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Yang PJ, Tsai EM, Hou MF, Lee YJ, Wang TN. Global untargeted and individual targeted plasma metabolomics of breast cancer recurrence modified by hormone receptors. Breast Cancer 2024; 31:659-670. [PMID: 38652345 DOI: 10.1007/s12282-024-01579-1] [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: 12/17/2023] [Accepted: 03/26/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Breast cancer is a heterogeneous and complex etiological disease. Understanding perturbations of circulating metabolites could improve prognosis. METHODS We recruited breast cancer patients from Kaohsiung Medical University (KMU) to perform untargeted (case-control design) and targeted (patient cohort) metabolomics analyses in the discovery and validation phases to evaluate interaction effects between clinical factors and plasma metabolites using multivariable Cox proportional hazards model. RESULTS In the discovery phase, partial least squares-discriminant analysis (PLS-DA) showed that plasma metabolites were significantly different between recurrent and non-recurrent breast cancer patients. Metabolite set enrichment analysis (MSEA) and metabolomic pathway analysis (MetPA) showed that valine, leucine, and isoleucine degradation was the significant pathway, and volcano plot showed significant ten upregulated and two downregulated metabolites between recurrent and non-recurrent cases. Combined with receiver operating characteristic (ROC) curve and biological significance, creatine, valine, methionine, and mannose were selected for the validation phase. In this patient cohort with 41 new-recurrent vs. 248 non-recurrent breast cancer cases, followed for 720.49 person-years, compared with low level of valine, high valine level was significantly negatively associated with recurrent breast cancer (aHR: 0.36, 95% CI: 0.18-0.72, P = 0.004), especially in ER-negative and PR-negative status. There were interaction effects between valine and ER (Pinteraction = 0.006) as well as PR (Pinteraction = 0.002) on recurrent breast cancer. After Bonferroni correction, stratification effects between valine and hormone receptors were still significant. CONCLUSION Our study revealed that plasma metabolites were significantly different between recurrent and non-recurrent patients, proposing therapeutic insights for breast cancer prognosis.
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Affiliation(s)
- Pei-Jing Yang
- Department of Public Health, College of Health Science, Kaohsiung Medical University, No. 100, Shin-Chuan 1St Road, Sanmin Dist., Kaohsiung, 80708, Taiwan
| | - Eing-Mei Tsai
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Sanmin Dist., Kaohsiung, 80708, Taiwan
- Department of Obstetrics and Gynecology, Kaohsiung Medical University Chung-Ho Memorial Hospital, No.100, Tzyou 1st Road, Sanmin Dist., Kaohsiung, 80756, Taiwan
| | - Ming-Feng Hou
- Division of Breast Oncology and Surgery, Department of Surgery, Kaohsiung Medical University Chung-Ho Memorial Hospital, No.100, Tzyou 1st Road, Sanmin Dist., Kaohsiung, 80756, Taiwan
- Department of Biomedical Science and Environmental Biology, College of Life Science, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Sanmin Dist., Kaohsiung, 80708, Taiwan
| | - Yen-Jung Lee
- Center for Research Resources and Development, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Sanmin Dist., Kaohsiung, 80708, Taiwan
| | - Tsu-Nai Wang
- Department of Public Health, College of Health Science, Kaohsiung Medical University, No. 100, Shin-Chuan 1St Road, Sanmin Dist., Kaohsiung, 80708, Taiwan.
- Research Center for Environmental Medicine, Kaohsiung Medical University, No.100, Shin-Chuan 1st Road, Sanmin Dist., Kaohsiung, 80708, Taiwan.
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Gadwal A, Panigrahi P, Khokhar M, Sharma V, Setia P, Vishnoi JR, Elhence P, Purohit P. A critical appraisal of the role of metabolomics in breast cancer research and diagnostics. Clin Chim Acta 2024; 561:119836. [PMID: 38944408 DOI: 10.1016/j.cca.2024.119836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 06/24/2024] [Accepted: 06/24/2024] [Indexed: 07/01/2024]
Abstract
Breast cancer (BC) remains the most prevalent cancer among women worldwide, despite significant advancements in its prevention and treatment. The escalating incidence of BC globally necessitates continued research into novel diagnostic and therapeutic strategies. Metabolomics, a burgeoning field, offers a comprehensive analysis of all metabolites within a cell, tissue, system, or organism, providing crucial insights into the dynamic changes occurring during cancer development and progression. This review focuses on the metabolic alterations associated with BC, highlighting the potential of metabolomics in identifying biomarkers for early detection, diagnosis, treatment and prognosis. Metabolomics studies have revealed distinct metabolic signatures in BC, including alterations in lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic changes not only support the rapid proliferation of cancer cells but also influence the tumour microenvironment and therapeutic response. Furthermore, metabolomics holds great promise in personalized medicine, facilitating the development of tailored treatment strategies based on an individual's metabolic profile. By providing a holistic view of the metabolic changes in BC, metabolomics has the potential to revolutionize our understanding of the disease and improve patient outcomes.
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Affiliation(s)
- Ashita Gadwal
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, 342005, India
| | - Pragyan Panigrahi
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, 342005, India
| | - Manoj Khokhar
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, 342005, India
| | - Vaishali Sharma
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, 342005, India
| | - Puneet Setia
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, Rajasthan, 342005, India
| | - Jeewan Ram Vishnoi
- Department of Oncosurgery, All India Institute of Medical Sciences, Jodhpur, Rajasthan, 342005, India
| | - Poonam Elhence
- Department of Pathology, All India Institute of Medical Sciences, Jodhpur Rajasthan, 342005, India
| | - Purvi Purohit
- Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, Rajasthan, 342005, India.
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3
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Samuels E, Parks J, Chu J, McDonald T, Spinelli J, Murphy RA, Bhatti P. Metabolites Associated with Polygenic Risk of Breast Cancer. Metabolites 2024; 14:295. [PMID: 38921430 PMCID: PMC11205321 DOI: 10.3390/metabo14060295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 05/11/2024] [Accepted: 05/20/2024] [Indexed: 06/27/2024] Open
Abstract
While hundreds of germline genetic variants have been associated with breast cancer risk, the mechanisms underlying the impacts of most of these variants on breast cancer remain uncertain. Metabolomics may offer valuable insights into the mechanisms underlying genetic risks of breast cancer. Among 143 cancer-free female participants, we used linear regression analyses to explore associations between the genetic risk of breast cancer, as determined by a previously developed polygenic risk score (PRS) that included 266 single-nucleotide polymorphisms (SNPs), and 223 measures of metabolites obtained from blood samples using nuclear magnetic resonance (NMR). A false discovery rate of 10% was applied to account for multiple comparisons. PRS was statistically significantly associated with 45 metabolite measures. These were primarily measures of very low-density lipoproteins (VLDLs) and high-density lipoproteins (HDLs), including triglycerides, cholesterol, and phospholipids. For example, the strongest effect was observed with the percent ratio of medium VLDL triglycerides to total lipids (0.53 unit increase in mean-standardized ln-transformed percent ratio per unit increase in PRS; q = 0.1). While larger-scale studies are needed to confirm these results, this exploratory study presents biologically plausible findings that are consistent with previously reported associations between lipids and breast cancer risk. If confirmed, these lipids could be targeted for lifestyle and pharmaceutical interventions among women at increased genetic risk of breast cancer.
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Affiliation(s)
- Elizabeth Samuels
- Cancer Control Research, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Jaclyn Parks
- Cancer Control Research, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Jessica Chu
- Cancer Control Research, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - Treena McDonald
- Cancer Control Research, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
| | - John Spinelli
- School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Rachel A. Murphy
- Cancer Control Research, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Parveen Bhatti
- Cancer Control Research, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
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4
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Hamaya R, Sun Q, Li J, Yun H, Wang F, Curhan GC, Huang T, Manson JE, Willett WC, Rimm EB, Clish C, Liang L, Hu FB, Ma Y. 24-h urinary sodium and potassium excretions, plasma metabolomic profiles, and cardiometabolic biomarkers in the United States adults: a cross-sectional study. Am J Clin Nutr 2024:S0002-9165(24)00473-8. [PMID: 38762185 DOI: 10.1016/j.ajcnut.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/23/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND High-sodium and low-potassium intakes are associated with a higher risk of hypertension and cardiovascular disease, but there are limited data on the circulating metabolomics profiles of 24-h urinary sodium and potassium excretions in free-living individuals. OBJECTIVES We aimed to characterize the metabolomics signatures of a high-sodium and low-potassium diet in a cross-sectional study. METHODS In 1028 healthy older adults from the Women's and Men's Lifestyle Validation Studies, we investigated the association of habitual sodium and potassium intakes measured by 2 to 4 24-h urine samples with plasma metabolites (quantified using liquid chromatography-tandem mass spectrometry) and metabolomic pathways. Our primary exposures were energy-adjusted 24-h urinary sodium excretion, potassium excretion, and sodium-to-potassium ratio, calculated based on energy expenditure derived from the doubly labeled water method. We then assessed the partial correlations of their metabolomics scores, derived from elastic net regressions, with cardiometabolic biomarkers. RESULTS Higher sodium excretion was associated with 38 metabolites including higher piperine, phosphatidylethanolamine, and C5:1 carnitine. In pathway analysis, higher sodium excretion was associated with enhanced biotin and propanoate metabolism and enhanced degradation of lysine and branched-chain amino acids (BCAAs). Metabolites associated with higher potassium and lower sodium-to-potassium ratio included quinic acid and proline-betaine. After adjusting for confounding factors, the metabolomics score for sodium-to-potassium ratio positively correlated with fasting insulin (Spearman's rank correlation coefficient ρ = 0.27), C-peptide (ρ = 0.30), and triglyceride (ρ = 0.46), and negatively with adiponectin (ρ = -0.40), and high-density lipoprotein cholesterol (ρ = -0.42). CONCLUSIONS We discovered metabolites and metabolomics pathways associated with a high-sodium diet, including metabolites related to biotin, propanoate, lysine, and BCAA pathways. The metabolomics signature for a higher sodium low-potassium diet is associated with multiple components of elevated cardiometabolic risk.
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Affiliation(s)
- Rikuta Hamaya
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Qi Sun
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Jun Li
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Huan Yun
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Gary C Curhan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Renal Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Tianyi Huang
- Division of Women's Health, Department of Medicine, Connors Center for Women's Health and Gender Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Mary Horrigan Connors Center for Women's Health and Gender Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Walter C Willett
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Eric B Rimm
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Clary Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
| | - Yuan Ma
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
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5
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González Olmedo C, Díaz Beltrán L, Madrid García V, Palacios Ferrer JL, Cano Jiménez A, Urbano Cubero R, Pérez del Palacio J, Díaz C, Vicente F, Sánchez Rovira P. Assessment of Untargeted Metabolomics by Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry to Define Breast Cancer Liquid Biopsy-Based Biomarkers in Plasma Samples. Int J Mol Sci 2024; 25:5098. [PMID: 38791138 PMCID: PMC11120904 DOI: 10.3390/ijms25105098] [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: 03/28/2024] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 05/26/2024] Open
Abstract
An early diagnosis of cancer is fundamental not only in regard to reducing its mortality rate but also in terms of counteracting the progression of the tumor in the initial stages. Breast cancer (BC) is the most common tumor pathology in women and the second deathliest cancer worldwide, although its survival rate is increasing thanks to improvements in screening programs. However, the most common techniques to detect a breast tumor tend to be time-consuming, unspecific or invasive. Herein, the use of untargeted hydrophilic interaction liquid chromatography-mass spectrometry analysis appears as an analytical technique with potential use for the early detection of biomarkers in liquid biopsies from BC patients. In this research, plasma samples from 134 BC patients were compared with 136 from healthy controls (HC), and multivariate statistical analyses showed a clear separation between four BC phenotypes (LA, LB, HER2, and TN) and the HC group. As a result, we identified two candidate biomarkers that discriminated between the groups under study with a VIP > 1 and an AUC of 0.958. Thus, targeting the specific aberrant metabolic pathways in future studies may allow for better molecular stratification or early detection of the disease.
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Affiliation(s)
- Carmen González Olmedo
- Medical Oncology Unit, University Hospital of Jaén, C/Ejército Español 10, 23007 Jaén, Spain; (V.M.G.); (A.C.J.); (R.U.C.); (P.S.R.)
- Andalusian Public Foundation for Biosanitary Research in Eastern Andalusia (FIBAO), University Hospital of Jaén, C/Ejército Español 10, 23007 Jaén, Spain
| | - Leticia Díaz Beltrán
- Medical Oncology Unit, University Hospital of Jaén, C/Ejército Español 10, 23007 Jaén, Spain; (V.M.G.); (A.C.J.); (R.U.C.); (P.S.R.)
- Andalusian Public Foundation for Biosanitary Research in Eastern Andalusia (FIBAO), University Hospital of Jaén, C/Ejército Español 10, 23007 Jaén, Spain
| | - Verónica Madrid García
- Medical Oncology Unit, University Hospital of Jaén, C/Ejército Español 10, 23007 Jaén, Spain; (V.M.G.); (A.C.J.); (R.U.C.); (P.S.R.)
- Andalusian Public Foundation for Biosanitary Research in Eastern Andalusia (FIBAO), University Hospital of Jaén, C/Ejército Español 10, 23007 Jaén, Spain
| | - José Luis Palacios Ferrer
- Biopathology and Regenerative Medicine Institute (IBIMER), Centre for Biomedical Research (CIBM), University of Granada, 18016 Granada, Spain;
| | - Alicia Cano Jiménez
- Medical Oncology Unit, University Hospital of Jaén, C/Ejército Español 10, 23007 Jaén, Spain; (V.M.G.); (A.C.J.); (R.U.C.); (P.S.R.)
| | - Rocío Urbano Cubero
- Medical Oncology Unit, University Hospital of Jaén, C/Ejército Español 10, 23007 Jaén, Spain; (V.M.G.); (A.C.J.); (R.U.C.); (P.S.R.)
| | - José Pérez del Palacio
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Armilla, 18016 Granada, Spain; (J.P.d.P.); (C.D.); (F.V.)
| | - Caridad Díaz
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Armilla, 18016 Granada, Spain; (J.P.d.P.); (C.D.); (F.V.)
| | - Francisca Vicente
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Armilla, 18016 Granada, Spain; (J.P.d.P.); (C.D.); (F.V.)
| | - Pedro Sánchez Rovira
- Medical Oncology Unit, University Hospital of Jaén, C/Ejército Español 10, 23007 Jaén, Spain; (V.M.G.); (A.C.J.); (R.U.C.); (P.S.R.)
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6
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Lee JH, Gwon MR, Kim JI, Hwang SY, Seong SJ, Yoon YR, Kim M, Kim H. Alterations in Plasma Lipid Profile before and after Surgical Removal of Soft Tissue Sarcoma. Metabolites 2024; 14:250. [PMID: 38786727 PMCID: PMC11123356 DOI: 10.3390/metabo14050250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/11/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
Soft tissue sarcoma (STS) is a relatively rare malignancy, accounting for about 1% of all adult cancers. It is known to have more than 70 subtypes. Its rarity, coupled with its various subtypes, makes early diagnosis challenging. The current standard treatment for STS is surgical removal. To identify the prognosis and pathophysiology of STS, we conducted untargeted metabolic profiling on pre-operative and post-operative plasma samples from 24 STS patients who underwent surgical tumor removal. Profiling was conducted using ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry. Thirty-nine putative metabolites, including phospholipids and acyl-carnitines were identified, indicating changes in lipid metabolism. Phospholipids exhibited an increase in the post-operative samples, while acyl-carnitines showed a decrease. Notably, the levels of pre-operative lysophosphatidylcholine (LPC) O-18:0 and LPC O-16:2 were significantly lower in patients who experienced recurrence after surgery compared to those who did not. Metabolic profiling may identify aggressive tumors that are susceptible to lipid synthase inhibitors. We believe that these findings could contribute to the elucidation of the pathophysiology of STS and the development of further metabolic studies in this rare malignancy.
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Affiliation(s)
- Jae-Hwa Lee
- Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (J.-H.L.); (M.-R.G.); (S.-J.S.); (Y.-R.Y.)
- BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea
| | - Mi-Ri Gwon
- Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (J.-H.L.); (M.-R.G.); (S.-J.S.); (Y.-R.Y.)
- Clinical Omics Institute, School of Medicine, Kyungpook National University, Daegu 41405, Republic of Korea
| | - Jeung-Il Kim
- Department of Orthopaedic Surgery and Biomedical Research Institute, School of Medicine, Pusan National University, Busan 49241, Republic of Korea;
| | - Seung-young Hwang
- Pharmacokinetics Laboratory, Clinical Trial Center, Pusan National University Hospital, Busan 49241, Republic of Korea;
| | - Sook-Jin Seong
- Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (J.-H.L.); (M.-R.G.); (S.-J.S.); (Y.-R.Y.)
- BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea
- Clinical Omics Institute, School of Medicine, Kyungpook National University, Daegu 41405, Republic of Korea
- Department of Clinical Pharmacology and Therapeutics, Kyungpook National University Hospital, Daegu 41944, Republic of Korea
| | - Young-Ran Yoon
- Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (J.-H.L.); (M.-R.G.); (S.-J.S.); (Y.-R.Y.)
- BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea
- Clinical Omics Institute, School of Medicine, Kyungpook National University, Daegu 41405, Republic of Korea
- Department of Clinical Pharmacology and Therapeutics, Kyungpook National University Hospital, Daegu 41944, Republic of Korea
| | - Myungsoo Kim
- Department of Neurosurgery, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea;
| | - Hyojeong Kim
- Department of Internal Medicine, Division of Hemato-Oncology, Maryknoll Hospital, Busan 48972, Republic of Korea
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7
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Yuan Q, Yin L, He J, Zeng Q, Liang Y, Shen Y, Zu X. Metabolism of asparagine in the physiological state and cancer. Cell Commun Signal 2024; 22:163. [PMID: 38448969 PMCID: PMC10916255 DOI: 10.1186/s12964-024-01540-x] [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: 01/09/2024] [Accepted: 02/22/2024] [Indexed: 03/08/2024] Open
Abstract
Asparagine, an important amino acid in mammals, is produced in several organs and is widely used for the production of other nutrients such as glucose, proteins, lipids, and nucleotides. Asparagine has also been reported to play a vital role in the development of cancer cells. Although several types of cancer cells can synthesise asparagine alone, their synthesis levels are insufficient to meet their requirements. These cells must rely on the supply of exogenous asparagine, which is why asparagine is considered a semi-essential amino acid. Therefore, nutritional inhibition by targeting asparagine is often considered as an anti-cancer strategy and has shown success in the treatment of leukaemia. However, asparagine limitation alone does not achieve an ideal therapeutic effect because of stress responses that upregulate asparagine synthase (ASNS) to meet the requirements for asparagine in cancer cells. Various cancer cells initiate different reprogramming processes in response to the deficiency of asparagine. Therefore, it is necessary to comprehensively understand the asparagine metabolism in cancers. This review primarily discusses the physiological role of asparagine and the current progress in the field of cancer research.
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Affiliation(s)
- Qiong Yuan
- Cancer Research Institute, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
- Department of Clinical Laboratory Medicine, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
| | - Liyang Yin
- Cancer Research Institute, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
| | - Jun He
- Department of Spine Surgery, The Nanhua Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Qiting Zeng
- Cancer Research Institute, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
- Department of Clinical Laboratory Medicine, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
| | - Yuxin Liang
- Cancer Research Institute, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China
- Department of Clinical Laboratory Medicine, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China
| | - Yingying Shen
- Cancer Research Institute, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China.
| | - Xuyu Zu
- Cancer Research Institute, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, PR China.
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8
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Harewood R, Rothwell JA, Bešević J, Viallon V, Achaintre D, Gicquiau A, Rinaldi S, Wedekind R, Prehn C, Adamski J, Schmidt JA, Jacobs I, Tjønneland A, Olsen A, Severi G, Kaaks R, Katzke V, Schulze MB, Prada M, Masala G, Agnoli C, Panico S, Sacerdote C, Jakszyn PG, Sánchez MJ, Castilla J, Chirlaque MD, Atxega AA, van Guelpen B, Heath AK, Papier K, Tong TYN, Summers SA, Playdon M, Cross AJ, Keski-Rahkonen P, Chajès V, Murphy N, Gunter MJ. Association between pre-diagnostic circulating lipid metabolites and colorectal cancer risk: a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC). EBioMedicine 2024; 101:105024. [PMID: 38412638 PMCID: PMC10907191 DOI: 10.1016/j.ebiom.2024.105024] [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: 09/29/2023] [Revised: 01/26/2024] [Accepted: 02/05/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Altered lipid metabolism is a hallmark of cancer development. However, the role of specific lipid metabolites in colorectal cancer development is uncertain. METHODS In a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined associations between pre-diagnostic circulating concentrations of 97 lipid metabolites (acylcarnitines, glycerophospholipids and sphingolipids) and colorectal cancer risk. Circulating lipids were measured using targeted mass spectrometry in 1591 incident colorectal cancer cases (55% women) and 1591 matched controls. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between concentrations of individual lipid metabolites and metabolite patterns with colorectal cancer risk. FINDINGS Of the 97 assayed lipids, 24 were inversely associated (nominally p < 0.05) with colorectal cancer risk. Hydroxysphingomyelin (SM (OH)) C22:2 (ORper doubling 0.60, 95% CI 0.47-0.77) and acylakyl-phosphatidylcholine (PC ae) C34:3 (ORper doubling 0.71, 95% CI 0.59-0.87) remained associated after multiple comparisons correction. These associations were unaltered after excluding the first 5 years of follow-up after blood collection and were consistent according to sex, age at diagnosis, BMI, and colorectal subsite. Two lipid patterns, one including 26 phosphatidylcholines and all sphingolipids, and another 30 phosphatidylcholines, were weakly inversely associated with colorectal cancer. INTERPRETATION Elevated pre-diagnostic circulating levels of SM (OH) C22:2 and PC ae C34:3 and lipid patterns including phosphatidylcholines and sphingolipids were associated with lower colorectal cancer risk. This study may provide insight into potential links between specific lipids and colorectal cancer development. Additional prospective studies are needed to validate the observed associations. FUNDING World Cancer Research Fund (reference: 2013/1002); European Commission (FP7: BBMRI-LPC; reference: 313010).
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Affiliation(s)
- Rhea Harewood
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France.
| | - Joseph A Rothwell
- Centre for Epidemiology and Population Health (U1018), Exposome and Heredity Team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Gustave Roussy, F-94805, Villejuif, France
| | - Jelena Bešević
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Vivian Viallon
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - David Achaintre
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France; School of Plant Sciences and Food Security, Faculty of Biology, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Audrey Gicquiau
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Sabina Rinaldi
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Roland Wedekind
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, 85764, Neuherberg, Germany
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117597; Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany; Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia
| | - Julie A Schmidt
- Department of Clinical Medicine, Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, Olof Palmes Allé 43-45, 8200 Aarhus N, Denmark
| | - Inarie Jacobs
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Diet, Cancer and Health, Strandboulevarden 49, DK-2100, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Anja Olsen
- Danish Cancer Society Research Center, Diet, Cancer and Health, Strandboulevarden 49, DK-2100, Copenhagen, Denmark; The Department of Public Health, University of Aarhus, Aarhus, Denmark
| | - Gianluca Severi
- Centre for Epidemiology and Population Health (U1018), Exposome and Heredity Team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Gustave Roussy, F-94805, Villejuif, France; Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Florence, Italy
| | - Rudolf Kaaks
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Verena Katzke
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558, Nuthetal, Germany; Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Marcela Prada
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558, Nuthetal, Germany
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Venezian, 1, 20133, Milan, Italy
| | - Salvatore Panico
- Dipartimento Di Medicina Clinica E Chirurgia Federico Ii University, Naples, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Via Santena 7, 10126, Turin, Italy
| | - Paula Gabriela Jakszyn
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain; Blanquerna School of Health Sciences, Ramon Llull University, Barcelona, Spain
| | - Maria-Jose Sánchez
- Escuela Andaluza de Salud Pública (EASP), 18011, Granada, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, 18012, Granada, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Granada, 18071, Granada, Spain
| | - Jesús Castilla
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain; Instituto de Salud Pública de Navarra - IdiSNA, Pamplona, Spain
| | - María-Dolores Chirlaque
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain; Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain
| | - Amaia Aizpurua Atxega
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastian, Spain; Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain
| | - Bethany van Guelpen
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Keren Papier
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tammy Y N Tong
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Scott A Summers
- Department of Nutrition and Integrative Physiology and the Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, Utah, USA
| | - Mary Playdon
- Department of Nutrition and Integrative Physiology and the Diabetes and Metabolism Research Center, University of Utah, Salt Lake City, Utah, USA; Cancer Control and Population Sciences, Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | - Amanda J Cross
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Pekka Keski-Rahkonen
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Véronique Chajès
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Neil Murphy
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France
| | - Marc J Gunter
- International Agency for Research on Cancer (IARC), 25 Av. Tony Garnier, 69007, Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
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9
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Grassmann F, Mälarstig A, Dahl L, Bendes A, Dale M, Thomas CE, Gabrielsson M, Hedman ÅK, Eriksson M, Margolin S, Huang TH, Ulfstedt M, Forsberg S, Eriksson P, Johansson M, Hall P, Schwenk JM, Czene K. The impact of circulating protein levels identified by affinity proteomics on short-term, overall breast cancer risk. Br J Cancer 2024; 130:620-627. [PMID: 38135714 PMCID: PMC10876928 DOI: 10.1038/s41416-023-02541-2] [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: 06/12/2023] [Revised: 11/22/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
OBJECTIVE Current breast cancer risk prediction scores and algorithms can potentially be further improved by including molecular markers. To this end, we studied the association of circulating plasma proteins using Proximity Extension Assay (PEA) with incident breast cancer risk. SUBJECTS In this study, we included 1577 women participating in the prospective KARMA mammographic screening cohort. RESULTS In a targeted panel of 164 proteins, we found 8 candidates nominally significantly associated with short-term breast cancer risk (P < 0.05). Similarly, in an exploratory panel consisting of 2204 proteins, 115 were found nominally significantly associated (P < 0.05). However, none of the identified protein levels remained significant after adjustment for multiple testing. This lack of statistically significant findings was not due to limited power, but attributable to the small effect sizes observed even for nominally significant proteins. Similarly, adding plasma protein levels to established risk factors did not improve breast cancer risk prediction accuracy. CONCLUSIONS Our results indicate that the levels of the studied plasma proteins captured by the PEA method are unlikely to offer additional benefits for risk prediction of short-term overall breast cancer risk but could provide interesting insights into the biological basis of breast cancer in the future.
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Affiliation(s)
- Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- Institute for Clinical Research and Systems Medicine, Health and Medical University, Potsdam, Germany.
| | - Anders Mälarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
| | - Leo Dahl
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Annika Bendes
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Matilda Dale
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Cecilia Engel Thomas
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Marike Gabrielsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Åsa K Hedman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sara Margolin
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Tzu-Hsuan Huang
- Cancer Immunology Discovery, Pfizer Inc., San Diego, CA, USA
| | | | | | - Per Eriksson
- Olink Proteomics, Uppsala Science Park, Uppsala, Sweden
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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10
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Mehrotra S, Sharma S, Pandey RK. A journey from omics to clinicomics in solid cancers: Success stories and challenges. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:89-139. [PMID: 38448145 DOI: 10.1016/bs.apcsb.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.
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11
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Watts EL, Moore SC, Gunter MJ, Chatterjee N. Adiposity and cancer: meta-analysis, mechanisms, and future perspectives. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.16.24302944. [PMID: 38405761 PMCID: PMC10889047 DOI: 10.1101/2024.02.16.24302944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Obesity is a recognised risk factor for many cancers and with rising global prevalence, has become a leading cause of cancer. Here we summarise the current evidence from both population-based epidemiologic investigations and experimental studies on the role of obesity in cancer development. This review presents a new meta-analysis using data from 40 million individuals and reports positive associations with 19 cancer types. Utilising major new data from East Asia, the meta-analysis also shows that the strength of obesity and cancer associations varies regionally, with stronger relative risks for several cancers in East Asia. This review also presents current evidence on the mechanisms linking obesity and cancer and identifies promising future research directions. These include the use of new imaging data to circumvent the methodological issues involved with body mass index and the use of omics technologies to resolve biologic mechanisms with greater precision and clarity.
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Affiliation(s)
- Eleanor L Watts
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Shady Grove, MD, USA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Shady Grove, MD, USA
| | - Marc J Gunter
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, USA
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12
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Jung S, Silva S, Dallal CM, LeBlanc E, Paris K, Shepherd J, Snetselaar LG, Van Horn L, Zhang Y, Dorgan JF. Untargeted serum metabolomic profiles and breast density in young women. Cancer Causes Control 2024; 35:323-334. [PMID: 37737303 DOI: 10.1007/s10552-023-01793-w] [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: 01/21/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE OF THE STUDY Breast density is an established risk factor for breast cancer. However, little is known about metabolic influences on breast density phenotypes. We conducted untargeted serum metabolomics analyses to identify metabolic signatures associated with breast density phenotypes among young women. METHODS In a cross-sectional study of 173 young women aged 25-29 who participated in the Dietary Intervention Study in Children 2006 Follow-up Study, 449 metabolites were measured in fasting serum samples using ultra-high-performance liquid chromatography-tandem mass spectrometry. Multivariable-adjusted mixed-effects linear regression identified metabolites associated with magnetic resonance imaging measured breast density phenotypes: percent dense breast volume (%DBV), absolute dense breast volume (ADBV), and absolute non-dense breast volume (ANDBV). Metabolite results were corrected for multiple comparisons using a false discovery rate adjusted p-value (q). RESULTS The amino acids valine and leucine were significantly inversely associated with %DBV. For each 1 SD increase in valine and leucine, %DBV decreased by 20.9% (q = 0.02) and 18.4% (q = 0.04), respectively. ANDBV was significantly positively associated with 16 lipid and one amino acid metabolites, whereas no metabolites were associated with ADBV. Metabolite set enrichment analysis also revealed associations of distinct metabolic signatures with %DBV, ADBV, and ANDBV; branched chain amino acids had the strongest inverse association with %DBV (p = 0.002); whereas, diacylglycerols and phospholipids were positively associated with ANDBV (p ≤ 0.002), no significant associations were observed for ADBV. CONCLUSION Our results suggest an inverse association of branched chain amino acids with %DBV. Larger studies in diverse populations are needed.
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Affiliation(s)
- Seungyoun Jung
- Department of Nutritional Science and Food Management, Ewha Womans University, Seoul, South Korea
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, South Korea
| | - Sarah Silva
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Cher M Dallal
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA
| | - Erin LeBlanc
- Kaiser Permanente Center for Health Research, Portland, OR, USA
| | - Kenneth Paris
- Department of Pediatrics, Louisiana State University School of Medicine, New Orleans, LA, USA
| | - John Shepherd
- University of Hawaii Cancer Center, Honolulu, HI, USA
| | | | - Linda Van Horn
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yuji Zhang
- Division of Cancer Epidemiology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 West Redwood St., Howard Hall, Room 102E, Baltimore, MD, 21201, USA
| | - Joanne F Dorgan
- Division of Cancer Epidemiology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 West Redwood St., Howard Hall, Room 102E, Baltimore, MD, 21201, USA.
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13
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Gong S, Wang Q, Huang J, Huang R, Chen S, Cheng X, Liu L, Dai X, Zhong Y, Fan C, Liao Z. LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer. Methods 2024; 222:100-111. [PMID: 38228196 DOI: 10.1016/j.ymeth.2024.01.003] [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: 07/04/2023] [Revised: 10/12/2023] [Accepted: 01/11/2024] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Breast cancer (BC), the most common form of malignant cancer affecting women worldwide, was characterized by heterogeneous metabolic disorder and lack of effective biomarkers for diagnosis. The purpose of this study is to search for reliable metabolite biomarkers of BC as well as triple-negative breast cancer (TNBC) using serum metabolomics approach. METHODS In this study, an untargeted metabolomics technique based on ultra-high performance liquid chromatography combined with mass spectrometry (UHPLC-MS) was utilized to investigate the differences in serum metabolic profile between the BC group (n = 53) and non-BC group (n = 57), as well as between TNBC patients (n = 23) and non-TNBC subjects (n = 30). The multivariate data analysis, determination of the fold change and the Mann-Whitney U test were used to screen out the differential metabolites. Additionally, machine learning methods including receiver operating curve analysis and logistic regression analysis were conducted to establish diagnostic biomarker panels. RESULTS There were 36 metabolites found to be significantly different between BC and non-BC groups, and 12 metabolites discovered to be significantly different between TNBC and non-TNBC patients. Results also showed that four metabolites, including N-acetyl-D-tryptophan, 2-arachidonoylglycerol, pipecolic acid and oxoglutaric acid, were considered as vital biomarkers for the diagnosis of BC and non-BC with an area under the curve (AUC) of 0.995. Another two-metabolite panel of N-acetyl-D-tryptophan and 2-arachidonoylglycerol was discovered to discriminate TNBC from non-TNBC and produced an AUC of 0.965. CONCLUSION This study demonstrated that serum metabolomics can be used to identify BC specifically and identified promising serum metabolic markers for TNBC diagnosis.
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Affiliation(s)
- Sisi Gong
- Clinical Lab and Medical Diagnostics Laboratory, Donghai Hospital District, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, PR China
| | - Qingshui Wang
- College of Life Sciences, Fujian Normal University, Fuzhou, PR China
| | - Jiewei Huang
- The Graduate School of Fujian Medical University, Fuzhou, PR China
| | - Rongfu Huang
- Clinical Lab and Medical Diagnostics Laboratory, Donghai Hospital District, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, PR China
| | - Shanshan Chen
- Clinical Lab and Medical Diagnostics Laboratory, Donghai Hospital District, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, PR China
| | - Xiaojuan Cheng
- Clinical Lab and Medical Diagnostics Laboratory, Donghai Hospital District, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, PR China
| | - Lei Liu
- Clinical Lab and Medical Diagnostics Laboratory, Donghai Hospital District, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, PR China
| | - Xiaofang Dai
- Clinical Lab and Medical Diagnostics Laboratory, Donghai Hospital District, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, PR China
| | - Yameng Zhong
- Clinical Lab and Medical Diagnostics Laboratory, Donghai Hospital District, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, PR China
| | - Chunmei Fan
- Clinical Lab and Medical Diagnostics Laboratory, Donghai Hospital District, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, PR China.
| | - Zhijun Liao
- Clinical Lab and Medical Diagnostics Laboratory, Donghai Hospital District, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, PR China; Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, PR China.
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14
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His M, Gunter MJ, Keski-Rahkonen P, Rinaldi S. Application of Metabolomics to Epidemiologic Studies of Breast Cancer: New Perspectives for Etiology and Prevention. J Clin Oncol 2024; 42:103-115. [PMID: 37944067 DOI: 10.1200/jco.22.02754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 07/24/2023] [Accepted: 09/11/2023] [Indexed: 11/12/2023] Open
Abstract
PURPOSE To provide an overview on how the application of metabolomics (high-throughput characterization of metabolites from cells, organs, tissues, or biofluids) to population-based studies may inform our understanding of breast cancer etiology. METHODS We evaluated studies that applied metabolomic analyses to prediagnostic blood samples from prospective epidemiologic studies to identify circulating metabolites associated with breast cancer risk, overall and by breast cancer subtype and menopausal status. We provide some important considerations for the application and interpretation of metabolomics approaches in this context. RESULTS Overall, specific lipids and amino acids were indicated as the most common metabolite classes associated with breast cancer development. However, comparison of results across studies is challenging because of heterogeneity in laboratory techniques, analytical methods, sample size, and applied statistical methods. CONCLUSION Metabolomics is being increasingly applied to population-based studies for the identification of new etiologic hypotheses and/or mechanisms related to breast cancer development. Despite its success in applications to epidemiology, studies of larger sample size with detailed information on menopausal status, breast cancer subtypes, and repeated biologic samples collected over time are needed to improve comparison of results between studies and enhance validation of results, allowing potential clinical translation of findings.
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Affiliation(s)
- Mathilde His
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
- Prevention Cancer Environment Department, Centre Léon Bérard, Lyon, France
- Inserm, U1296 Unit, "Radiation: Defense, Health and Environment", Centre Léon Bérard, Lyon, France
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
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15
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Xu H, Wang X, Xu X, Liu L, Zhang Y, Yan X, Zhang Y, Dang K, Li Y. Association of plasma branched-chain amino acid with multiple cancers: A mendelian randomization analysis. Clin Nutr 2023; 42:2493-2502. [PMID: 37922693 DOI: 10.1016/j.clnu.2023.10.019] [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: 05/09/2023] [Revised: 10/08/2023] [Accepted: 10/17/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Studies have suggested a possible relevance between branched-chain amino acid (BCAA) catabolic enzymes and cancers. However, few studies have explored the variation in circulating concentrations of BCAAs. Our study used bi-directional, two-sample Mendelian randomization (MR) analysis for predicting the causality between the BCAA levels and 9 types of cancers. METHODS The largest genome-wide association studies (GWAS) provided data for total BCAAs, valine, leucine, and isoleucine from the UK Biobank. Data on multiple cancer endpoints were collected from various sources, such as the International Lung Cancer Consortium (ILCCO), the Pancreatic Cancer Cohort Consortium 1 (PanScan1), the Breast Cancer Association Consortium (BCAC), the FinnGen Biobank, and the Ovarian Cancer National Alliance (OCAC). The mainly analysis method was the inverse-variance-weighted (IVW). For assessing horizontal pleiotropy, the researchers performed MR-Egger regression and MR-PRESSO global test. Finally, the Cochran's Q test served for evaluating the heterogeneity. RESULTS Circulating total BCAAs levels (OR 1.708, 95%CI 1.168, 2.498; p = 0.006), valine levels (OR 1.747, 95%CI 1.217, 2.402; p < 0.001), leucine levels (OR 1.923, 95%CI 1.279, 2.890; p = 0.002) as well as isoleucine levels (OR 1.898, 95%CI 1.164, 3.094; p = 0.010) positively correlated with the squamous cell lung cancer risk. Nevertheless, no compelling evidence was found to support a causal link between BCAAs and any other examined cancers. CONCLUSIONS Increased circulating total-BCAAs levels, leucine levels, isoleucine levels and valine levels had higher hazard of squamous cell lung cancer. No such associations were found for BCAAs with other cancers.
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Affiliation(s)
- Huan Xu
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China; The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xuanyang Wang
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Xiaoqing Xu
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Lin Liu
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Yuntao Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Xuemin Yan
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Yingfeng Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Keke Dang
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China
| | - Ying Li
- Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, Harbin, China.
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16
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Ma J, Chen K, Ding Y, Li X, Tang Q, Jin B, Luo RY, Thyparambil S, Han Z, Chou CJ, Zhou A, Schilling J, Lin Z, Ma Y, Li Q, Zhang M, Sylvester KG, Nagpal S, McElhinney DB, Ling XB, Chen B. High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers. Front Mol Biosci 2023; 10:1257079. [PMID: 38028545 PMCID: PMC10644155 DOI: 10.3389/fmolb.2023.1257079] [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: 07/11/2023] [Accepted: 10/17/2023] [Indexed: 12/01/2023] Open
Abstract
Background: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin's lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomarker panels for the improved diagnosis or differential diagnosis of primary central nervous system lymphoma (PCNSL). Methods: In this study, a cohort of 68 individuals, including patients with primary central nervous system lymphoma (PCNSL), non-malignant disease controls, and patients with other brain tumors, was recruited. Their cerebrospinal fluid samples were analyzed using the Ultra-high performance liquid chromatography - tandem mass spectrometer (UHPLC-MS/MS) technique for targeted metabolomics analysis. Multivariate statistical analysis and logistic regression modeling were employed to identify biomarkers for both diagnosis (Dx) and differential diagnosis (Diff) purposes. The Dx and Diff models were further validated using a separate cohort of 34 subjects through logistic regression modeling. Results: A targeted analysis of 45 metabolites was conducted using UHPLC-MS/MS on cerebrospinal fluid (CSF) samples from a cohort of 68 individuals, including PCNSL patients, non-malignant disease controls, and patients with other brain tumors. Five metabolic features were identified as biomarkers for PCNSL diagnosis, while nine metabolic features were found to be biomarkers for differential diagnosis. Logistic regression modeling was employed to validate the Dx and Diff models using an independent cohort of 34 subjects. The logistic model demonstrated excellent performance, with an AUC of 0.83 for PCNSL vs. non-malignant disease controls and 0.86 for PCNSL vs. other brain tumor patients. Conclusion: Our study has successfully developed two logistic regression models utilizing metabolic markers in cerebrospinal fluid (CSF) for the diagnosis and differential diagnosis of PCNSL. These models provide valuable insights and hold promise for the future development of a non-invasive and reliable diagnostic tool for PCNSL.
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Affiliation(s)
- Jingjing Ma
- Department of Hematology, Huashan Hospital, Fudan University, Shanghai, China
| | - Kun Chen
- Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yun Ding
- mProbe Inc., Palo Alto, CA, United States
| | - Xiao Li
- mProbe Inc., Palo Alto, CA, United States
| | | | - Bo Jin
- mProbe Inc., Palo Alto, CA, United States
| | - Ruben Y. Luo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
| | - Sheeno Thyparambil
- Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhi Han
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, United States
| | - C. James Chou
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
| | | | | | - Zhiguang Lin
- Department of Hematology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yan Ma
- Department of Hematology, Huashan Hospital, Fudan University, Shanghai, China
| | - Qing Li
- Department of Hematology, Huashan Hospital, Fudan University, Shanghai, China
| | - Mengxue Zhang
- Department of Hematology, Huashan Hospital, Fudan University, Shanghai, China
| | - Karl G. Sylvester
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Seema Nagpal
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Doff B. McElhinney
- Departments of Cardiothoracic Surgery and Pediatrics (Cardiology), Stanford University School of Medicine, Stanford, CA, United States
| | - Xuefeng B. Ling
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Bobin Chen
- Department of Hematology, Huashan Hospital, Fudan University, Shanghai, China
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17
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Bel’skaya LV, Gundyrev IA, Solomatin DV. The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review. Curr Issues Mol Biol 2023; 45:7513-7537. [PMID: 37754258 PMCID: PMC10527988 DOI: 10.3390/cimb45090474] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/28/2023] Open
Abstract
This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10-15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.
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Affiliation(s)
- Lyudmila V. Bel’skaya
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
| | - Ivan A. Gundyrev
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
| | - Denis V. Solomatin
- Department of Mathematics and Mathematics Teaching Methods, Omsk State Pedagogical University, 644043 Omsk, Russia;
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18
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Yu CT, Farhat Z, Livinski AA, Loftfield E, Zanetti KA. Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review. Cancer Epidemiol Biomarkers Prev 2023; 32:1130-1145. [PMID: 37410086 PMCID: PMC10472112 DOI: 10.1158/1055-9965.epi-23-0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 04/26/2023] [Accepted: 06/28/2023] [Indexed: 07/07/2023] Open
Abstract
An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.
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Affiliation(s)
- Catherine T. Yu
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
| | - Zeinab Farhat
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Alicia A. Livinski
- National Institutes of Health Library, Office of Research Services, Office of the Director, National Institutes of Health, Bethesda, Maryland
| | - Erikka Loftfield
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Krista A. Zanetti
- Office of Nutrition Research, Division of Program Coordination, Planning, and Strategic Initiatives, Office of the Director, National Institutes of Health, Bethesda, Maryland
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19
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Orsini A, Diquigiovanni C, Bonora E. Omics Technologies Improving Breast Cancer Research and Diagnostics. Int J Mol Sci 2023; 24:12690. [PMID: 37628869 PMCID: PMC10454385 DOI: 10.3390/ijms241612690] [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: 06/12/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.
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Affiliation(s)
| | - Chiara Diquigiovanni
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40131 Bologna, Italy; (A.O.); (E.B.)
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20
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Tardito S, MacKay C. Rethinking our approach to cancer metabolism to deliver patient benefit. Br J Cancer 2023; 129:406-415. [PMID: 37340094 PMCID: PMC10403540 DOI: 10.1038/s41416-023-02324-9] [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: 02/28/2023] [Revised: 05/25/2023] [Accepted: 06/12/2023] [Indexed: 06/22/2023] Open
Abstract
Altered cellular metabolism is a major mechanism by which tumours support nutrient consumption associated with increased cellular proliferation. Selective dependency on specific metabolic pathways provides a therapeutic vulnerability that can be targeted in cancer therapy. Anti-metabolites have been used clinically since the 1940s and several agents targeting nucleotide metabolism are now well established as standard of care treatment in a range of indications. However, despite great progress in our understanding of the metabolic requirements of cancer and non-cancer cells within the tumour microenvironment, there has been limited clinical success for novel agents targeting pathways outside of nucleotide metabolism. We believe that there is significant therapeutic potential in targeting metabolic processes within cancer that is yet to be fully realised. However, current approaches to identify novel targets, test novel therapies and select patient populations most likely to benefit are sub-optimal. We highlight recent advances in technologies and understanding that will support the identification and validation of novel targets, re-evaluation of existing targets and design of optimal clinical positioning strategies to deliver patient benefit.
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Affiliation(s)
- Saverio Tardito
- The Cancer Research UK Beatson Institute, Glasgow, UK
- Institute of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Craig MacKay
- Cancer Research Horizons, The Cancer Research UK Beatson Institute, Glasgow, UK.
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21
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Xia Z, Zhao N, Liu M, Jiang D, Gao S, Ma P, Huang L. GPD1 inhibits the carcinogenesis of breast cancer through increasing PI3K/AKT-mediated lipid metabolism signaling pathway. Heliyon 2023; 9:e18128. [PMID: 37483742 PMCID: PMC10362286 DOI: 10.1016/j.heliyon.2023.e18128] [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: 02/13/2023] [Revised: 07/04/2023] [Accepted: 07/07/2023] [Indexed: 07/25/2023] Open
Abstract
Glycerol 3-phosphate dehydrogenase 1 (GPD1) acts as a tumor suppressor in various types of cancer. However, the mechanisms of GPD1 anti-tumor remain unclear in breast cancer. This study aims to explore the function and clinical relevance of GPD1 in breast cancer. We confirmed that GPD1 inhibited the ability of proliferation, migration, and invasion in GPD1 overexpression breast cancer cells by CCK-8, wound healing, and Transwell assays, respectively. We found that GPD1 overexpression activated the lipid synthesis pathway and PI3K/AKT signaling pathway. The inhibitory effect of GPD1 on breast cancer cells was also weakened after treatment with LY294002, a PI3K/AKT pathway inhibitor. These results indicated that GPD1 suppressed the carcinogenesis of breast cancer through increasing PI3K/AKT-mediated lipid signaling pathways. Meanwhile, we detected that the relationship between GPD1 level and survival rate presents a positive correlation in breast cancer patients from the Cancer Genome Atlas (TCGA) database. Therefore, GPD1 can be a prognostic biomarker and target in developing therapeutic strategies for breast cancer patients.
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Affiliation(s)
- Zhengchao Xia
- Department of Pharmacy, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan, China
| | - Ningming Zhao
- Department of Pharmacy, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan, China
| | - Mingzhou Liu
- Department of Pharmacy, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan, China
| | - DanDan Jiang
- Department of Pharmacy, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan, China
| | - Shanjun Gao
- Microbiome Laboratory, Henan Provincial People's Hospital, Zhengzhou, Henan, China
| | - Peizhi Ma
- Department of Pharmacy, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan, China
| | - Li Huang
- Department of Pathology, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan, China
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22
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Wang W, Rong Z, Wang G, Hou Y, Yang F, Qiu M. Cancer metabolites: promising biomarkers for cancer liquid biopsy. Biomark Res 2023; 11:66. [PMID: 37391812 DOI: 10.1186/s40364-023-00507-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/27/2023] [Indexed: 07/02/2023] Open
Abstract
Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.
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Affiliation(s)
- Wenxiang Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China
| | - Zhiwei Rong
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, 100191, China
| | - Guangxi Wang
- Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, 100191, China
| | - Yan Hou
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Clinical Research Center, Peking University, Beijing, 100191, China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China.
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China.
| | - Mantang Qiu
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China.
- Peking University People's Hospital Thoracic Oncology Institute, Beijing, 100044, China.
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23
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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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24
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Jiao Z, Pan Y, Chen F. The Metabolic Landscape of Breast Cancer and Its Therapeutic Implications. Mol Diagn Ther 2023; 27:349-369. [PMID: 36991275 DOI: 10.1007/s40291-023-00645-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2023] [Indexed: 03/31/2023]
Abstract
Breast cancer is the most common malignant tumor globally as of 2020 and remains the second leading cause of cancer-related death among female individuals worldwide. Metabolic reprogramming is well recognized as a hallmark of malignancy owing to the rewiring of multiple biological processes, notably, glycolysis, oxidative phosphorylation, pentose phosphate pathway, as well as lipid metabolism, which support the demands for the relentless growth of tumor cells and allows distant metastasis of cancer cells. Breast cancer cells are well documented to reprogram their metabolism via mutations or inactivation of intrinsic factors such as c-Myc, TP53, hypoxia-inducible factor, and the PI3K/AKT/mTOR pathway or crosstalk with the surrounding tumor microenvironments, including hypoxia, extracellular acidification and interaction with immune cells, cancer-associated fibroblasts, and adipocytes. Furthermore, altered metabolism contributes to acquired or inherent therapeutic resistance. Therefore, there is an urgent need to understand the metabolic plasticity underlying breast cancer progression as well as to dictate metabolic reprogramming that accounts for the resistance to standard of care. This review aims to illustrate the altered metabolism in breast cancer and its underlying mechanisms, as well as metabolic interventions in breast cancer treatment, with the intention to provide strategies for developing novel therapeutic treatments for breast cancer.
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Affiliation(s)
- Zhuoya Jiao
- School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, No. 350, Longzihu Road, Xinzhan District, Hefei, 230012, China
| | - Yunxia Pan
- School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, No. 350, Longzihu Road, Xinzhan District, Hefei, 230012, China
| | - Fengyuan Chen
- School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, No. 350, Longzihu Road, Xinzhan District, Hefei, 230012, China.
- Institute of Integrated Chinese and Western Medicine, Anhui Academy of Chinese Medicine, Hefei, China.
- Anhui Province Key Laboratory of Chinese Medicinal Formula, Hefei, China.
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25
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Danzi F, Pacchiana R, Mafficini A, Scupoli MT, Scarpa A, Donadelli M, Fiore A. To metabolomics and beyond: a technological portfolio to investigate cancer metabolism. Signal Transduct Target Ther 2023; 8:137. [PMID: 36949046 PMCID: PMC10033890 DOI: 10.1038/s41392-023-01380-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 03/24/2023] Open
Abstract
Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.
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Affiliation(s)
- Federica Danzi
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Raffaella Pacchiana
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
| | - Andrea Mafficini
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Maria T Scupoli
- Department of Neurosciences, Biomedicine and Movement Sciences, Biology and Genetics Section, University of Verona, Verona, Italy
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
- ARC-NET Research Centre, University and Hospital Trust of Verona, Verona, Italy
| | - Massimo Donadelli
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
| | - Alessandra Fiore
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy
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Mrowiec K, Kurczyk A, Jelonek K, Debik J, Giskeødegård GF, Bathen TF, Widłak P. Association of serum metabolome profile with the risk of breast cancer in participants of the HUNT2 study. Front Oncol 2023; 13:1116806. [PMID: 37007110 PMCID: PMC10061137 DOI: 10.3389/fonc.2023.1116806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
BackgroundThe serum metabolome is a potential source of molecular biomarkers associated with the risk of breast cancer. Here we aimed to analyze metabolites present in pre-diagnostic serum samples collected from healthy women participating in the Norwegian Trøndelag Health Study (HUNT2 study) for whom long-term information about developing breast cancer was available.MethodsWomen participating in the HUNT2 study who developed breast cancer within a 15-year follow-up period (BC cases) and age-matched women who stayed breast cancer-free were selected (n=453 case-control pairs). Using a high-resolution mass spectrometry approach 284 compounds were quantitatively analyzed, including 30 amino acids and biogenic amines, hexoses, and 253 lipids (acylcarnitines, glycerides, phosphatidylcholines, sphingolipids, and cholesteryl esters).ResultsAge was a major confounding factor responsible for a large heterogeneity in the dataset, hence age-defined subgroups were analyzed separately. The largest number of metabolites whose serum levels differentiated BC cases and controls (82 compounds) were observed in the subgroup of younger women (<45 years old). Noteworthy, increased levels of glycerides, phosphatidylcholines, and sphingolipids were associated with reduced risk of cancer in younger and middle-aged women (≤64 years old). On the other hand, increased levels of serum lipids were associated with an enhanced risk of breast cancer in older women (>64 years old). Moreover, several metabolites could be detected whose serum levels were different between BC cases diagnosed earlier (<5 years) and later (>10 years) after sample collecting, yet these compounds were also correlated with the age of participants. Current results were coherent with the results of the NMR-based metabolomics study performed in the cohort of HUNT2 participants, where increased serum levels of VLDL subfractions were associated with reduced risk of breast cancer in premenopausal women.ConclusionsChanges in metabolite levels detected in pre-diagnostic serum samples, which reflected an impaired lipid and amino acid metabolism, were associated with long-term risk of breast cancer in an age-dependent manner.
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Affiliation(s)
- Katarzyna Mrowiec
- Center for Translational Research and Molecular Biology of Cancer, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Agata Kurczyk
- Center for Translational Research and Molecular Biology of Cancer, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Karol Jelonek
- Center for Translational Research and Molecular Biology of Cancer, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Julia Debik
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Guro F. Giskeødegård
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Clinic of Surgery, St. Olavs University Hospital, Trondheim, Norway
| | - Tone F. Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Medical Imaging and Nuclear Medicine, St. Olavs University Hospital, Trondheim, Norway
| | - Piotr Widłak
- Clinical Research Support Centre, Medical University of Gdańsk, Gdańsk, Poland
- *Correspondence: Piotr Widłak,
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Díaz C, González-Olmedo C. Untargeted Metabolomics by Liquid Chromatography-Mass Spectrometry in Biomedical Research. Methods Mol Biol 2023; 2571:57-69. [PMID: 36152150 DOI: 10.1007/978-1-0716-2699-3_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Metabolomics, alone or in combination with other omics sciences, has shown great relevance in a large number of investigations in different branches of biomedicine, often providing novel discoveries and helping to expand the knowledge. Metabolomics analyses are carried out using different techniques, but in this chapter, we focus on liquid chromatography coupled to high-resolution mass spectrometry. The designated methodology consists of an untargeted approach for the analysis of plasma samples. The use of this method, with a reverse-phase column and electrospray ionization in positive mode, covers the detection of a broad range of metabolites, mainly of nonpolar and of intermediate polarity. This chapter also reviews the mass fragmentation spectra for the identification of bile acids, acylcarnitines, and glycerophospholipids.
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Affiliation(s)
- Caridad Díaz
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Granada, Andalucía, Spain.
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Stevens VL, Carter BD, Jacobs EJ, McCullough ML, Teras LR, Wang Y. A prospective case-cohort analysis of plasma metabolites and breast cancer risk. Breast Cancer Res 2023; 25:5. [PMID: 36650550 PMCID: PMC9847033 DOI: 10.1186/s13058-023-01602-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 01/03/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Breast cancer incidence rates have not declined despite an improvement in risk prediction and the identification of modifiable risk factors, suggesting the need to identify novel risk factors and etiological pathways involved in this cancer. Metabolomics has emerged as a promising tool to find circulating metabolites associated with breast cancer risk. METHODS Untargeted metabolomic analysis was done on prediagnostic plasma samples from a case-cohort study of 1695 incident breast cancer cases and a 1983 women subcohort drawn from Cancer Prevention Study 3. The associations of 868 named metabolites (per one standard deviation increase) with breast cancer were determined using Prentice-weighted Cox proportional hazards regression modeling. RESULTS A total of 11 metabolites were associated with breast cancer at false discovery rate (FDR) < 0.05 with the majority having inverse association [ranging from RR = 0.85 (95% CI 0.80-0.92) to RR = 0.88 (95% CI 0.82-0.94)] and one having a positive association [RR = 1.14 (95% CI 1.06-1.23)]. An additional 50 metabolites were associated at FDR < 0.20 with inverse associations ranging from RR = 0.88 (95% CI 0.81-0.94) to RR = 0.91 (95% CI 0.85-0.98) and positive associations ranging from RR = 1.13 (95% CI 1.05-1.22) to RR = 1.11 (95% CI 1.02-1.20). Several of these associations validated the findings of previous metabolomic studies. These included findings that several progestogen and androgen steroids were associated with increased risk of breast cancer in postmenopausal women and four phospholipids, and the amino acids glutamine and asparagine were associated with decreased risk of this cancer in pre- and postmenopausal women. Several novel associations were also identified, including a positive association for syringol sulfate, a biomarker for smoked meat, and 3-methylcatechol sulfate and 3-hydroxypyridine glucuronide, which are metabolites of xenobiotics used for the production of pesticides and other products. CONCLUSIONS Our study validated previous metabolite findings and identified novel metabolites associated with breast cancer risk, demonstrating the utility of large metabolomic studies to provide new leads for understanding breast cancer etiology. Our novel findings suggest that consumption of smoked meats and exposure to catechol and pyridine should be investigated as potential risk factors for breast cancer.
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Affiliation(s)
- Victoria L. Stevens
- grid.422418.90000 0004 0371 6485Department of Population Sciences, American Cancer Society, 3380 Chastain Meadows Pkwy NW Suite 200, Kennesaw, GA 30144 USA ,grid.280861.5Present Address: Social and Scientific Systems, DLH Holdings Corporation, Atlanta, GA USA
| | - Brian D. Carter
- grid.422418.90000 0004 0371 6485Department of Population Sciences, American Cancer Society, 3380 Chastain Meadows Pkwy NW Suite 200, Kennesaw, GA 30144 USA
| | - Eric J. Jacobs
- grid.422418.90000 0004 0371 6485Department of Population Sciences, American Cancer Society, 3380 Chastain Meadows Pkwy NW Suite 200, Kennesaw, GA 30144 USA
| | - Marjorie L. McCullough
- grid.422418.90000 0004 0371 6485Department of Population Sciences, American Cancer Society, 3380 Chastain Meadows Pkwy NW Suite 200, Kennesaw, GA 30144 USA
| | - Lauren R. Teras
- grid.422418.90000 0004 0371 6485Department of Population Sciences, American Cancer Society, 3380 Chastain Meadows Pkwy NW Suite 200, Kennesaw, GA 30144 USA
| | - Ying Wang
- Department of Population Sciences, American Cancer Society, 3380 Chastain Meadows Pkwy NW Suite 200, Kennesaw, GA, 30144, USA.
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Paul D, Nedelcu AM. The underexplored links between cancer and the internal body climate: Implications for cancer prevention and treatment. Front Oncol 2022; 12:1040034. [PMID: 36620608 PMCID: PMC9815514 DOI: 10.3389/fonc.2022.1040034] [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: 09/08/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022] Open
Abstract
In order to effectively manage and cure cancer we should move beyond the general view of cancer as a random process of genetic alterations leading to uncontrolled cell proliferation or simply a predictable evolutionary process involving selection for traits that increase cell fitness. In our view, cancer is a systemic disease that involves multiple interactions not only among cells within tumors or between tumors and surrounding tissues but also with the entire organism and its internal "milieu". We define the internal body climate as an emergent property resulting from spatial and temporal interactions among internal components themselves and with the external environment. The body climate itself can either prevent, promote or support cancer initiation and progression (top-down effect; i.e., body climate-induced effects on cancer), as well as be perturbed by cancer (bottom-up effect; i.e., cancer-induced body climate changes) to further favor cancer progression and spread. This positive feedback loop can move the system towards a "cancerized" organism and ultimately results in its demise. In our view, cancer not only affects the entire system; it is a reflection of an imbalance of the entire system. This model provides an integrated framework to study all aspects of cancer as a systemic disease, and also highlights unexplored links that can be altered to both prevent body climate changes that favor cancer initiation, progression and dissemination as well as manipulate or restore the body internal climate to hinder the success of cancer inception, progression and metastasis or improve therapy outcomes. To do so, we need to (i) identify cancer-relevant factors that affect specific climate components, (ii) develop 'body climate biomarkers', (iii) define 'body climate scores', and (iv) develop strategies to prevent climate changes, stop or slow the changes, or even revert the changes (climate restoration).
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Affiliation(s)
- Doru Paul
- Weill Cornell Medicine, New York, NY, United States,*Correspondence: Doru Paul,
| | - Aurora M. Nedelcu
- Biology Department, University of New Brunswick, Fredericton, NB, Canada
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Dorgan JF, Baer HJ, Bertrand KA, LeBlanc ES, Jung S, Magder LS, Snetselaar LG, Stevens VJ, Zhang Y, Van Horn L. Childhood adiposity, serum metabolites and breast density in young women. Breast Cancer Res 2022; 24:91. [PMID: 36536390 PMCID: PMC9764542 DOI: 10.1186/s13058-022-01588-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Childhood adiposity is inversely associated with young adult percent dense breast volume (%DBV) and absolute dense breast volume (ADBV), which could contribute to its protective effect for breast cancer later in life. The objective of this study was to identify metabolites in childhood serum that may mediate the inverse association between childhood adiposity and young adult breast density. METHODS Longitudinal data from 182 female participants in the Dietary Intervention Study in Children (DISC) and the DISC 2006 (DISC06) Follow-Up Study were analyzed. Childhood adiposity was assessed by anthropometry at the DISC visit with serum available that occurred closest to menarche and expressed as a body mass index (BMI) z-score. Serum metabolites were measured by untargeted metabolomics using ultra-high-performance liquid chromatography-tandem mass spectrometry. %DBV and ADBV were measured by magnetic resonance imaging at the DISC06 visit when participants were 25-29 years old. Robust mixed effects linear regression was used to identify serum metabolites associated with childhood BMI z-scores and breast density, and the R package mediation was used to quantify mediation. RESULTS Of the 115 metabolites associated with BMI z-scores (FDR < 0.20), 4 were significantly associated with %DBV and 6 with ADBV before, though not after, adjustment for multiple comparisons. Mediation analysis identified 2 unnamed metabolites, X-16576 and X-24588, as potential mediators of the inverse association between childhood adiposity and dense breast volume. X-16576 mediated 14% (95% confidence interval (CI) = 0.002, 0.46; P = 0.04) of the association of childhood adiposity with %DBV and 11% (95% CI = 0.01, 0.26; P = 0.02) of its association with ADBV. X-24588 also mediated 7% (95% CI = 0.001, 0.18; P = 0.05) of the association of childhood adiposity with ADBV. None of the other metabolites examined contributed to mediation of the childhood adiposity-%DBV association, though there was some support for contributions of lysine, valine and 7-methylguanine to mediation of the inverse association of childhood adiposity with ADBV. CONCLUSIONS Additional large longitudinal studies are needed to identify metabolites and other biomarkers that mediate the inverse association of childhood adiposity with breast density and possibly breast cancer risk.
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Affiliation(s)
- Joanne F Dorgan
- Division of Cancer Epidemiology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 West Redwood St., Howard Hall, Room 102E, Baltimore, MD, 21201, USA.
| | - Heather J Baer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Kimberly A Bertrand
- Slone Epidemiology Center, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Erin S LeBlanc
- Kaiser Permanente Center for Health Research, Portland, OR, 97227, USA
| | - Seungyoun Jung
- Department of Nutritional Science and Food Management, Ewha Womans University, Seoul, South Korea
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, South Korea
| | - Laurence S Magder
- Division of Cancer Epidemiology, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 West Redwood St., Howard Hall, Room 102E, Baltimore, MD, 21201, USA
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, 21201, USA
| | - Linda G Snetselaar
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, 52242, USA
| | - Victor J Stevens
- Kaiser Permanente Center for Health Research, Portland, OR, 97227, USA
| | - Yuji Zhang
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, 21201, USA
| | - Linda Van Horn
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
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Romanos-Nanclares A, Tabung FK, Willett WC, Rosner B, Holmes MD, Chen WY, Tamimi RM, Eliassen AH. Insulinemic potential of diet and risk of total and subtypes of breast cancer among US females. Am J Clin Nutr 2022; 116:1530-1539. [PMID: 36178066 PMCID: PMC9761760 DOI: 10.1093/ajcn/nqac284] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/15/2022] [Accepted: 09/28/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Insulin resistance and hyperinsulinemia play important roles in the progression of multiple chronic disease and conditions. Diet modulates insulin response; however, evidence is limited regarding whether diets with higher insulinemic potential increase the risk of invasive breast cancer. OBJECTIVES We aimed to prospectively evaluate the association between a food-based empirical dietary index for hyperinsulinemia (EDIH) and the incidence of invasive breast cancer. METHODS We prospectively followed 76,686 women from the Nurses' Health Study (NHS; 1984-2016) and 93,287 women from the Nurses' Health Study II (NHSII; 1991-2017). Diet was assessed by food-frequency questionnaires every 4 y. The insulinemic potential of diet was evaluated using the previously established EDIH based on circulating C-peptide concentrations. Higher scores indicate higher insulinemic potential of the diet. Covariates included reproductive, hormonal, and anthropometric factors (height and BMI at age 18 y); race; socioeconomic status; total alcohol intake; total caloric intake; and physical activity. RESULTS During 4,216,106 person-years of follow-up, we documented 10,602 breast cancer cases (6689 NHS, 3913 NHSII). In the pooled multivariable-adjusted analyses, women in the highest, compared with the lowest, EDIH quintile (Q) were at higher breast cancer risk (HRQ5 vs. Q1 = 1.15; 95% CI: 1.07, 1.24; P-trend < 0.01). Although heterogeneity by estrogen receptor (ER) status was nonsignificant, the strongest association between EDIH and breast cancer was observed for ER-negative tumors (HRQ5 vs. Q1 = 1.21; 95% CI: 1.00, 1.46; P-trend = 0.02). Among tumor molecular subtypes, the strongest associations were observed for human epidermal growth factor receptor 2 (HER2)-enriched tumors (HRQ5 vs. Q1 = 1.62; 95% CI: 1.01, 2.61; P-trend = 0.02). CONCLUSIONS A dietary pattern contributing to hyperinsulinemia and insulin resistance was associated with greater breast cancer risk, especially ER-negative and HER2-enriched tumors. Our findings suggest that dietary modifications to reduce insulinemic potential may reduce the risk of breast cancer.
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Affiliation(s)
- Andrea Romanos-Nanclares
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Fred K Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH, USA
- The Ohio State University Comprehensive Cancer Center—Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH, USA
| | - Walter C Willett
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Bernard Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Michelle D Holmes
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Wendy Y Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Liu T, Wang X, Jia P, Liu C, Wei Y, Song Y, Li S, Liu L, Wang B, Shi H. Association between serum arginine levels and cancer risk: A community-based nested case-control study. Front Nutr 2022; 9:1069113. [PMID: 36466394 PMCID: PMC9712959 DOI: 10.3389/fnut.2022.1069113] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/02/2022] [Indexed: 01/10/2024] Open
Abstract
OBJECTIVE The effect of arginine on tumors appears to be bidirectional. The association of serum arginine with the risk of incident cancer remains uncovered at present. We aimed to investigate the prospective relationship of baseline serum arginine concentrations with the risk of incident cancer in hypertensive participants. MATERIALS AND METHODS A nested, case-control study with 1,389 incident cancer cases and 1,389 matched controls was conducted using data from the China H-Type Hypertension Registry Study (CHHRS). Conditional logistic regression analyses were performed to evaluate the association between serum arginine and the risk of the overall, digestive system, non-digestive system, and site-specific cancer. RESULTS Compared with matched controls, cancer patients had higher levels of arginine (21.41 μg/mL vs. 20.88 μg/mL, p < 0.05). When serum arginine concentrations were assessed as quartiles, compared with participants in the lowest arginine quartile, participants in the highest arginine quartile had a 32% (OR = 1.32, 95% CI: 1.03 to 1.71), and 68% (OR = 1.68, 95% CI: 1.09 to 2.59) increased risk of overall and digestive system cancer, respectively, in the adjusted models. In the site-specific analysis, each standard deviation (SD) increment of serum arginine was independently and positively associated with the risk of colorectal cancer (OR = 1.35, 95% CI: 1.01 to 1.82) in the adjusted analysis. CONCLUSION We found that hypertensive individuals with higher serum arginine levels exhibited a higher risk of overall, digestive system, and colorectal cancer.
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Affiliation(s)
- Tong Liu
- Department of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Xiaomeng Wang
- Department of Education, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China
| | - Pingping Jia
- Department of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Chenan Liu
- Department of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
| | - Yaping Wei
- Key Laboratory of Precision Nutrition and Food Quality, Ministry of Education, Department of Nutrition and Health, College of Food Sciences and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Yun Song
- Shenzhen Evergreen Medical Institute, Shenzhen, China
| | - Shuqun Li
- Shenzhen Evergreen Medical Institute, Shenzhen, China
| | - Lishun Liu
- Shenzhen Evergreen Medical Institute, Shenzhen, China
| | - Binyan Wang
- Shenzhen Evergreen Medical Institute, Shenzhen, China
| | - Hanping Shi
- Department of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, China
- Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, China
- Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, China
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Breeur M, Ferrari P, Dossus L, Jenab M, Johansson M, Rinaldi S, Travis RC, His M, Key TJ, Schmidt JA, Overvad K, Tjønneland A, Kyrø C, Rothwell JA, Laouali N, Severi G, Kaaks R, Katzke V, Schulze MB, Eichelmann F, Palli D, Grioni S, Panico S, Tumino R, Sacerdote C, Bueno-de-Mesquita B, Olsen KS, Sandanger TM, Nøst TH, Quirós JR, Bonet C, Barranco MR, Chirlaque MD, Ardanaz E, Sandsveden M, Manjer J, Vidman L, Rentoft M, Muller D, Tsilidis K, Heath AK, Keun H, Adamski J, Keski-Rahkonen P, Scalbert A, Gunter MJ, Viallon V. Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition. BMC Med 2022; 20:351. [PMID: 36258205 PMCID: PMC9580145 DOI: 10.1186/s12916-022-02553-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 09/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. METHODS We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty. RESULTS Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk. CONCLUSIONS These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.
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Affiliation(s)
- Marie Breeur
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Pietro Ferrari
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Laure Dossus
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Mazda Jenab
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Mattias Johansson
- Genetics Branch, International Agency for Research on Cancer, 69372 CEDEX 08, Lyon, France
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Mathilde His
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Tim J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
- Department of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University Hospital and Aarhus University, DK-8200, Aarhus N, Denmark
| | - Kim Overvad
- Department of Public Health, Aarhus University, DK-8000, Aarhus C, Denmark
| | - Anne Tjønneland
- Danish Cancer Society Research Center Diet, Genes and Environment Nutrition and Biomarkers, DK-2100, Copenhagen, Denmark
| | - Cecilie Kyrø
- Danish Cancer Society Research Center Diet, Genes and Environment Nutrition and Biomarkers, DK-2100, Copenhagen, Denmark
| | - Joseph A Rothwell
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, 94800, Villejuif, France
| | - Nasser Laouali
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, 94800, Villejuif, France
| | - Gianluca Severi
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, 94800, Villejuif, France
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Verena Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition, 14558, Nuthetal, Germany
| | - Fabian Eichelmann
- Department of Molecular Epidemiology, German Institute of Human Nutrition, 14558, Nuthetal, Germany
- German Center for Diabetes Research (DZD), 85764, Neuherberg, Germany
| | - Domenico Palli
- Institute of Cancer Research, Prevention and Clinical Network (ISPRO), 50139, Florence, Italy
| | - Sara Grioni
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133, Milan, Italy
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Federico II University, 80131, Naples, Italy
| | - Rosario Tumino
- Hyblean Association for Epidemiological Research, AIRE-ONLUS, 97100, Ragusa, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology Città della Salute e della Scienza University-Hospital, 10126, Turin, Italy
| | - Bas Bueno-de-Mesquita
- Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720, BA, Bilthoven, The Netherlands
| | - Karina Standahl Olsen
- Department of Community Medicine, UiT The Arctic University of Norway, N-9037, Tromsø, Norway
| | | | - Therese Haugdahl Nøst
- Department of Community Medicine, UiT The Arctic University of Norway, N-9037, Tromsø, Norway
| | - J Ramón Quirós
- Public Health Directorate, 33006, Oviedo, Asturias, Spain
| | - Catalina Bonet
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
| | - Miguel Rodríguez Barranco
- Escuela Andaluza de Salud Pública (EASP), 18011, Granada, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, 18012, Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
| | - María-Dolores Chirlaque
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, 30003, Murcia, Spain
| | - Eva Ardanaz
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029, Madrid, Spain
- Navarra Public Health Institute, 31003, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, 31008, Pamplona, Spain
| | - Malte Sandsveden
- Department of Clinical Sciences Malmö Lund University, SE-214 28, Malmö, Sweden
| | - Jonas Manjer
- Departement of Surgery, Skåne University Hospital Malmö, Lund University, SE-214 28, Malmö, Sweden
| | - Linda Vidman
- Department of Radiation Sciences, Oncology Umeå University, SE-901 87, Umeå, Sweden
| | - Matilda Rentoft
- Department of Radiation Sciences, Oncology Umeå University, SE-901 87, Umeå, Sweden
| | - David Muller
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Kostas Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Hector Keun
- Department of Surgery and Cancer, Cancer Metabolism and Systems Toxicology Group, Division of Cancer, Imperial College London, London, SW7 2AZ, UK
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, 1000, Ljubljana, Slovenia
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Augustin Scalbert
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France.
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Brantley KD, Zeleznik OA, Dickerman BA, Balasubramanian R, Clish CB, Avila-Pacheco J, Rosner B, Tamimi RM, Eliassen AH. A metabolomic analysis of adiposity measures and pre- and postmenopausal breast cancer risk in the Nurses' Health Studies. Br J Cancer 2022; 127:1076-1085. [PMID: 35717425 PMCID: PMC9470549 DOI: 10.1038/s41416-022-01873-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 05/20/2022] [Accepted: 05/27/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Adiposity is consistently positively associated with postmenopausal breast cancer and inversely associated with premenopausal breast cancer risk, though the reasons for this difference remain unclear. METHODS In this nested case-control study of 1649 breast cancer cases and 1649 matched controls from the Nurses' Health Study (NHS) and the NHSII, we selected lipid and polar metabolites correlated with BMI, waist circumference, weight change since age 18, or derived fat mass, and developed a metabolomic score for each measure using LASSO regression. Logistic regression was used to investigate the association between this score and breast cancer risk, adjusted for risk factors and stratified by menopausal status at blood draw and diagnosis. RESULTS Metabolite scores developed among only premenopausal or postmenopausal women were highly correlated with scores developed in all women (r = 0.93-0.96). Higher metabolomic adiposity scores were generally inversely related to breast cancer risk among premenopausal women. Among postmenopausal women, significant positive trends with risk were observed (e.g., metabolomic waist circumference score OR Q4 vs. Q1 = 1.47, 95% CI = 1.03-2.08, P-trend = 0.01). CONCLUSIONS Though the same metabolites represented adiposity in pre- and postmenopausal women, breast cancer risk associations differed suggesting that metabolic dysregulation may have a differential association with pre- vs. postmenopausal breast cancer.
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Affiliation(s)
- Kristen D Brantley
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Barbra A Dickerman
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Bernard Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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Melatonin Regulates the Daily Levels of Plasma Amino Acids, Acylcarnitines, Biogenic Amines, Sphingomyelins, and Hexoses in a Xenograft Model of Triple Negative Breast Cancer. Int J Mol Sci 2022; 23:ijms23169105. [PMID: 36012374 PMCID: PMC9408859 DOI: 10.3390/ijms23169105] [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: 07/05/2022] [Revised: 08/04/2022] [Accepted: 08/12/2022] [Indexed: 11/17/2022] Open
Abstract
Metabolic dysregulation as a reflection of specific metabolite production and its utilization is a common feature of many human neoplasms. Melatonin, an indoleamine that is highly available during darkness, has a variety of metabolic functions in solid tumors. Because plasma metabolites undergo circadian changes, we investigated the role of melatonin on the profile of amino acids (AAs), biogenic amines, carnitines, sphingolipids, and hexoses present in the plasma of mice bearing xenograft triple negative breast cancer (MDA-MB-231 cells) over 24 h. Plasma concentrations of nine AAs were reduced by melatonin, especially during the light phase, with a profile closer to that of non-breast cancer (BC) animals. With respect to acylcarnitine levels, melatonin reduced 12 out of 24 molecules in BC-bearing animals compared to their controls, especially at 06:00 h and 15:00 h. Importantly, melatonin reduced the concentrations of asymmetric dimethylarginine, carnosine, histamine, kynurenine, methionine sulfoxide, putrescine, spermidine, spermine, and symmetric dimethylarginine, which are associated with the BC metabolite sets. Melatonin also led to reduced levels of sphingomyelins and hexoses, which showed distinct daily variations over 24 h. These results highlight the role of melatonin in controlling the levels of plasma metabolites in human BC xenografts, which may impact cancer bioenergetics, in addition to emphasizing the need for a more accurate examination of its metabolomic changes at different time points.
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Lipoprotein and metabolite associations to breast cancer risk in the HUNT2 study. Br J Cancer 2022; 127:1515-1524. [PMID: 35927310 PMCID: PMC9553939 DOI: 10.1038/s41416-022-01924-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 07/06/2022] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND The aim of this study was to gain an increased understanding of the aetiology of breast cancer, by investigating possible associations between serum lipoprotein subfractions and metabolites and the long-term risk of developing the disease. METHODS From a cohort of 65,200 participants within the Trøndelag Health Study (HUNT study), we identified all women who developed breast cancer within a 22-year follow-up period. Using nuclear magnetic resonance (NMR) spectroscopy, 28 metabolites and 89 lipoprotein subfractions were quantified from prediagnostic serum samples of future breast cancer patients and matching controls (n = 1199 case-control pairs). RESULTS Among premenopausal women (554 cases) 14 lipoprotein subfractions were associated with long-term breast cancer risk. In specific, different subfractions of VLDL particles (in particular VLDL-2, VLDL-3 and VLDL-4) were inversely associated with breast cancer. In addition, inverse associations were detected for total serum triglyceride levels and HDL-4 triglycerides. No significant association was found in postmenopausal women. CONCLUSIONS We identified several associations between lipoprotein subfractions and long-term risk of breast cancer in premenopausal women. Inverse associations between several VLDL subfractions and breast cancer risk were found, revealing an altered metabolism in the endogenous lipid pathway many years prior to a breast cancer diagnosis.
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Wedekind R, Rothwell JA, Viallon V, Keski-Rahkonen P, Schmidt JA, Chajes V, Katzke V, Johnson T, Santucci de Magistris M, Krogh V, Amiano P, Sacerdote C, Redondo-Sánchez D, Huerta JM, Tjønneland A, Pokharel P, Jakszyn P, Tumino R, Ardanaz E, Sandanger TM, Winkvist A, Hultdin J, Schulze MB, Weiderpass E, Gunter MJ, Huybrechts I, Scalbert A. Determinants of blood acylcarnitine concentrations in healthy individuals of the European Prospective Investigation into Cancer and Nutrition. Clin Nutr 2022; 41:1735-1745. [PMID: 35779425 PMCID: PMC9358353 DOI: 10.1016/j.clnu.2022.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 04/07/2022] [Accepted: 05/28/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND & AIMS Circulating levels of acylcarnitines (ACs) have been associated with the risk of various diseases such as cancer and type 2 diabetes. Diet and lifestyle factors have been shown to influence AC concentrations but a better understanding of their biological, lifestyle and metabolic determinants is needed. METHODS Circulating ACs were measured in blood by targeted (15 ACs) and untargeted metabolomics (50 ACs) in 7770 and 395 healthy participants of the European Prospective Investigation into Cancer and Nutrition (EPIC), respectively. Associations with biological and lifestyle characteristics, dietary patterns, self-reported intake of individual foods, estimated intake of carnitine and fatty acids, and fatty acids in plasma phospholipid fraction and amino acids in blood were assessed. RESULTS Age, sex and fasting status were associated with the largest proportion of AC variability (partial-r up to 0.19, 0.18 and 0.16, respectively). Some AC species of medium or long-chain fatty acid moiety were associated with the corresponding fatty acids in plasma (partial-r = 0.24) or with intake of specific foods such as dairy foods containing the same fatty acid. ACs of short-chain fatty acid moiety (propionylcarnitine and valerylcarnitine) were moderately associated with concentrations of branched-chain amino acids (partial-r = 0.5). Intake of most other foods and of carnitine showed little association with AC levels. CONCLUSIONS Our results show that determinants of ACs in blood vary according to their fatty acid moiety, and that their concentrations are related to age, sex, diet, and fasting status. Knowledge on their potential determinants may help interpret associations of ACs with disease risk and inform on potential dietary and lifestyle factors that might be modified for disease prevention.
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Affiliation(s)
- Roland Wedekind
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France.
| | - Joseph A Rothwell
- (CESP), Faculté de Medicine, Université Paris-Saclay, Inserm, Villejuif, France; Institut Gustave Roussy, Villejuif, France
| | - Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, UK
| | - Veronique Chajes
- International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Vna Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Theron Johnson
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori di Milano, Milan, Italy
| | - Pilar Amiano
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastian, Spain; Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain; Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città Della Salute e Della Scienza University-Hospital, Via Santena 7, 10126 Turin, Italy
| | - Daniel Redondo-Sánchez
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; Escuela Andaluza de Salud Pública (EASP), 18011 Granada, Spain; Instituto de Investigación Biosanitaria Ibs.GRANADA, 18012 Granada, Spain
| | - José María Huerta
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Pratik Pokharel
- Danish Cancer Society Research Center, Copenhagen, Denmark; Institute for Nutrition Research, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Paula Jakszyn
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain; Blanquerna School of Health Sciences, Ramon Llull University, Barcelona, Spain
| | - Rosario Tumino
- Hyblean Association for Epidemiological Research AIRE - ONLUS, Ragusa, Italy
| | - Eva Ardanaz
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; Navarra Public Health Institute, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Torkjel M Sandanger
- Department of Community Medicine, UiT - the Arctic University of Norway, Langnes, Tromsø, Norway
| | - Anna Winkvist
- Sustainable Health, Dept Epidemiology and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Johan Hultdin
- Medical Biosciences, Clinical Chemistry, Umeå University, Umeå, Sweden
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; University of Potsdam, Institute of Nutritional Science, Potsdam, Germany
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Inge Huybrechts
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
| | - Augustin Scalbert
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, Lyon, France
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Metabolomics of Breast Cancer: A Review. Metabolites 2022; 12:metabo12070643. [PMID: 35888767 PMCID: PMC9325024 DOI: 10.3390/metabo12070643] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/09/2022] [Accepted: 07/11/2022] [Indexed: 12/10/2022] Open
Abstract
Breast cancer is the most commonly diagnosed cancer in women worldwide. Major advances have been made towards breast cancer prevention and treatment. Unfortunately, the incidence of breast cancer is still increasing globally. Metabolomics is the field of science which studies all the metabolites in a cell, tissue, system, or organism. Metabolomics can provide information on dynamic changes occurring during cancer development and progression. The metabolites identified using cutting-edge metabolomics techniques will result in the identification of biomarkers for the early detection, diagnosis, and treatment of cancers. This review briefly introduces the metabolic changes in cancer with particular focus on breast cancer.
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Changes in Metabolism as a Diagnostic Tool for Lung Cancer: Systematic Review. Metabolites 2022; 12:metabo12060545. [PMID: 35736478 PMCID: PMC9229104 DOI: 10.3390/metabo12060545] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 05/28/2022] [Accepted: 06/02/2022] [Indexed: 02/04/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3–62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose (18F-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up.
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40
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Shu X, Chen Z, Long J, Guo X, Yang Y, Qu C, Ahn YO, Cai Q, Casey G, Gruber SB, Huyghe JR, Jee SH, Jenkins MA, Jia WH, Jung KJ, Kamatani Y, Kim DH, Kim J, Kweon SS, Le Marchand L, Matsuda K, Matsuo K, Newcomb PA, Oh JH, Ose J, Oze I, Pai RK, Pan ZZ, Pharoah PD, Playdon MC, Ren ZF, Schoen RE, Shin A, Shin MH, Shu XO, Sun X, Tangen CM, Tanikawa C, Ulrich CM, van Duijnhoven FJ, Van Guelpen B, Wolk A, Woods MO, Wu AH, Peters U, Zheng W. Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk. Cancer Epidemiol Biomarkers Prev 2022; 31:1216-1226. [PMID: 35266989 PMCID: PMC9354799 DOI: 10.1158/1055-9965.epi-21-1008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/12/2021] [Accepted: 03/04/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND The etiology of colorectal cancer is not fully understood. METHODS Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis. RESULTS Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini-Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10-8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60-2.36, P = 2.6 × 10-11; OREUR: 1.01, 95% CI, 0.99-1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer. CONCLUSIONS This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data. IMPACT The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.
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Affiliation(s)
- Xiang Shu
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA,Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Yaohua Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Yoon-Ok Ahn
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Stephen B. Gruber
- Department of Preventive Medicine & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Jeroen R. Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Sun Ha Jee
- Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - Keum Ji Jung
- Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan,Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Dong-Hyun Kim
- Department of Social and Preventive Medicine, Hallym University College of Medicine, Okcheon-dong, Korea
| | - Jeongseon Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, South Korea
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, South Korea
| | | | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Keitaro Matsuo
- Division of Molecular and Clinical Epidemiology, Aichi Cancer Center Research Institute, Nagoya, Japan,Department of Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Polly A. Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA,School of Public Health, University of Washington, Seattle, Washington, USA
| | - Jae Hwan Oh
- Center for Colorectal Cancer, National Cancer Center Hospital, National Cancer Center, Gyeonggi-do, South Korea
| | - Jennifer Ose
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Isao Oze
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Rish K. Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, Arizona, USA
| | - Zhi-Zhong Pan
- State Key Laboratory of Oncology in South China, Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - Paul D.P. Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Mary C. Playdon
- Cancer Control and Population Sciences, Huntsman Cancer Institute and Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, Utah, USA
| | - Ze-Fang Ren
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Robert E. Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Aesun Shin
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea,Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, South Korea
| | - Xiao-ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Xiaohui Sun
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA,Department of Epidemiology, Zhejiang Chinese Medical University, Zhejiang, China
| | - Catherine M. Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Chizu Tanikawa
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Cornelia M. Ulrich
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
| | | | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O. Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Anna H. Wu
- University of Southern California, Preventative Medicine, Los Angeles, California, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA,Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, USA
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Rothwell JA, Murphy N, Bešević J, Kliemann N, Jenab M, Ferrari P, Achaintre D, Gicquiau A, Vozar B, Scalbert A, Huybrechts I, Freisling H, Prehn C, Adamski J, Cross AJ, Pala VM, Boutron-Ruault MC, Dahm CC, Overvad K, Gram IT, Sandanger TM, Skeie G, Jakszyn P, Tsilidis KK, Aleksandrova K, Schulze MB, Hughes DJ, van Guelpen B, Bodén S, Sánchez MJ, Schmidt JA, Katzke V, Kühn T, Colorado-Yohar S, Tumino R, Bueno-de-Mesquita B, Vineis P, Masala G, Panico S, Eriksen AK, Tjønneland A, Aune D, Weiderpass E, Severi G, Chajès V, Gunter MJ. Metabolic Signatures of Healthy Lifestyle Patterns and Colorectal Cancer Risk in a European Cohort. Clin Gastroenterol Hepatol 2022; 20:e1061-e1082. [PMID: 33279777 PMCID: PMC9049188 DOI: 10.1016/j.cgh.2020.11.045] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/19/2020] [Accepted: 11/25/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND & AIMS Colorectal cancer risk can be lowered by adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) guidelines. We derived metabolic signatures of adherence to these guidelines and tested their associations with colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition cohort. METHODS Scores reflecting adherence to the WCRF/AICR recommendations (scale, 1-5) were calculated from participant data on weight maintenance, physical activity, diet, and alcohol among a discovery set of 5738 cancer-free European Prospective Investigation into Cancer and Nutrition participants with metabolomics data. Partial least-squares regression was used to derive fatty acid and endogenous metabolite signatures of the WCRF/AICR score in this group. In an independent set of 1608 colorectal cancer cases and matched controls, odds ratios (ORs) and 95% CIs were calculated for colorectal cancer risk per unit increase in WCRF/AICR score and per the corresponding change in metabolic signatures using multivariable conditional logistic regression. RESULTS Higher WCRF/AICR scores were characterized by metabolic signatures of increased odd-chain fatty acids, serine, glycine, and specific phosphatidylcholines. Signatures were inversely associated more strongly with colorectal cancer risk (fatty acids: OR, 0.51 per unit increase; 95% CI, 0.29-0.90; endogenous metabolites: OR, 0.62 per unit change; 95% CI, 0.50-0.78) than the WCRF/AICR score (OR, 0.93 per unit change; 95% CI, 0.86-1.00) overall. Signature associations were stronger in male compared with female participants. CONCLUSIONS Metabolite profiles reflecting adherence to WCRF/AICR guidelines and additional lifestyle or biological risk factors were associated with colorectal cancer. Measuring a specific panel of metabolites representative of a healthy or unhealthy lifestyle may identify strata of the population at higher risk of colorectal cancer.
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Affiliation(s)
- Joseph A Rothwell
- Centre for Epidemiology and Population Health, U1018, Generations and Health Team, Faculté de Médecine, Université Paris-Saclay, INSERM, Villejuif, France; Gustave Roussy, Villejuif, France; International Agency for Research on Cancer, Lyon, France.
| | - Neil Murphy
- International Agency for Research on Cancer, Lyon, France
| | - Jelena Bešević
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | | | - Mazda Jenab
- International Agency for Research on Cancer, Lyon, France
| | - Pietro Ferrari
- International Agency for Research on Cancer, Lyon, France
| | | | | | - Béatrice Vozar
- International Agency for Research on Cancer, Lyon, France
| | | | | | | | - Cornelia Prehn
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Jerzy Adamski
- Research Unit, Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Experimental Genetics, School of Life Science Weihenstephan, Technische Universität München, Neuherberg, Germany
| | - Amanda J Cross
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Valeria Maria Pala
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Marie-Christine Boutron-Ruault
- Centre for Epidemiology and Population Health, U1018, Generations and Health Team, Faculté de Médecine, Université Paris-Saclay, INSERM, Villejuif, France; Gustave Roussy, Villejuif, France
| | - Christina C Dahm
- Department of Public Health, Aarhus University, Aarhus C, Denmark
| | - Kim Overvad
- Department of Public Health, Aarhus University, Aarhus C, Denmark
| | - Inger Torhild Gram
- Faculty of Health Sciences, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Torkjel M Sandanger
- Faculty of Health Sciences, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Guri Skeie
- Faculty of Health Sciences, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Paula Jakszyn
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Barcelona, Spain; Blanquerna School of Health Sciences, Ramon Llull University, Barcelona, Spain
| | - Kostas K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Krasimira Aleksandrova
- Nutrition, Immunity and Metabolism Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Matthias B Schulze
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany; Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - David J Hughes
- School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin, Ireland
| | - Bethany van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umea University, Umea, Sweden
| | - Stina Bodén
- Department of Radiation Sciences, Oncology Unit, Umea University, Umea, Sweden
| | - Maria-José Sánchez
- CIBER Epidemiología y Salud Pública, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain; Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain
| | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Verena Katzke
- Division of Cancer Epidemiology, Deutsches Krebsforschungszentrum, Stiftung des Öffentlichen Rechts, Heidelberg, Germany
| | - Tilman Kühn
- Division of Cancer Epidemiology, Deutsches Krebsforschungszentrum, Stiftung des Öffentlichen Rechts, Heidelberg, Germany
| | - Sandra Colorado-Yohar
- Department of Epidemiology, Murcia Regional Health Council, Instituto Murciano de Investigatión Biomédica (IMIB)-Arrixaca, Murcia, Spain; CIBER Epidemiología y Salud Pública, Spain; Research Group on Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority, Ragusa, Italy
| | - Bas Bueno-de-Mesquita
- Department for Determinants of Chronic Diseases, National Institute for Public Health and the Environment, BA Bilthoven, The Netherlands
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Italian Institute of Technology, Genova, Italy
| | - Giovanna Masala
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network-Istituto per lo Studio, la Prevenzione e la Rete Oncologica (ISPRO), Florence, Italy
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy
| | - Anne Kirstine Eriksen
- Danish Cancer Society Research Center, Diet, Genes and Environment, Copenhagen, Denmark
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Diet, Genes and Environment, Copenhagen, Denmark
| | - Dagfinn Aune
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Department of Nutrition, Bjørknes University College, Oslo, Norway; Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Gianluca Severi
- Centre for Epidemiology and Population Health, U1018, Generations and Health Team, Faculté de Médecine, Université Paris-Saclay, INSERM, Villejuif, France; Gustave Roussy, Villejuif, France
| | | | - Marc J Gunter
- International Agency for Research on Cancer, Lyon, France
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Debik J, Sangermani M, Wang F, Madssen TS, Giskeødegård GF. Multivariate analysis of NMR-based metabolomic data. NMR IN BIOMEDICINE 2022; 35:e4638. [PMID: 34738674 DOI: 10.1002/nbm.4638] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/08/2021] [Accepted: 09/29/2021] [Indexed: 06/13/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.
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Affiliation(s)
- Julia Debik
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Matteo Sangermani
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Feng Wang
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
- Clinic of Surgery, St. Olavs Hospital HF, Trondheim, Norway
| | - Torfinn S Madssen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Guro F Giskeødegård
- Clinic of Surgery, St. Olavs Hospital HF, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
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Brantley KD, Zeleznik OA, Rosner B, Tamimi RM, Avila-Pacheco J, Clish CB, Eliassen AH. Plasma Metabolomics and Breast Cancer Risk Over 20 Years of Follow-up Among Postmenopausal Women in the Nurses' Health Study. Cancer Epidemiol Biomarkers Prev 2022; 31:839-850. [DOI: 10.1158/1055-9965.epi-21-1023] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/08/2021] [Accepted: 01/10/2022] [Indexed: 12/09/2022] Open
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Temprosa M, Moore SC, Zanetti KA, Appel N, Ruggieri D, Mazzilli KM, Chen KL, Kelly RS, Lasky-Su JA, Loftfield E, McClain K, Park B, Trijsburg L, Zeleznik OA, Mathé EA. COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia. Am J Epidemiol 2022; 191:147-158. [PMID: 33889934 PMCID: PMC8897993 DOI: 10.1093/aje/kwab120] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 04/14/2021] [Accepted: 04/14/2021] [Indexed: 12/13/2022] Open
Abstract
Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ewy A Mathé
- Correspondence to Dr. Ewy Mathé, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Rockville, MD 20850 (e-mail: )
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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: 6] [Impact Index Per Article: 2.0] [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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His M, Viallon V, Dossus L, Schmidt JA, Travis RC, Gunter MJ, Overvad K, Kyrø C, Tjønneland A, Lécuyer L, Rothwell JA, Severi G, Johnson T, Katzke V, Schulze MB, Masala G, Sieri S, Panico S, Tumino R, Macciotta A, Boer JMA, Monninkhof EM, Olsen KS, Nøst TH, Sandanger TM, Agudo A, Sánchez MJ, Amiano P, Colorado-Yohar SM, Ardanaz E, Vidman L, Winkvist A, Heath AK, Weiderpass E, Huybrechts I, Rinaldi S. Lifestyle correlates of eight breast cancer-related metabolites: a cross-sectional study within the EPIC cohort. BMC Med 2021; 19:312. [PMID: 34886862 PMCID: PMC8662901 DOI: 10.1186/s12916-021-02183-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/09/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Metabolomics is a promising molecular tool for identifying novel etiological pathways leading to cancer. In an earlier prospective study among pre- and postmenopausal women not using exogenous hormones, we observed a higher risk of breast cancer associated with higher blood concentrations of one metabolite (acetylcarnitine) and a lower risk associated with higher blood concentrations of seven others (arginine, asparagine, phosphatidylcholines (PCs) aa C36:3, ae C34:2, ae C36:2, ae C36:3, and ae C38:2). METHODS To identify determinants of these breast cancer-related metabolites, we conducted a cross-sectional analysis to identify their lifestyle and anthropometric correlates in 2358 women, who were previously included as controls in case-control studies nested within the European Prospective Investigation into Cancer and Nutrition cohort and not using exogenous hormones at blood collection. Associations of each metabolite concentration with 42 variables were assessed using linear regression models in a discovery set of 1572 participants. Significant associations were evaluated in a validation set (n = 786). RESULTS For the metabolites previously associated with a lower risk of breast cancer, concentrations of PCs ae C34:2, C36:2, C36:3, and C38:2 were negatively associated with adiposity and positively associated with total and saturated fat intakes. PC ae C36:2 was also negatively associated with alcohol consumption and positively associated with two scores reflecting adherence to a healthy lifestyle. Asparagine concentration was negatively associated with adiposity. Arginine and PC aa C36:3 concentrations were not associated to any of the factors examined. For the metabolite previously associated with a higher risk of breast cancer, acetylcarnitine, a positive association with age was observed. CONCLUSIONS These associations may indicate possible mechanisms underlying associations between lifestyle and anthropometric factors, and risk of breast cancer. Further research is needed to identify potential non-lifestyle correlates of the metabolites investigated.
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Affiliation(s)
- Mathilde His
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Vivian Viallon
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Laure Dossus
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Marc J Gunter
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Kim Overvad
- Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Cecilie Kyrø
- Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Copenhagen, Denmark
- Department of Public Health, Section of Environmental Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lucie Lécuyer
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, Villejuif, France
| | - Joseph A Rothwell
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, Villejuif, France
| | - Gianluca Severi
- Université Paris-Saclay, UVSQ, Inserm, CESP U1018, "Exposome and Heredity" team, Gustave Roussy, Villejuif, France
- Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Florence, Italy
| | - Theron Johnson
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Verena Katzke
- Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Sabina Sieri
- Epidemiology and Prevention Unit, Fondazione IRCCS Instituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Salvatore Panico
- Dipartimento Di Medicina Clinica E Chirurgia, Federico Ii University, Naples, Italy
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7) Ragusa, Ragusa, Italy
| | - Alessandra Macciotta
- Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | - Jolanda M A Boer
- Center for Nutrition, Prevention, and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, 3720, BA, the Netherlands
| | - Evelyn M Monninkhof
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Karina Standahl Olsen
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, NO-9037, Tromsø, Norway
| | - Therese H Nøst
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, NO-9037, Tromsø, Norway
| | - Torkjel M Sandanger
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, NO-9037, Tromsø, Norway
| | - Antonio Agudo
- Unit of Nutrition and Cancer, Catalan Institute of Oncology - ICO, L'Hospitalet de Llobregat, Spain
- Nutrition and Cancer Group; Epidemiology, Public Health, Cancer Prevention and Palliative Care Program; Bellvitge Biomedical Research Institute - IDIBELL, L'Hospitalet de Llobregat, Spain
| | - Maria-Jose Sánchez
- Escuela Andaluza de Salud Pública (EASP), Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain
| | - 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, Group of Epidemiology of Chronic and Communicable Diseases, San Sebastián, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Sandra M Colorado-Yohar
- CIBER Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain
- Research Group on Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia
| | - Eva Ardanaz
- CIBER Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Navarra Public Health Institute, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Linda Vidman
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Anna Winkvist
- Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Elisabete Weiderpass
- International Agency for Research on Cancer (IARC/WHO), Office of the Director, Lyon, France
| | - Inge Huybrechts
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France
| | - Sabina Rinaldi
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, 150 cours Albert Thomas, 69372, CEDEX 08, Lyon, France.
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Zeleznik OA, Balasubramanian R, Ren Y, Tobias DK, Rosner BA, Peng C, Bever AM, Frueh L, Jeanfavre S, Avila-Pacheco J, Clish CB, Mora S, Hu FB, Eliassen AH. Branched-Chain Amino Acids and Risk of Breast Cancer. JNCI Cancer Spectr 2021; 5:pkab059. [PMID: 34585062 PMCID: PMC8460878 DOI: 10.1093/jncics/pkab059] [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] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/16/2021] [Accepted: 07/14/2021] [Indexed: 12/12/2022] Open
Abstract
Background Circulating branched-chain amino acid (BCAA) levels reflect metabolic health and dietary intake. However, associations with breast cancer are unclear. Methods We evaluated circulating BCAA levels and breast cancer risk within the Nurses’ Health Study (NHS) and NHSII (1997 cases and 1997 controls). A total of 592 NHS women donated 2 blood samples 10 years apart. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer risk in multivariable logistic regression models. We conducted an external validation in 1765 cases in the Women’s Health Study (WHS). All statistical tests were 2-sided. Results Among NHSII participants (predominantly premenopausal at blood collection), elevated circulating BCAA levels were associated with lower breast cancer risk (eg, isoleucine highest vs lowest quartile, multivariable OR = 0.86, 95% CI = 0.65 to 1.13, Ptrend = .20), with statistically significant linear trends among fasting samples (eg, isoleucine OR = 0.74, 95% CI = 0.53 to 1.05, Ptrend = .05). In contrast, among postmenopausal women, proximate measures (<10 years from blood draw) were associated with increased breast cancer risk (eg, isoleucine OR = 1.63, 95% CI = 1.12 to 2.39, Ptrend = .01), with stronger associations among fasting samples (OR = 1.73, 95% CI = 1.15 to 2.61, Ptrend = .01). Distant measures (10-20 years since blood draw) were not associated with risk. In the WHS, a positive association was observed for distant measures of leucine among postmenopausal women (OR = 1.23, 95% CI = 0.96 to 1.58, Ptrend = .04). Conclusions No statistically significant associations between BCAA levels and breast cancer risk were consistent across NHS and WHS or NHSII and WHS. Elevated circulating BCAA levels were associated with lower breast cancer risk among predominantly premenopausal NHSII women and higher risk among postmenopausal women in NHS but not in the WHS. Additional studies are needed to understand this complex relationship.
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Affiliation(s)
- Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Correspondence to: Oana A. Zeleznik, PhD, Channing Division of Network Medicine, Brigham and Women’s Hospital,181 Longwood Ave, Boston, MA 02115, USA (e-mail: )
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts–Amherst, Amherst, MA, USA
| | - Yumeng Ren
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Deirdre K Tobias
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Bernard A Rosner
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Cheng Peng
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Alaina M Bever
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lisa Frueh
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Jeanfavre
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Julian Avila-Pacheco
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Samia Mora
- Department of Biostatistics and Epidemiology, University of Massachusetts–Amherst, Amherst, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Saigusa D, Hishinuma E, Matsukawa N, Takahashi M, Inoue J, Tadaka S, Motoike IN, Hozawa A, Izumi Y, Bamba T, Kinoshita K, Ekroos K, Koshiba S, Yamamoto M. Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values. Metabolites 2021; 11:652. [PMID: 34677367 PMCID: PMC8538467 DOI: 10.3390/metabo11100652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/16/2021] [Accepted: 09/17/2021] [Indexed: 12/18/2022] Open
Abstract
Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.
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Affiliation(s)
- Daisuke Saigusa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan; (E.H.); (N.M.); (J.I.); (S.T.); (I.N.M.); (K.K.); (S.K.); (M.Y.)
- Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan
| | - Eiji Hishinuma
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan; (E.H.); (N.M.); (J.I.); (S.T.); (I.N.M.); (K.K.); (S.K.); (M.Y.)
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan
| | - Naomi Matsukawa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan; (E.H.); (N.M.); (J.I.); (S.T.); (I.N.M.); (K.K.); (S.K.); (M.Y.)
- Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan
| | - Masatomo Takahashi
- Division of Metabolomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan; (M.T.); (Y.I.); (T.B.)
| | - Jin Inoue
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan; (E.H.); (N.M.); (J.I.); (S.T.); (I.N.M.); (K.K.); (S.K.); (M.Y.)
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan
| | - Shu Tadaka
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan; (E.H.); (N.M.); (J.I.); (S.T.); (I.N.M.); (K.K.); (S.K.); (M.Y.)
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki Aza-Aoba, Aoba-ku, Sendai 980-8579, Japan
| | - Ikuko N. Motoike
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan; (E.H.); (N.M.); (J.I.); (S.T.); (I.N.M.); (K.K.); (S.K.); (M.Y.)
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki Aza-Aoba, Aoba-ku, Sendai 980-8579, Japan
| | - Atsushi Hozawa
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan;
| | - Yoshihiro Izumi
- Division of Metabolomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan; (M.T.); (Y.I.); (T.B.)
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Takeshi Bamba
- Division of Metabolomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan; (M.T.); (Y.I.); (T.B.)
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Kengo Kinoshita
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan; (E.H.); (N.M.); (J.I.); (S.T.); (I.N.M.); (K.K.); (S.K.); (M.Y.)
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan
- Graduate School of Information Sciences, Tohoku University, 6-3-09, Aramaki Aza-Aoba, Aoba-ku, Sendai 980-8579, Japan
| | - Kim Ekroos
- Lipidomics Consulting Ltd., 02230 Espoo, Finland;
| | - Seizo Koshiba
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan; (E.H.); (N.M.); (J.I.); (S.T.); (I.N.M.); (K.K.); (S.K.); (M.Y.)
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan
| | - Masayuki Yamamoto
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan; (E.H.); (N.M.); (J.I.); (S.T.); (I.N.M.); (K.K.); (S.K.); (M.Y.)
- Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8575, Japan
- Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai 980-8573, Japan
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Viallon V, His M, Rinaldi S, Breeur M, Gicquiau A, Hemon B, Overvad K, Tjønneland A, Rostgaard-Hansen AL, Rothwell JA, Lecuyer L, Severi G, Kaaks R, Johnson T, Schulze MB, Palli D, Agnoli C, Panico S, Tumino R, Ricceri F, Verschuren WMM, Engelfriet P, Onland-Moret C, Vermeulen R, Nøst TH, Urbarova I, Zamora-Ros R, Rodriguez-Barranco M, Amiano P, Huerta JM, Ardanaz E, Melander O, Ottoson F, Vidman L, Rentoft M, Schmidt JA, Travis RC, Weiderpass E, Johansson M, Dossus L, Jenab M, Gunter MJ, Lorenzo Bermejo J, Scherer D, Salek RM, Keski-Rahkonen P, Ferrari P. A New Pipeline for the Normalization and Pooling of Metabolomics Data. Metabolites 2021; 11:631. [PMID: 34564446 PMCID: PMC8467830 DOI: 10.3390/metabo11090631] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/10/2021] [Accepted: 09/13/2021] [Indexed: 01/10/2023] Open
Abstract
Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.
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Affiliation(s)
- Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Mathilde His
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Marie Breeur
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Audrey Gicquiau
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Bertrand Hemon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Kim Overvad
- Department of Public Health, Aarhus University Bartholins Alle 2, DK-8000 Aarhus, Denmark;
| | - Anne Tjønneland
- Danish Cancer Society Research Center, DK-2100 Copenhagen, Denmark; (A.T.); (A.L.R.-H.)
| | | | - Joseph A. Rothwell
- UVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, France; (J.A.R.); (L.L.); (G.S.)
| | - Lucie Lecuyer
- UVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, France; (J.A.R.); (L.L.); (G.S.)
| | - Gianluca Severi
- UVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, France; (J.A.R.); (L.L.); (G.S.)
- Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, 50134 Florence, Italy
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (R.K.); (T.J.)
| | - Theron Johnson
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (R.K.); (T.J.)
| | - Matthias B. Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany;
- Institute of Nutritional Science, University of Potsdam, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
| | - Domenico Palli
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy;
| | - Claudia Agnoli
- Epidemiology and Prevention Unit Department of Research, Fondazione IRCCS—Istituto Nazionale dei Tumori, 20133 Milan, Italy;
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Federico II University, 80131 Naples, Italy;
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7), 97100 Ragusa, Italy;
| | - Fulvio Ricceri
- Department of Clinical and Biological Sciences, University of Turin, 10043 Orbassano, Italy;
- Unit of Epidemiology, Regional Health Service ASL TO3, 10095 Grugliasco, Italy
| | - W. M. Monique Verschuren
- National Institute for Public Health and the Environment, Centre for Nutrition, Prevention and Health Services, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands; (W.M.M.V.); (P.E.)
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; (C.O.-M.); (R.V.)
| | - Peter Engelfriet
- National Institute for Public Health and the Environment, Centre for Nutrition, Prevention and Health Services, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands; (W.M.M.V.); (P.E.)
| | - Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; (C.O.-M.); (R.V.)
| | - Roel Vermeulen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; (C.O.-M.); (R.V.)
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, 3584 CM Utrecht, The Netherlands
| | - Therese Haugdahl Nøst
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, 9037 Tromsø, Norway; (T.H.N.); (I.U.)
| | - Ilona Urbarova
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, 9037 Tromsø, Norway; (T.H.N.); (I.U.)
| | - Raul Zamora-Ros
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L’Hospitalet de Llobregat, Spain;
| | - Miguel Rodriguez-Barranco
- Escuela Andaluza de Salud Pública (EASP), 18011 Granada, Spain;
- Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
| | - Pilar Amiano
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
- Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, 20013 San Sebastián, Spain
- Biodonostia Health Research Institute, Group of Epidemiology of Chronic and Communicable Diseases, 20014 San Sebastián, Spain
| | - José Maria Huerta
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, 30007 Murcia, Spain
| | - Eva Ardanaz
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
- Navarra Public Health Institute, 31003 Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, 31008 Pamplona, Spain
| | - Olle Melander
- Department of Clincal Sciences, Lund University, SE-21 428 Malmö, Sweden;
- Department of Emergency and Internal Medicine, Skåne University Hospital, SE-20 502 Malmö, Sweden
| | - Filip Ottoson
- Department of Immunotechnology, Lund University, SE-22 100 Lund, Sweden;
| | - Linda Vidman
- Department of Radiation Sciences, Oncology, Umeå University, SE-901 87 Umeå, Sweden; (L.V.); (M.R.)
| | - Matilda Rentoft
- Department of Radiation Sciences, Oncology, Umeå University, SE-901 87 Umeå, Sweden; (L.V.); (M.R.)
| | - Julie A. Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.A.S.); (R.C.T.)
| | - Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.A.S.); (R.C.T.)
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France;
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France;
| | - Laure Dossus
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Mazda Jenab
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Marc J. Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Justo Lorenzo Bermejo
- Statistical Genetics Group, Institute of Medical Biometry, University of Heidelberg, 69120 Heidelberg, Germany; (J.L.B.); (D.S.)
| | - Dominique Scherer
- Statistical Genetics Group, Institute of Medical Biometry, University of Heidelberg, 69120 Heidelberg, Germany; (J.L.B.); (D.S.)
| | - Reza M. Salek
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Pietro Ferrari
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
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50
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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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