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Title: Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition
Author: Breeur, Marie
Ferrari, Pietro
Dossus, Laure
Jenab, Mazda
Johansson, Mattias
Rinaldi, Sabina
Travis, Ruth C.
His, Mathilde
Key, Tim J.
Schmidt, Julie A.
Overvad, Kim
Tjønneland, Anne
Kyrø, Cecilie
Rothwell, Joseph A.
Laouali, Nasser
Severi, Gianluca
Kaaks, Rudolf
Katzke, Verena
Schulze, Matthias B.
Eichelmann, Fabian
Palli, Domenico
Grioni, Sara
Panico, Salvatore
Tumino, Rosario
Sacerdote, Carlotta
Bueno de Mesquita, Bas
Olsen, Karina Standahl
Sandanger, Torkjel Manning
Nøst, Therese Haugdahl
Quirós, J. Ramón
Bonet Bonet, Catalina
Rodríguez Barranco, Miguel
Chirlaque, María Dolores
Ardanaz, Eva
Sandsveden, Malte
Manjer, Jonas
Vidman, Linda
Rentoft, Matilda
Muller, David
Tsilidis, Kostas
Heath, Alicia K.
Keun, Hector
Adamski, Jerzy
Keski-Rahkonen, Pekka
Scalbert, Augustin
Gunter, Marc J.
Viallon, Vivian
Keywords: Metabolòmica
Issue Date: 19-Oct-2022
Publisher: Springer Science and Business Media LLC
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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It is part of: BMC Medicine, 2022, vol. 20, num. 1
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ISSN: 1741-7015
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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