkers Group, KK Research Centre, KK Women's and Children's Hospital, Singapore, 229899, Singapore
- OBGYN-Academic Clinical Program, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Haitao Lu
- Laboratory for Functional Metabolomics Science, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
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Nizioł J, Ossoliński K, Tripet BP, Copié V, Arendowski A, Ruman T. Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based serum metabolomics of kidney cancer. Anal Bioanal Chem 2020; 412:5827-5841. [PMID: 32661677 PMCID: PMC7413895 DOI: 10.1007/s00216-020-02807-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 06/19/2020] [Accepted: 07/06/2020] [Indexed: 12/24/2022]
Abstract
Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite all the efforts made, no serum-specific biomarker is currently used in the clinical management of patients with this tumor. In this study, comprehensive high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR) and silver-109 nanoparticle-enhanced steel target laser desorption/ionization mass spectrometry (109AgNPET LDI MS) approaches were conducted, in conjunction with multivariate data analysis, to discriminate the global serum metabolic profiles of kidney cancer (n = 50) and healthy volunteers (n = 49). Eight potential biomarkers have been identified using 1H NMR metabolomics and nine mass spectral features which differed significantly (p < 0.05) between kidney cancer patients and healthy volunteers, as observed by LDI MS. A partial least squares discriminant analysis (OPLS-DA) model generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from cancerous samples (Q2 > 0.7), area under the receiver operative characteristic curve (ROC) AUC > 0.96. Compared with healthy human serum, kidney cancer serum had higher levels of glucose and lower levels of choline, glycerol, glycine, lactate, leucine, myo-inositol, and 1-methylhistidine. Analysis of differences between these metabolite levels in patients with different types and grades of kidney cancer was undertaken. Our results, derived from the combination of LDI MS and 1H NMR methods, suggest that serum biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer. Graphical abstract.
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Affiliation(s)
- Joanna Nizioł
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave, 35-959, Rzeszów, Poland.
| | - Krzysztof Ossoliński
- Department of Urology, John Paul II Hospital, Grunwaldzka 4 St, 36-100, Kolbuszowa, Poland
| | - Brian P Tripet
- The Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, 59717, USA
| | - Valérie Copié
- The Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, 59717, USA
| | - Adrian Arendowski
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave, 35-959, Rzeszów, Poland
| | - Tomasz Ruman
- Faculty of Chemistry, Rzeszów University of Technology, 6 Powstańców Warszawy Ave, 35-959, Rzeszów, Poland
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29
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Yusof HM, Ab-Rahim S, Wan Ngah WZ, Nathan S, A Jamal AR, Mazlan M. Metabolomic characterization of colorectal cancer cell lines highlighting stage-specific alterations during cancer progression. BIOIMPACTS : BI 2020; 11:147-156. [PMID: 33842285 PMCID: PMC8022234 DOI: 10.34172/bi.2021.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 12/12/2019] [Accepted: 02/04/2020] [Indexed: 12/24/2022]
Abstract
Introduction: Metabolomic studies on various colorectal cancer (CRC) cell lines have improved our understanding of the biochemical events underlying the disease. However, the metabolic profile dynamics associated with different stages of CRC progression is still lacking. Such information can provide further insights into the pathophysiology and progression of the disease that will prove useful in identifying specific targets for drug designing and therapeutics. Thus, our study aims to characterize the metabolite profiles in the established cell lines corresponding to different stages of CRC. Methods: Metabolite profiling of normal colon cell lines (CCD 841 CoN) and CRC cell lines corresponding to different stages, i.e., SW 1116 (stage A), HT 29 and SW 480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D), was carried out using liquid chromatography-mass spectrometry (LC-MS). Mass Profiler Professional and Metaboanalyst 4.0 software were used for statistical and pathway analysis. METLIN database was used for the identification of metabolites. Results: We identified 72 differential metabolites compared between CRC cell lines of all the stages and normal colon cells. Principle component analysis and partial least squares discriminant analysis score plot were used to segregate normal and CRC cells, as well as CRC cells in different stages of the disease. Variable importance in projection score identified unique differential metabolites in CRC cells of the different stages. We identified 7 differential metabolites unique to stage A, 3 in stage B, 5 in stage C, and 5 in stage D. Conclusion: This study highlights the differential metabolite profiling in CRC cell lines corresponding to different stages. The identification of the differential metabolites in CRC cells at individual stages will lead to a better understanding of the pathophysiology of CRC development and progression and, hence, its application in treatment strategies.
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Affiliation(s)
- Hazwani Mohd Yusof
- Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Universiti Teknologi MARA, Campus Sungai Buloh, 47000 Sungai Buloh, Selangor, Malaysia
| | - Sharaniza Ab-Rahim
- Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Universiti Teknologi MARA, Campus Sungai Buloh, 47000 Sungai Buloh, Selangor, Malaysia
| | - Wan Zurinah Wan Ngah
- Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, Bandar Tun Razak, 56000 Batu 9 Cheras, Wilayah Persekutuan Kuala Lumpur, Malaysia
| | - Sheila Nathan
- Department of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - A Rahman A Jamal
- UKM Medical Molecular Biology Institute, UKM Medical Centre, Jalan Yaacob Latif, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Malaysia
| | - Musalmah Mazlan
- Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Universiti Teknologi MARA, Campus Sungai Buloh, 47000 Sungai Buloh, Selangor, Malaysia
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30
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Fujimoto K, Kishino H, Hashimoto K, Watanabe K, Yamoto T, Mori K. Biochemical profiles of rat primary cultured hepatocytes following treatment with rotenone, FCCP, or (+)-usnic acid. J Toxicol Sci 2020; 45:339-347. [PMID: 32493876 DOI: 10.2131/jts.45.339] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
The metabolomic profiles of rat primary hepatocytes following treatment with rotenone, FCCP, or (+)-usnic acid were determined using liquid chromatography-mass spectrometry/mass spectrometry and gas chromatography-mass spectrometry. Significant and similar changes in the levels of 283 biochemical metabolites were associated with the three treatments compared with solvent control samples. Overall, the three treatments generated similar global biochemical profiles, with some minor differences associated with rotenone treatment. All three treatments resulted in a shift in energy metabolism as demonstrated by decreased glycogen stores and glycolysis. A reduced antioxidant response was detected in cells following all treatments. In addition, bile acid biosynthesis decreased as a potential consequence of increased oxidative stress by all three treatments. Conversely, rotenone treatment induced a number of changes after 1 hr, which were not detected in FCCP- or (+)-usnic acid-treated samples; these changes were not sustained over time and included increased NAD+ salvage and lysine degradation. In conclusion, these biochemical profiles could provide new insights into the mechanism(s) of mitochondrial toxicity.
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Affiliation(s)
| | | | | | - Kyoko Watanabe
- Drug Metabolism and Pharmacokinetics Research Laboratories, Daiichi Sankyo Co., Ltd
| | - Takashi Yamoto
- Product Information Management Department, Daiichi Sankyo Co., Ltd
| | - Kazuhiko Mori
- Medicinal Safety Research Laboratories, Daiichi Sankyo Co., Ltd
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31
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Sun C, Wang F, Zhang Y, Yu J, Wang X. Mass spectrometry imaging-based metabolomics to visualize the spatially resolved reprogramming of carnitine metabolism in breast cancer. Theranostics 2020; 10:7070-7082. [PMID: 32641979 PMCID: PMC7330837 DOI: 10.7150/thno.45543] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 05/19/2020] [Indexed: 01/08/2023] Open
Abstract
New insights into tumor-associated metabolic reprogramming have provided novel vulnerabilities that can be targeted for cancer therapy. Here, we propose a mass spectrometry imaging (MSI)-based metabolomic strategy to visualize the spatially resolved reprogramming of carnitine metabolism in heterogeneous breast cancer. Methods: A wide carnitine coverage MSI method was developed to investigate the spatial alternations of carnitines in cancer tissues of xenograft mouse models and human samples. Spatial expression of key metabolic enzymes that are closely associated with the altered carnitines was examined in adjacent cancer tissue sections. Results: A total of 17 carnitines, including L-carnitine, 6 short-chain acylcarnitines, 3 middle-chain acylcarnitines, and 7 long-chain acylcarnitines were imaged. L-carnitine and short-chain acylcarnitines are significantly reprogrammed in breast cancer. A classification model based on the carnitine profiles of 170 cancer samples and 128 normal samples enables an accurate identification of breast cancer. CPT 1A, CPT 2, and CRAT, which are extensively involved in carnitine system-mediated fatty acid β-oxidation pathway were also found to be abnormally expressed in breast cancer. Remarkably, the expressions of CPT 2 and CRAT were found for the first time to be altered in breast cancer. Conclusion: These data not only expand our understanding of the complex tumor metabolic reprogramming, but also provide the first evidence that carnitine metabolism is reprogrammed at both the metabolite and enzyme levels in breast cancer.
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Affiliation(s)
- Chenglong Sun
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Fukai Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Yang Zhang
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Jinqian Yu
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Xiao Wang
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
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32
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Dudka I, Thysell E, Lundquist K, Antti H, Iglesias-Gato D, Flores-Morales A, Bergh A, Wikström P, Gröbner G. Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2-ERG fusion status. BMC Cancer 2020; 20:437. [PMID: 32423389 PMCID: PMC7236196 DOI: 10.1186/s12885-020-06908-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 04/27/2020] [Indexed: 12/31/2022] Open
Abstract
Background Prostate cancer (PC) can display very heterogeneous phenotypes ranging from indolent asymptomatic to aggressive lethal forms. Understanding how these PC subtypes vary in their striving for energy and anabolic molecules is of fundamental importance for developing more effective therapies and diagnostics. Here, we carried out an extensive analysis of prostate tissue samples to reveal metabolic alterations during PC development and disease progression and furthermore between TMPRSS2-ERG rearrangement-positive and -negative PC subclasses. Methods Comprehensive metabolomics analysis of prostate tissue samples was performed by non-destructive high-resolution magic angle spinning nuclear magnetic resonance (1H HR MAS NMR). Subsequently, samples underwent moderate extraction, leaving tissue morphology intact for histopathological characterization. Metabolites in tissue extracts were identified by 1H/31P NMR and liquid chromatography-mass spectrometry (LC-MS). These metabolomics profiles were analyzed by chemometric tools and the outcome was further validated using proteomic data from a separate sample cohort. Results The obtained metabolite patterns significantly differed between PC and benign tissue and between samples with high and low Gleason score (GS). Five key metabolites (phosphocholine, glutamate, hypoxanthine, arginine and α-glucose) were identified, who were sufficient to differentiate between cancer and benign tissue and between high to low GS. In ERG-positive PC, the analysis revealed several acylcarnitines among the increased metabolites together with decreased levels of proteins involved in β-oxidation; indicating decreased acyl-CoAs oxidation in ERG-positive tumors. The ERG-positive group also showed increased levels of metabolites and proteins involved in purine catabolism; a potential sign of increased DNA damage and oxidative stress. Conclusions Our comprehensive metabolomic analysis strongly indicates that ERG-positive PC and ERG-negative PC should be considered as different subtypes of PC; a fact requiring different, sub-type specific treatment strategies for affected patients.
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Affiliation(s)
- Ilona Dudka
- Department of Chemistry, Umeå University, Linnaeus väg 6, 901 87, Umeå, Sweden
| | - Elin Thysell
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Kristina Lundquist
- Department of Chemistry, Umeå University, Linnaeus väg 6, 901 87, Umeå, Sweden
| | - Henrik Antti
- Department of Chemistry, Umeå University, Linnaeus väg 6, 901 87, Umeå, Sweden
| | - Diego Iglesias-Gato
- IVS, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.,Novo Nordisk Foundation Centre for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.,Danish Cancer Society, Copenhagen, Denmark
| | - Amilcar Flores-Morales
- IVS, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.,Novo Nordisk Foundation Centre for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.,Danish Cancer Society, Copenhagen, Denmark
| | - Anders Bergh
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Pernilla Wikström
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
| | - Gerhard Gröbner
- Department of Chemistry, Umeå University, Linnaeus väg 6, 901 87, Umeå, Sweden.
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Liu X, Zhang M, Cheng X, Liu X, Sun H, Guo Z, Li J, Tang X, Wang Z, Sun W, Zhang Y, Ji Z. LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma. Front Oncol 2020; 10:717. [PMID: 32500026 PMCID: PMC7243740 DOI: 10.3389/fonc.2020.00717] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 04/16/2020] [Indexed: 12/17/2022] Open
Abstract
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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Affiliation(s)
- Xiang Liu
- Institute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Mingxin Zhang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiangming Cheng
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China
| | - Xiaoyan Liu
- Institute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Haidan Sun
- Institute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhengguang Guo
- Institute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Li
- Institute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaoyue Tang
- Institute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhan Wang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China
| | - Wei Sun
- Institute of Basic Medical Sciences, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yushi Zhang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China
| | - Zhigang Ji
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China
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Sato T, Kawasaki Y, Maekawa M, Takasaki S, Shimada S, Morozumi K, Sato M, Kawamorita N, Yamashita S, Mitsuzuka K, Mano N, Ito A. Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma. Cancer Sci 2020; 111:2570-2578. [PMID: 32350988 PMCID: PMC7385347 DOI: 10.1111/cas.14440] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/20/2020] [Accepted: 04/26/2020] [Indexed: 12/20/2022] Open
Abstract
Using surgically resected tissue, we identified characteristic metabolites related to the diagnosis and malignant status of clear cell renal cell carcinoma (ccRCC). Specifically, we quantified these metabolites in urine samples to evaluate their potential as clinically useful noninvasive biomarkers of ccRCC. Between January 2016 and August 2018, we collected urine samples from 87 patients who had pathologically diagnosed ccRCC and from 60 controls who were patients with benign urological conditions. Metabolite concentrations in urine samples were investigated using liquid chromatography‐mass spectrometry with an internal standard and adjustment based on urinary creatinine levels. We analyzed the association between metabolite concentration and predictability of diagnosis and of malignant status by multiple logistic regression and receiver operating characteristic (ROC) curves to establish ccRCC predictive models. Of the 47 metabolites identified in our previous study, we quantified 33 metabolites in the urine samples. Multiple logistic regression analysis revealed 5 metabolites (l‐glutamic acid, lactate, d‐sedoheptulose 7‐phosphate, 2‐hydroxyglutarate, and myoinositol) for a diagnostic predictive model and 4 metabolites (l‐kynurenine, l‐glutamine, fructose 6‐phosphate, and butyrylcarnitine) for a predictive model for clinical stage III/IV. The sensitivity and specificity of the diagnostic predictive model were 93.1% and 95.0%, respectively, yielding an area under the ROC curve (AUC) of 0.966. The sensitivity and specificity of the predictive model for clinical stage were 88.5% and 75.4%, respectively, with an AUC of 0.837. In conclusion, quantitative analysis of urinary metabolites yielded predictive models for diagnosis and malignant status of ccRCC. Urinary metabolites have the potential to be clinically useful noninvasive biomarkers of ccRCC to improve patient outcomes.
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Affiliation(s)
- Tomonori Sato
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yoshihide Kawasaki
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Masamitsu Maekawa
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - Shinya Takasaki
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - Shuichi Shimada
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kento Morozumi
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Masahiko Sato
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Naoki Kawamorita
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shinichi Yamashita
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Koji Mitsuzuka
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Nariyasu Mano
- Department of Pharmaceutical Sciences, Tohoku University Hospital, Sendai, Japan
| | - Akihiro Ito
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
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35
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Urine metabolomic analysis in clear cell and papillary renal cell carcinoma: A pilot study. J Proteomics 2020; 218:103723. [DOI: 10.1016/j.jprot.2020.103723] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 02/24/2020] [Accepted: 02/25/2020] [Indexed: 12/21/2022]
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36
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Dinges SS, Hohm A, Vandergrift LA, Nowak J, Habbel P, Kaltashov IA, Cheng LL. Cancer metabolomic markers in urine: evidence, techniques and recommendations. Nat Rev Urol 2020; 16:339-362. [PMID: 31092915 DOI: 10.1038/s41585-019-0185-3] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Urinary tests have been used as noninvasive, cost-effective tools for screening, diagnosis and monitoring of diseases since ancient times. As we progress through the 21st century, modern analytical platforms have enabled effective measurement of metabolites, with promising results for both a deeper understanding of cancer pathophysiology and, ultimately, clinical translation. The first study to measure metabolomic urinary cancer biomarkers using NMR and mass spectrometry (MS) was published in 2006 and, since then, these techniques have been used to detect cancers of the urological system (kidney, prostate and bladder) and nonurological tumours including those of the breast, ovary, lung, liver, gastrointestinal tract, pancreas, bone and blood. This growing field warrants an assessment of the current status of research developments and recommendations to help systematize future research.
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Affiliation(s)
- Sarah S Dinges
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Haematology and Oncology, CCM, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Annika Hohm
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Diagnostic and Interventional Neuroradiology, University Hospital of Würzburg, Würzburg, Germany
| | - Lindsey A Vandergrift
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Johannes Nowak
- Department of Diagnostic and Interventional Radiology, University Hospital of Würzburg, Würzburg, Germany
| | - Piet Habbel
- Department of Haematology and Oncology, CCM, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Igor A Kaltashov
- Department of Chemistry, University of Massachusetts-Amherst, Amherst, MA, USA.
| | - Leo L Cheng
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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MicroRNAs and Neutrophil Activation Markers Predict Venous Thrombosis in Pancreatic Ductal Adenocarcinoma and Distal Extrahepatic Cholangiocarcinoma. Int J Mol Sci 2020; 21:ijms21030840. [PMID: 32012923 PMCID: PMC7043221 DOI: 10.3390/ijms21030840] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 01/20/2020] [Accepted: 01/23/2020] [Indexed: 12/24/2022] Open
Abstract
Cancer-associated venous thrombosis (VTE) increases mortality and morbidity. However, limited tools are available to identify high risk patients. Upon activation, neutrophils release their content through different mechanisms, thereby prompting thrombosis. We explored plasma microRNAs (miRNAs) and neutrophil activation markers to predict VTE in pancreatic ductal adenocarcinoma (PDAC) and distal extrahepatic cholangiocarcinoma (DECC). Twenty-six PDAC and 6 DECC patients recruited at cancer diagnosis, were examined for deep vein thrombosis and pulmonary embolisms, and were then followed-up with clinical examinations, blood collections, and biCUS. Ten patients developed VTE and were compared with 22 age- and sex-matched controls. miRNA expression levels were measured at diagnosis and right before VTE, and neutrophil activation markers (cell-free DNA, nucleosomes, calprotectin, and myeloperoxidase) were measured in every sample obtained during follow-up. We obtained a profile of 7 miRNAs able to estimate the risk of future VTE at diagnosis (AUC = 0.95; 95% Confidence Interval (CI) (0.987, 1)) with targets involved in the pancreatic cancer and complement and coagulation cascades pathways. Seven miRNAs were up- or down-regulated before VTE compared with diagnosis. We obtained a predictive model of VTE with calprotectin as predictor (AUC = 0.77; 95% CI (0.57, 0.95)). This is the first study that addresses the ability of plasma miRNAs and neutrophil activation markers to predict VTE in PDAC and DECC.
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Graça G, Lau CHE, Gonçalves LG. Exploring Cancer Metabolism: Applications of Metabolomics and Metabolic Phenotyping in Cancer Research and Diagnostics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1219:367-385. [PMID: 32130709 DOI: 10.1007/978-3-030-34025-4_19] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Altered metabolism is one of the key hallmarks of cancer. The development of sensitive, reproducible and robust bioanalytical tools such as Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry techniques offers numerous opportunities for cancer metabolism research, and provides additional and exciting avenues in cancer diagnosis, prognosis and for the development of more effective and personalized treatments. In this chapter, we introduce the current state of the art of metabolomics and metabolic phenotyping approaches in cancer research and clinical diagnostics.
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Affiliation(s)
- Gonçalo Graça
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
| | - Chung-Ho E Lau
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Luís G Gonçalves
- Proteomics of Non-Model Organisms Lab, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal.
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Gupta A, Nath K, Bansal N, Kumar M. Role of metabolomics-derived biomarkers to identify renal cell carcinoma: a comprehensive perspective of the past ten years and advancements. Expert Rev Mol Diagn 2019; 20:5-18. [DOI: 10.1080/14737159.2020.1704259] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Ashish Gupta
- Centre of Biomedical Research, SGPGIMS Campus, Lucknow, India
| | - Kavindra Nath
- Department of Radiology, University of Pennsylvania, Pheladelphia, PA, USA
| | - Navneeta Bansal
- Department of Urology, King George’s Medical University, Lucknow, India
| | - Manoj Kumar
- Department of Urology, King George’s Medical University, Lucknow, India
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Wang Z, Liu X, Liu X, Sun H, Guo Z, Zheng G, Zhang Y, Sun W. UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma. BMC Cancer 2019; 19:1195. [PMID: 31805976 PMCID: PMC6896793 DOI: 10.1186/s12885-019-6354-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 11/11/2019] [Indexed: 12/25/2022] Open
Abstract
Background To discover biomarker panels that could distinguish cancers (BC and RCC) from healthy controls (HCs) and bladder cancers (BC) from renal cell carcinoma (RCC), regardless of whether the patients have haematuria. In addition, we also explored the altered metabolomic pathways of BC and RCC. Methods In total, 403 participants were enrolled in our study, which included 146 BC patients (77 without haematuria and 69 with haematuria), 115 RCC patients (94 without haematuria and 21 with haematuria) and 142 sex- and age-matched HCs. Their midstream urine samples were collected and analysed by performing UPLC-MS. The statistical methods and pathway analyses were applied to discover potential biomarker panels and altered metabolic pathways. Results The panel of α-CEHC, β-cortolone, deoxyinosine, flunisolide, 11b,17a,21-trihydroxypreg-nenolone and glycerol tripropanoate could distinguish the patients with cancer from the HCs (the AUC was 0.950) and the external validation also displayed a good predictive ability (the AUC was 0.867). The panel of 4-ethoxymethylphenol, prostaglandin F2b, thromboxane B3, hydroxybutyrylcarnitine, 3-hydroxyphloretin and N′-formylkynurenine could differentiate BC from RCC without haematuria. The AUC was 0.829 in the discovering group and 0.76 in the external validation. The metabolite panel comprising 1-hydroxy-2-oxopropyl tetrahydropterin, 1-acetoxy-2-hydroxy-16-heptadecyn-4-one, 1,2-dehydrosalsolinol and L-tyrosine could significantly discriminate BC from RCC with haematuria (AUC was 0.913). Pathway analyses revealed altered lipid and purine metabolisms between cancer patients and HCs, together with disordered amino acid and purine metabolisms between BC and RCC with haematuria. Conclusions UPLC-MS urine metabolomic analyses could not only differentiate cancers from HCs but also discriminate BC from RCC. In addition, pathway analyses demonstrated a deeper metabolic mechanism of BC and RCC.
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Affiliation(s)
- Zhan Wang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Xiaoyan Liu
- Core facility of instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Xiang Liu
- Core facility of instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Haidan Sun
- Core facility of instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Zhengguang Guo
- Core facility of instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Guoyang Zheng
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Yushi Zhang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
| | - Wei Sun
- Core facility of instrument, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
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Alsaleh M, Sithithaworn P, Khuntikeo N, Loilome W, Yongvanit P, Chamadol N, Hughes T, O'Connor T, Andrews RH, Holmes E, Taylor-Robinson SD. Characterisation of the Urinary Metabolic Profile of Liver Fluke-Associated Cholangiocarcinoma. J Clin Exp Hepatol 2019; 9:657-675. [PMID: 31889746 PMCID: PMC6926188 DOI: 10.1016/j.jceh.2019.06.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 06/26/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Human infection with Opisthorchis viverrini, a carcinogenic liver fluke inhabiting the biliary tree, is endemic in Southeast Asia. Chronic infection is associated with a fatal complication, cholangiocarcinoma (CCA), a late-presenting and aggressive malignancy. Currently, annual mortality rates from CCA mirror trends in incidence, due in part to limited availability of efficient prognostic and early diagnostic biomarkers. With ability to detect thousands of urinary metabolites using metabonomics, the urine metabolome holds great potential in providing an insight into system-level alterations in carcinogenesis and in identifying metabolic markers altered in response to disturbed homoeostasis. METHODS Global molecular profiling using reversed-phase ultraperformance liquid chromatography mass spectrometry was utilised to acquire the urinary spectral profile of 137 Thai subjects (48 at high risk of infection, 41 with O. viverrini infection, 34 periportal fibrosis and 14 CCA) from Khon Kaen, Thailand. RESULTS Multivariate statistical analysis identified perturbation in several molecular classes related to purine metabolism and lipid metabolism in the CCA urine metabolome. These markers mainly reflect changes in energy metabolism to support proliferation (increased fatty acid oxidation and purine recycling), DNA methylation and hepatic injury. CONCLUSIONS Several metabolites of biological interest were discovered from this proof-of-principle dataset. Augmenting these findings is essential to accelerate the development of urinary metabolic markers in CCA.
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Key Words
- Opisthorchis viverrini
- acetaminophen, APAP
- bile duct cancer
- carnitine palmitoyltransferase 1, CPT1
- carnitine palmitoyltransferase 2, CPT2
- carnitine/acylcarnitine translocase, CACT
- cholangiocarcinoma screening and care program, CASCAP
- cholangiocarcinoma, CCA
- data-dependent acquisition, DDA
- electrospray ionisation, ESI
- hypoxanthine phosphoribosyltransferase 1, HPRT1
- hypoxanthine-guanine phosphoribosyltransferase, HPRT
- mass spectrometry
- metabonomics
- orthogonal projections to latent structures discriminant analysis, OPLS-DA
- periductal fibrosis, PDF
- periportal fibrosis, PPF
- primary biliary cholangitis, PBC
- primary sclerosing cholangitis, PSC
- principal component analysis, PCA
- reversed-phase ultra-performance liquid-chromatography mass spectrometry, RP-UPLC-MS
- ultra-performance liquid chromatography mass spectrometry, UPLC-MS
- variable importance in projection, VIP
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Affiliation(s)
- Munirah Alsaleh
- Division of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Paiboon Sithithaworn
- Cholangiocarcinoma Research Centre, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Narong Khuntikeo
- Cholangiocarcinoma Research Centre, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Watcharin Loilome
- Cholangiocarcinoma Research Centre, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Puangrat Yongvanit
- Cholangiocarcinoma Research Centre, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Nittaya Chamadol
- Cholangiocarcinoma Research Centre, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Thomas Hughes
- Division of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Thomas O'Connor
- Division of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Ross H. Andrews
- Division of Surgery and Cancer, Imperial College London, London, United Kingdom
- Cholangiocarcinoma Research Centre, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand
| | - Elaine Holmes
- Division of Surgery and Cancer, Imperial College London, London, United Kingdom
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Jing L, Guigonis JM, Borchiellini D, Durand M, Pourcher T, Ambrosetti D. LC-MS based metabolomic profiling for renal cell carcinoma histologic subtypes. Sci Rep 2019; 9:15635. [PMID: 31666664 PMCID: PMC6821699 DOI: 10.1038/s41598-019-52059-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 09/25/2019] [Indexed: 12/20/2022] Open
Abstract
Renal cell carcinomas (RCC) are classified according to their histological features. Accurate classification of RCC and comprehensive understanding of their metabolic dysregulation are of critical importance. Here we investigate the use of metabolomic analyses to classify the main RCC subtypes and to describe the metabolic variation for each subtype. To this end, we performed metabolomic profiling of 65 RCC frozen samples (40 clear cell, 14 papillary and 11 chromophobe) using liquid chromatography-mass spectrometry. OPLS-DA multivariate analysis based on metabolomic data showed clear discrimination of all three main subtypes of RCC (R2 = 75.0%, Q2 = 59.7%). The prognostic performance was evaluated using an independent cohort and showed an AUROC of 0.924, 0.991 and 1 for clear cell, papillary and chromophobe RCC, respectively. Further pathway analysis using the 21 top metabolites showed significant differences in amino acid and fatty acid metabolism between three RCC subtypes. In conclusion, this study shows that metabolomic profiling could serve as a tool that is complementary to histology for RCC subtype classification. An overview of metabolic dysregulation in RCC subtypes was established giving new insights into the understanding of their clinical behaviour and for the development of targeted therapeutic strategies.
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Affiliation(s)
- Lun Jing
- Laboratory Transporter in Imaging and Radiotherapy in Oncology (TIRO), Institut de biosciences et biotechnologies d'Aix-Marseille (BIAM), Commissariat à lEnergie Atomique, Nice, France.,Laboratory Transporter in Imaging and Radiotherapy in Oncology (TIRO), school of medicine, Université Nice Sophia Antipolis, Université Côte d'Azur, Nice, France
| | - Jean-Marie Guigonis
- Laboratory Transporter in Imaging and Radiotherapy in Oncology (TIRO), Institut de biosciences et biotechnologies d'Aix-Marseille (BIAM), Commissariat à lEnergie Atomique, Nice, France.,Laboratory Transporter in Imaging and Radiotherapy in Oncology (TIRO), school of medicine, Université Nice Sophia Antipolis, Université Côte d'Azur, Nice, France
| | | | - Matthieu Durand
- Urology Department, Centre Hospitalier Universitaire, Nice, France
| | - Thierry Pourcher
- Laboratory Transporter in Imaging and Radiotherapy in Oncology (TIRO), Institut de biosciences et biotechnologies d'Aix-Marseille (BIAM), Commissariat à lEnergie Atomique, Nice, France. .,Laboratory Transporter in Imaging and Radiotherapy in Oncology (TIRO), school of medicine, Université Nice Sophia Antipolis, Université Côte d'Azur, Nice, France.
| | - Damien Ambrosetti
- Central Laboratory of Anatomopathology, Centre Hospitalier Universitaire, Nice, France
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Liu G, Lee DP, Schmidt E, Prasad GL. Pathway Analysis of Global Metabolomic Profiles Identified Enrichment of Caffeine, Energy, and Arginine Metabolism in Smokers but Not Moist Snuff Consumers. Bioinform Biol Insights 2019; 13:1177932219882961. [PMID: 31666793 PMCID: PMC6798164 DOI: 10.1177/1177932219882961] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 09/18/2019] [Indexed: 12/22/2022] Open
Abstract
Existing US epidemiological data demonstrate that consumption of smokeless
tobacco, particularly moist snuff, is less harmful than cigarette smoking.
However, the molecular and biochemical changes due to moist snuff consumption
relative to smoking remain incompletely understood. We previously reported that
smokers (SMK) exhibit elevated oxidative stress and inflammation relative to
moist snuff consumers (MSC) and non-tobacco consumers (NTC), based on
metabolomic profiling data of saliva, plasma, and urine from MSC, SMK, and NTC.
In this study, we investigated the effects of tobacco consumption on additional
metabolic pathways using pathway-based analysis tools. To this end, metabolic
pathway enrichment analysis and topology analysis were performed through
pair-wise comparisons of global metabolomic profiles of SMK, MSC, and NTC. The
analyses identified >8 significantly perturbed metabolic pathways in SMK
compared with NTC and MSC in all 3 matrices. Among these differentially enriched
pathways, perturbations of caffeine metabolism, energy metabolism, and arginine
metabolism were mostly observed. In comparison, fewer enriched metabolic
pathways were identified in MSC compared with NTC (5 in plasma, none in urine
and saliva). This is consistent with our transcriptomics profiling results that
show no significant differences in peripheral blood mononuclear cell gene
expression between MSC and NTC. These findings, taken together with our previous
biochemical, metabolomic, and transcriptomic analysis results, provide a better
understanding of the relative changes in healthy tobacco consumers, and
demonstrate that chronic cigarette smoking, relative to the use of smokeless
tobacco, results in more pronounced biological changes, which could culminate in
smoking-related diseases.
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Affiliation(s)
- Gang Liu
- RAI Services Company, Winston-Salem, NC, USA
| | | | | | - G L Prasad
- RAI Services Company, Winston-Salem, NC, USA
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Manzi M, Riquelme G, Zabalegui N, Monge ME. Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics. J Pharm Biomed Anal 2019; 178:112905. [PMID: 31707200 DOI: 10.1016/j.jpba.2019.112905] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 09/27/2019] [Accepted: 10/01/2019] [Indexed: 12/24/2022]
Abstract
The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.
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Affiliation(s)
- Malena Manzi
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina; Departamento de Química Biológica, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Junín 956, C1113AAD, Ciudad de Buenos Aires, Argentina
| | - Gabriel Riquelme
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina; Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina
| | - Nicolás Zabalegui
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina; Departamento de Química Inorgánica, Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, C1425FQD, Ciudad de Buenos Aires, Argentina.
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Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways. Cancers (Basel) 2019; 11:cancers11091244. [PMID: 31450698 PMCID: PMC6770126 DOI: 10.3390/cancers11091244] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 08/20/2019] [Accepted: 08/22/2019] [Indexed: 12/24/2022] Open
Abstract
Cancer is one of the major causes of mortality worldwide and its already large burden is projected to increase significantly in the near future with a predicted 22 million new cancer cases and 13 million cancer-related deaths occurring annually by 2030. Unfortunately, current procedures for diagnosis are characterized by low diagnostic accuracies. Given the proved correlation between cancer presence and alterations of biological fluid composition, many researchers suggested their characterization to improve cancer detection at early stages. This paper reviews the information that can be found in the scientific literature, regarding the correlation of different cancer forms with the presence of specific metabolites in human urine, in a schematic and easily interpretable form, because of the huge amount of relevant literature. The originality of this paper relies on the attempt to point out the odor properties of such metabolites, and thus to highlight the correlation between urine odor alterations and cancer presence, which is proven by recent literature suggesting the analysis of urine odor for diagnostic purposes. This investigation aims to evaluate the possibility to compare the results of studies based on different approaches to be able in the future to identify those compounds responsible for urine odor alteration.
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Liu X, Zhang M, Liu X, Sun H, Guo Z, Tang X, Wang Z, Li J, Li H, Sun W, Zhang Y. Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC. Front Oncol 2019; 9:663. [PMID: 31380290 PMCID: PMC6653643 DOI: 10.3389/fonc.2019.00663] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 07/05/2019] [Indexed: 12/15/2022] Open
Abstract
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.
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Affiliation(s)
- Xiaoyan Liu
- School of Basic Medicine, Institute of Basic Medical Sciences, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Mingxin Zhang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China
- Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiang Liu
- School of Basic Medicine, Institute of Basic Medical Sciences, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Haidan Sun
- School of Basic Medicine, Institute of Basic Medical Sciences, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhengguang Guo
- School of Basic Medicine, Institute of Basic Medical Sciences, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaoyue Tang
- School of Basic Medicine, Institute of Basic Medical Sciences, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhan Wang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China
| | - Jing Li
- School of Basic Medicine, Institute of Basic Medical Sciences, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Hanzhong Li
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China
| | - Wei Sun
- School of Basic Medicine, Institute of Basic Medical Sciences, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yushi Zhang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China
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Lucarelli G, Loizzo D, Franzin R, Battaglia S, Ferro M, Cantiello F, Castellano G, Bettocchi C, Ditonno P, Battaglia M. Metabolomic insights into pathophysiological mechanisms and biomarker discovery in clear cell renal cell carcinoma. Expert Rev Mol Diagn 2019; 19:397-407. [PMID: 30983433 DOI: 10.1080/14737159.2019.1607729] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Clear cell renal cell carcinoma (ccRCC) is a metabolic disease, of which the incidence rate is increasing worldwide. Renal carcinoma is characterized by mutations in target genes involved in metabolic pathways. Metabolic reprogramming covers different processes such as aerobic glycolysis, fatty acid metabolism, and the utilization of tryptophan, glutamine, and arginine. In the era of the multi-omics approach (with integrated transcriptomics, proteomics, and metabolomics), discovering biomarkers for early diagnosis is gaining renewed importance. Areas covered: In this review, we discuss the pathophysiological mechanisms underlying ccRCC metabolic reprogramming. In addition, we describe the emerging metabolomics-based biomarkers differentially expressed in ccRCC and the rationale for the recently developed drugs specifically targeting the ccRCC metabolome. Expert opinion: A number of metabolic pathways will be explored in future years, and many of these pathways are potential therapeutic targets and may serve as diagnostic and prognostic biomarkers of ccRCC.
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Affiliation(s)
- Giuseppe Lucarelli
- a Department of Emergency and Organ Transplantation - Urology, Andrology and Kidney Transplantation Unit , University of Bari , Bari , Italy
| | - Davide Loizzo
- a Department of Emergency and Organ Transplantation - Urology, Andrology and Kidney Transplantation Unit , University of Bari , Bari , Italy
| | - Rossana Franzin
- a Department of Emergency and Organ Transplantation - Urology, Andrology and Kidney Transplantation Unit , University of Bari , Bari , Italy
| | - Stefano Battaglia
- a Department of Emergency and Organ Transplantation - Urology, Andrology and Kidney Transplantation Unit , University of Bari , Bari , Italy
| | - Matteo Ferro
- b Division of Urology , European Institute of Oncology , Milan , Italy
| | - Francesco Cantiello
- c Department of Urology , Magna Graecia University of Catanzaro , Catanzaro , Italy
| | - Giuseppe Castellano
- d Department of Emergency and Organ Transplantation - Nephrology and Dialysis Unit , University of Bari , Bari , Italy
| | - Carlo Bettocchi
- a Department of Emergency and Organ Transplantation - Urology, Andrology and Kidney Transplantation Unit , University of Bari , Bari , Italy
| | - Pasquale Ditonno
- a Department of Emergency and Organ Transplantation - Urology, Andrology and Kidney Transplantation Unit , University of Bari , Bari , Italy
| | - Michele Battaglia
- a Department of Emergency and Organ Transplantation - Urology, Andrology and Kidney Transplantation Unit , University of Bari , Bari , Italy
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Ni J, Xu L, Li W, Zheng C, Wu L. Targeted metabolomics for serum amino acids and acylcarnitines in patients with lung cancer. Exp Ther Med 2019; 18:188-198. [PMID: 31258653 PMCID: PMC6566041 DOI: 10.3892/etm.2019.7533] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022] Open
Abstract
Lung cancer is one of the most prevalent types of cancer, but accurate diagnosis remains a challenge. The aim of the present study was to create a model using amino acids and acylcarnitines for lung cancer screening. Serum samples were obtained from two groups of patients with lung cancer recruited in 2015 (including 40 patients and 100 matched controls) and 2017 (including 17 patients and 30 matched controls). Using a metabolomics method, 21 metabolites (13 types of amino acids and 8 types of acylcarnitines) were measured using liquid chromatography-tandem mass spectrometry. Data (from the 2015 and 2017 data sets) were analysed using a Mann-Whitney U test, Student's t-test, Welch's F test, receiver-operator characteristic curve or logistic regression in order to investigate the potential biomarkers. Six metabolites (glycine, valine, methionine, citrulline, arginine and C16-carnitine) were indicated to be involved in distinguishing patients with lung cancer from healthy controls. The six discriminating metabolites from the 2017 data set were further analysed using Partial least squares-discriminant analysis (PLS-DA). The PLS-DA model was verified using Spearman's correlation analysis and receiver operating characteristic curve analysis. These results demonstrated that the PLS-DA model using the six metabolites (glycine, valine, methionine, citrulline, arginine and C16-carnitine) had a strong ability to identify lung cancer. Therefore, the PLS-DA model using glycine, valine, methionine, citrulline, arginine and C16-carnitine may become a novel screening tool in patients with lung cancer.
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Affiliation(s)
- Junjun Ni
- Department of Medicinal Chemistry and Natural Medicine Chemistry, College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China.,Beijing Harmony Health Medical Diagnostics Co., Ltd., Beijing 101111, P.R. China
| | - Li Xu
- Beijing Harmony Health Medical Diagnostics Co., Ltd., Beijing 101111, P.R. China
| | - Wei Li
- Beijing Harmony Health Medical Diagnostics Co., Ltd., Beijing 101111, P.R. China
| | - Chunmei Zheng
- Beijing Harmony Health Medical Diagnostics Co., Ltd., Beijing 101111, P.R. China
| | - Lijun Wu
- Department of Medicinal Chemistry and Natural Medicine Chemistry, College of Pharmacy, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
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Kim K, Trott JF, Gao G, Chapman A, Weiss RH. Plasma metabolites and lipids associate with kidney function and kidney volume in hypertensive ADPKD patients early in the disease course. BMC Nephrol 2019; 20:66. [PMID: 30803434 PMCID: PMC6388487 DOI: 10.1186/s12882-019-1249-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 02/06/2019] [Indexed: 01/09/2023] Open
Abstract
Background Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and is characterized by gradual cyst growth and expansion, increase in kidney volume with an ultimate decline in kidney function leading to end stage renal disease (ESRD). Given the decades long period of stable kidney function while cyst growth occurs, it is important to identify those patients who will progress to ESRD. Recent data from our and other laboratories have demonstrated that metabolic reprogramming may play a key role in cystic epithelial proliferation resulting in cyst growth in ADPKD. Height corrected total kidney volume (ht-TKV) accurately reflects cyst burden and predicts future loss of kidney function. We hypothesize that specific plasma metabolites will correlate with eGFR and ht-TKV early in ADPKD, both predictors of disease progression, potentially indicative of early physiologic derangements of renal disease severity. Methods To investigate the predictive role of plasma metabolites on eGFR and/or ht-TKV, we used a non-targeted GC-TOF/MS-based metabolomics approach on hypertensive ADPKD patients in the early course of their disease. Patient data was obtained from the HALT-A randomized clinical trial at baseline including estimated glomerular filtration rate (eGFR) and measured ht-TKV. To identify individual metabolites whose intensities are significantly correlated with eGFR and ht-TKV, association analyses were performed using linear regression with each metabolite signal level as the primary predictor variable and baseline eGFR and ht-TKV as the continuous outcomes of interest, while adjusting for covariates. Significance was determined by Storey’s false discovery rate (FDR) q-values to correct for multiple testing. Results Twelve metabolites significantly correlated with eGFR and two triglycerides significantly correlated with baseline ht-TKV at FDR q-value < 0.05. Specific significant metabolites, including pseudo-uridine, indole-3-lactate, uric acid, isothreonic acid, and creatinine, have been previously shown to accumulate in plasma and/or urine in both diabetic and cystic renal diseases with advanced renal insufficiency. Conclusions This study identifies metabolic derangements in early ADPKD which may be prognostic for ADPKD disease progression. Clinical trial HALT Progression of Polycystic Kidney Disease (HALT PKD) Study A; Clinical www.clinicaltrials.gov identifier: NCT00283686; first posted January 30, 2006, last update posted March 19, 2015. Electronic supplementary material The online version of this article (10.1186/s12882-019-1249-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kyoungmi Kim
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Josephine F Trott
- Division of Nephrology, Department of Internal Medicine, University of California, Genome and Biomedical Sciences Building, Room 6311, 451 Health Sciences Dr, Davis, CA, 95616, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Arlene Chapman
- Nephrology Section, University of Chicago, Chicago, IL, USA
| | - Robert H Weiss
- Division of Nephrology, Department of Internal Medicine, University of California, Genome and Biomedical Sciences Building, Room 6311, 451 Health Sciences Dr, Davis, CA, 95616, USA. .,Cancer Center, University of California, Davis, CA, USA. .,Medical Service, VA Northern California Health Care System, Sacramento, CA, USA.
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Li S, Gao D, Jiang Y. Function, Detection and Alteration of Acylcarnitine Metabolism in Hepatocellular Carcinoma. Metabolites 2019; 9:E36. [PMID: 30795537 PMCID: PMC6410233 DOI: 10.3390/metabo9020036] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/07/2019] [Accepted: 02/14/2019] [Indexed: 01/01/2023] Open
Abstract
Acylcarnitines play an essential role in regulating the balance of intracellular sugar and lipid metabolism. They serve as carriers to transport activated long-chain fatty acids into mitochondria for β-oxidation as a major source of energy for cell activities. The liver is the most important organ for endogenous carnitine synthesis and metabolism. Hepatocellular carcinoma (HCC), a primary malignancy of the live with poor prognosis, may strongly influence the level of acylcarnitines. In this paper, the function, detection and alteration of acylcarnitine metabolism in HCC were briefly reviewed. An overview was provided to introduce the metabolic roles of acylcarnitines involved in fatty acid β-oxidation. Then different analytical platforms and methodologies were also briefly summarised. The relationship between HCC and acylcarnitine metabolism was described. Many of the studies reported that short, medium and long-chain acylcarnitines were altered in HCC patients. These findings presented current evidence in support of acylcarnitines as new candidate biomarkers for studies on the pathogenesis and development of HCC. Finally we discussed the challenges and perspectives of exploiting acylcarnitine metabolism and its related metabolic pathways as a target for HCC diagnosis and prognosis.
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Affiliation(s)
- Shangfu Li
- State Key Laboratory of Chemical Oncogenomics, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
- National & Local United Engineering Lab for Personalized Anti-tumour Drugs, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
| | - Dan Gao
- State Key Laboratory of Chemical Oncogenomics, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
- National & Local United Engineering Lab for Personalized Anti-tumour Drugs, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
- Key Laboratory of Metabolomics at Shenzhen, Shenzhen 518055, China.
| | - Yuyang Jiang
- State Key Laboratory of Chemical Oncogenomics, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.
- School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China.
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