TY - JOUR
T1 - Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition
AU - Breeur, Marie
AU - Ferrari, Pietro
AU - Dossus, Laure
AU - Jenab, Mazda
AU - Johansson, Mattias
AU - Rinaldi, Sabina
AU - Travis, Ruth C.
AU - His, Mathilde
AU - Key, Tim J.
AU - Schmidt, Julie A.
AU - Overvad, Kim
AU - Tjønneland, Anne
AU - Kyrø, Cecilie
AU - Rothwell, Joseph A.
AU - Laouali, Nasser
AU - Severi, Gianluca
AU - Kaaks, Rudolf
AU - Katzke, Verena
AU - Schulze, Matthias B.
AU - Eichelmann, Fabian
AU - Palli, Domenico
AU - Grioni, Sara
AU - Panico, Salvatore
AU - Tumino, Rosario
AU - Sacerdote, Carlotta
AU - Bueno-de-Mesquita, Bas
AU - Olsen, Karina Standahl
AU - Sandanger, Torkjel Manning
AU - Nøst, Therese Haugdahl
AU - Quirós, J. Ramón
AU - Bonet, Catalina
AU - Barranco, Miguel Rodríguez
AU - Chirlaque, María Dolores
AU - Ardanaz, Eva
AU - Sandsveden, Malte
AU - Manjer, Jonas
AU - Vidman, Linda
AU - Rentoft, Matilda
AU - Muller, David
AU - Tsilidis, Kostas
AU - Heath, Alicia K.
AU - Keun, Hector
AU - Adamski, Jerzy
AU - Keski-Rahkonen, Pekka
AU - Scalbert, Augustin
AU - Gunter, Marc J.
AU - Viallon, Vivian
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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.
AB - 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.
KW - Breast
KW - Cancer
KW - Colorectal
KW - EPIC
KW - Endometrial
KW - Kidney
KW - Lasso
KW - Liver
KW - Metabolomics
KW - Prostate
UR - http://www.scopus.com/inward/record.url?scp=85140184323&partnerID=8YFLogxK
U2 - 10.1186/s12916-022-02553-4
DO - 10.1186/s12916-022-02553-4
M3 - Article
C2 - 36258205
AN - SCOPUS:85140184323
SN - 1741-7015
VL - 20
JO - BMC Medicine
JF - BMC Medicine
IS - 1
M1 - 351
ER -