Perturbations in the blood metabolome up to a decade before prostate cancer diagnosis in 4387 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition.

European prospective investigation into cancer and nutrition (EPIC) cancer biomarkers metabolomics prospective cohort prostate cancer

Journal

International journal of cancer
ISSN: 1097-0215
Titre abrégé: Int J Cancer
Pays: United States
ID NLM: 0042124

Informations de publication

Date de publication:
08 Oct 2024
Historique:
revised: 18 07 2024
received: 26 03 2024
accepted: 06 08 2024
medline: 8 10 2024
pubmed: 8 10 2024
entrez: 8 10 2024
Statut: aheadofprint

Résumé

Measuring pre-diagnostic blood metabolites may help identify novel risk factors for prostate cancer. Using data from 4387 matched case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC) study, we investigated the associations of 148 individual metabolites and three previously defined metabolite patterns with prostate cancer risk. Metabolites were measured by liquid chromatography-mass spectrometry. Multivariable-adjusted conditional logistic regression was used to estimate the odds ratio per standard deviation increase in log metabolite concentration and metabolite patterns (OR1SD) for prostate cancer overall, and for advanced, high-grade, aggressive. We corrected for multiple testing using the Benjamini-Hochberg method. Overall, there were no associations between specific metabolites or metabolite patterns and overall, aggressive, or high-grade prostate cancer that passed the multiple testing threshold (padj <0.05). Six phosphatidylcholines (PCs) were inversely associated with advanced prostate cancer diagnosed at or within 10 years of blood collection. metabolite patterns 1 (64 PCs and three hydroxysphingomyelins) and 2 (two acylcarnitines, glutamate, ornithine, and taurine) were also inversely associated with advanced prostate cancer; when stratified by follow-up time, these associations were observed for diagnoses at or within 10 years of recruitment (OR

Identifiants

pubmed: 39378119
doi: 10.1002/ijc.35208
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Cancer Research UK
ID : C8221/A29017
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C8221/A30904
Pays : United Kingdom

Informations de copyright

© 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.

Références

Ferlay J, Ervik M, Lam F, et al. Global Cancer Observatory: Cancer Today. Lyon, France; 2024. Available from: https://gco.iarc.who.int/today Accessed: February 15 2024.
Schmidt JA, Fensom GK, Rinaldi S, et al. Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: a prospective study of 3,057 matched case–control sets from EPIC. Int J Cancer. 2020;146:720‐730. doi:10.1002/ijc.32314
Fages A, Duarte‐Salles T, Stepien M, et al. Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort. BMC Med. 2015;13:242. doi:10.1186/s12916‐015‐0462‐9
Röhnisch HE, Kyrø C, Olsen A, Thysell E, Hallmans G, Moazzami AA. Identification of metabolites associated with prostate cancer risk: a nested case‐control study with long follow‐up in the northern Sweden health and disease study. BMC Med. 2020;18:187. doi:10.1186/s12916‐020‐01655‐1
Huang J, Mondul AM, Weinstein SJ, et al. Prospective serum Metabolomic profiling of lethal prostate cancer. Int J Cancer. 2019;145:3231‐3243. doi:10.1002/ijc.32218
Mondul AM, Moore SC, Weinstein SJ, Männistö S, Sampson JN, Albanes D. 1‐Stearoylglycerol is associated with risk of prostate cancer: results from a serum Metabolomic profiling analysis. Metabolomics. 2014;10:1036‐1041. doi:10.1007/s11306‐014‐0643‐0
Mondul AM, Moore SC, Weinstein SJ, Karoly ED, Sampson JN, Albanes D. Metabolomic analysis of prostate cancer risk in a prospective cohort: the alpha‐tocopherol, beta‐carotene cancer prevention (ATBC) study. Int J Cancer. 2015;137:2124‐2132. doi:10.1002/ijc.29576
Huang J, Mondul AM, Weinstein SJ, et al. Serum Metabolomic profiling of prostate cancer risk in the prostate, lung, colorectal, and ovarian cancer screening trial. Br J Cancer. 2016;115:1087‐1095. doi:10.1038/bjc.2016.305
Huang J, Mondul AM, Weinstein SJ, Karoly ED, Sampson JN, Albanes D. Prospective serum Metabolomic profile of prostate cancer by size and extent of primary tumour. Oncotarget. 2017;8:45190‐45199. doi:10.18632/oncotarget.16775
Fontana L, Adelaiye RM, Rastelli AL, et al. Dietary protein restriction inhibits tumour growth in human xenograft models of prostate and breast cancer. Oncotarget. 2013;4:2451‐2461. doi:10.18632/oncotarget.1586
Melnik BC, John S, Carrera‐Bastos P, Cordain L. The impact of Cow's Milk‐mediated mTORC1‐signaling in the initiation and progression of prostate cancer. Nutr Metab. 2012;9:74. doi:10.1186/1743‐7075‐9‐74
Labuschagne CF, van den Broek NJF, Mackay GM, Vousden KH, Maddocks ODK. Serine, but not glycine, supports one‐carbon metabolism and proliferation of cancer cells. Cell Rep. 2014;7:1248‐1258. doi:10.1016/j.celrep.2014.04.045
Falegan OS, Jarvi K, Vogel HJ, Hyndman ME. Seminal plasma metabolomics reveals lysine and serine dysregulation as unique features distinguishing between prostate cancer Tumours of Gleason grades 6 and 7. Prostate. 2021;81:713‐720. doi:10.1002/pros.24145
Riboli E, Hunt K, Slimani N, et al. European prospective investigation into cancer and nutrition (EPIC): study populations and data collection. Public Health Nutr. 2002;5:1113‐1124. doi:10.1079/PHN2002394
Pearce N. What does the odds ratio estimate in a case‐control study? Int J Epidemiol. 1993;22:1189‐1192. doi:10.1093/ije/22.6.1189
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Methodol. 1995;57:289‐300. doi:10.1111/j.2517‐6161.1995.tb02031.x
Gorst‐Rasmussen A, Dahm CC, Dethlefsen C, Scheike T, Overvad K. Exploring dietary patterns by using the Treelet transform. Am J Epidemiol. 2011;173:1097‐1104. doi:10.1093/aje/kwr060
Gorst‐Rasmussen A. Tt: Treelet transform with Stata. Stata J. 2012;12:130‐146. doi:10.1177/1536867X1201200108
Kelly RS, Vander Heiden MG, Giovannucci E, Mucci LA. Metabolomic biomarkers of prostate cancer: prediction, diagnosis, progression, prognosis, and recurrence. Cancer Epidemiol Biomarkers Prev. 2016;25:887‐906. doi:10.1158/1055‐9965.EPI‐15‐1223
Lima AR, Bastos MDL, Carvalho M, Guedes De Pinho P. Biomarker discovery in human prostate cancer: an update in metabolomics studies. Transl Oncol. 2016;9:357‐370. doi:10.1016/j.tranon.2016.05.004
Dudka I, Thysell E, Lundquist K, et al. Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumour aggressiveness and TMPRSS2‐ERG fusion status. BMC Cancer. 2020;20:437. doi:10.1186/s12885‐020‐06908‐z
Lokhov PG, Dashtiev MI, Moshkovskii SA, Archakov AI. Metabolite profiling of blood plasma of patients with prostate cancer. Metabolomics. 2010;6:156‐163. doi:10.1007/s11306‐009‐0187‐x
Strmiska V, Michalek P, Eckschlager T, et al. Prostate cancer‐specific hallmarks of amino acids metabolism: towards a paradigm of precision medicine. Biochim Biophys Acta (BBA)—Rev Cancer. 2019;1871:248‐258. doi:10.1016/j.bbcan.2019.01.001
Thapar R, Titus M. Recent advances in metabolic profiling and imaging of prostate cancer. CMB. 2014;2:53‐69. doi:10.2174/2213235X02666140301002510
Lloyd SM, Arnold J, Sreekumar A. Metabolomic profiling of hormone‐dependent cancers: a Bird's eye view. Trends Endocrinol Metab. 2015;26:477‐485. doi:10.1016/j.tem.2015.07.001
Miyagi Y, Higashiyama M, Gochi A, et al. Plasma free amino acid profiling of five types of cancer patients and its application for early detection. PLoS One. 2011;6:e24143. doi:10.1371/journal.pone.0024143
Fages A, Ferrari P, Monni S, et al. Investigating sources of variability in metabolomic data in the EPIC study: the principal component partial R‐square (PC‐PR2) method. Metabolomics. 2014;10:1074‐1083. doi:10.1007/s11306‐014‐0647‐9
Townsend MK, Bao Y, Poole EM, et al. Impact of pre‐analytic blood sample collection factors on metabolomics. Cancer Epidemiol Biomarkers Prev. 2016;25:823‐829. doi:10.1158/1055‐9965.EPI‐15‐1206
Sampson JN, Boca SM, Shu XO, et al. Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomarkers Prev. 2013;22:631‐640. doi:10.1158/1055‐9965.EPI‐12‐1109
Kühn T, Sookthai D, Rolle‐Kampczyk U, et al. Mid‐ and long‐term correlations of plasma metabolite concentrations measured by a targeted metabolomics approach. Metabolomics. 2016;12:184. doi:10.1007/s11306‐016‐1133‐3
Carayol M, Licaj I, Achaintre D, et al. Reliability of serum metabolites over a two‐year period: a targeted Metabolomic approach in fasting and non‐fasting Samples from EPIC. PLoS One. 2015;10:e0135437. doi:10.1371/journal.pone.0135437

Auteurs

Zoe S Grenville (ZS)

Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.

Urwah Noor (U)

Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.

Sabina Rinaldi (S)

Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.

Marc J Gunter (MJ)

Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK.

Pietro Ferrari (P)

Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.

Claudia Agnoli (C)

Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumouri, Milan, Italy.

Pilar Amiano (P)

CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain.
Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastian, Spain.
BioGipuzkoa (BioDonostia) Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain.

Alberto Catalano (A)

Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy.
Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy.

María Dolores Chirlaque (MD)

CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain.
Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain.

Sofia Christakoudi (S)

Department of Epidemiology and Biostatistics, White City Campus, Imperial College, London, UK.

Marcela Guevara (M)

Instituto de Salud Pública y Laboral de Navarra, Pamplona, Spain.
Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Navarra Institute for Health Research (IdiSNA), Pamplona, Spain.

Matthias Johansson (M)

Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.

Rudolf Kaaks (R)

Department of Cancer Epidemiology, German Cancer research Center (DKFZ), Heidelberg, Germany.

Verena Katzke (V)

Department of Cancer Epidemiology, German Cancer research Center (DKFZ), Heidelberg, Germany.

Giovanna Masala (G)

Clinical Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy.

Anja Olsen (A)

The Danish Cancer Institute, Copenhagen, Denmark.
Department of Public Health, Aarhus University, Aarhus, Denmark.

Keren Papier (K)

Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.

Maria-Jose Sánchez (MJ)

Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
Escuela Andaluza de Salud Pública (EASP), Granada, Spain.
Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain.
Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain.

Matthias B Schulze (MB)

Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany.

Anne Tjønneland (A)

The Danish Cancer Institute, Copenhagen, Denmark.
Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

Tammy Y N Tong (TYN)

Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.

Rosario Tumino (R)

Hyblean Association for Epidemiology Research, AIRE ONLUS, Ragusa, Italy.

Elisabete Weiderpass (E)

International Agency for Research on Cancer, World Health Organization, Lyon, France.

Raul Zamora-Ros (R)

Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.

Timothy J Key (TJ)

Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.

Karl Smith-Byrne (K)

Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.

Julie A Schmidt (JA)

Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.
Department of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University and Aarhus, University Hospital, Aarhus, Denmark.

Ruth C Travis (RC)

Cancer Epidemiology Unit, Oxford Population Health, University of Oxford, Oxford, UK.

Classifications MeSH