5
IRUS Total
Downloads
  Altmetric

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition

File Description SizeFormat 
s12916-022-02553-4.pdfPublished version1.56 MBAdobe PDFView/Open
Title: Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition
Authors: 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, M-D
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
Item Type: Journal Article
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.
Issue Date: 19-Oct-2022
Date of Acceptance: 5-Sep-2022
URI: http://hdl.handle.net/10044/1/100130
DOI: 10.1186/s12916-022-02553-4
ISSN: 1741-7015
Publisher: BioMed Central
Journal / Book Title: BMC Medicine
Volume: 20
Issue: 1
Copyright Statement: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Sponsor/Funder: Cancer Research UK
Funder's Grant Number: 21351
Keywords: Breast
Cancer
Colorectal
EPIC
Endometrial
Kidney
Lasso
Liver
Metabolomics
Prostate
Breast
Cancer
Colorectal
EPIC
Endometrial
Kidney
Lasso
Liver
Metabolomics
Prostate
General & Internal Medicine
11 Medical and Health Sciences
Publication Status: Published
Conference Place: England
Open Access location: https://doi.org/10.1186/s12916-022-02553-4
Article Number: ARTN 351
Online Publication Date: 2022-10-19
Appears in Collections:Department of Surgery and Cancer
School of Public Health



This item is licensed under a Creative Commons License Creative Commons