RT Journal Article
T1 Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition.
A1 Breeur, Marie
A1 Ferrari, Pietro
A1 Dossus, Laure
A1 Jenab, Mazda
A1 Johansson, Mattias
A1 Rinaldi, Sabina
A1 Travis, Ruth C
A1 His, Mathilde
A1 Key, Tim J
A1 Schmidt, Julie A
A1 Overvad, Kim
A1 Tjønneland, Anne
A1 Kyrø, Cecilie
A1 Rothwell, Joseph A
A1 Laouali, Nasser
A1 Severi, Gianluca
A1 Kaaks, Rudolf
A1 Katzke, Verena
A1 Schulze, Matthias B
A1 Eichelmann, Fabian
A1 Palli, Domenico
A1 Grioni, Sara
A1 Panico, Salvatore
A1 Tumino, Rosario
A1 Sacerdote, Carlotta
A1 Bueno-de-Mesquita, Bas
A1 Olsen, Karina Standahl
A1 Sandanger, Torkjel Manning
A1 Nøst, Therese Haugdahl
A1 Quirós, J Ramón
A1 Bonet, Catalina
A1 Barranco, Miguel Rodríguez
A1 Chirlaque, María-Dolores
A1 Ardanaz, Eva
A1 Sandsveden, Malte
A1 Manjer, Jonas
A1 Vidman, Linda
A1 Rentoft, Matilda
A1 Muller, David
A1 Tsilidis, Kostas
A1 Heath, Alicia K
A1 Keun, Hector
A1 Adamski, Jerzy
A1 Keski-Rahkonen, Pekka
A1 Scalbert, Augustin
A1 Gunter, Marc J
A1 Viallon, Vivian
K1 Breast
K1 Cancer
K1 Colorectal
K1 EPIC
K1 Endometrial
K1 Kidney
K1 Lasso
K1 Liver
K1 Metabolomics
K1 Prostate
AB 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. 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. 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. These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.
PB BioMed Central Ltd.
YR 2022
FD 2022-09-05
LK http://hdl.handle.net/10668/20309
UL http://hdl.handle.net/10668/20309
LA en
NO Breeur M, Ferrari P, Dossus L, Jenab M, Johansson M, Rinaldi S,, et al. Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition. BMC Med. 2022 Oct 19;20(1):351
DS RISalud
RD Apr 10, 2025