• Marie Breeur, Pietro Ferrari, Laure Dossus, Mazda Jenab, Mattias Johansson, Sabina Rinaldi, Ruth C Travis, Mathilde His, Tim J Key, Julie A Schmidt, Kim Overvad, Anne Tjønneland, Cecilie Kyrø, Joseph A Rothwell, Nasser Laouali, Gianluca Severi, Rudolf Kaaks, Verena Katzke, Matthias B Schulze, Fabian Eichelmann, Domenico Palli, Sara Grioni, Salvatore Panico, Rosario Tumino, Carlotta Sacerdote, Bas Bueno-de-Mesquita, Karina Standahl Olsen, Torkjel Manning Sandanger, Therese Haugdahl Nøst, J Ramón Quirós, Catalina Bonet, BarrancoMiguel RodríguezMREscuela Andaluza de Salud Pública (EASP), 18011, Granada, Spain.Instituto de Investigación Biosanitaria ibs. GRANADA, 18012, Granada, Spain.Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 280, María-Dolores Chirlaque, Eva Ardanaz, Malte Sandsveden, Jonas Manjer, Linda Vidman, Matilda Rentoft, David Muller, Kostas Tsilidis, Alicia K Heath, Hector Keun, Jerzy Adamski, Pekka Keski-Rahkonen, Augustin Scalbert, Marc J Gunter, and Vivian Viallon.
• Nutrition and Metabolism Branch, International Agency for Research on Cancer, NME Branch, 69372 CEDEX 08, Lyon, France.
• Bmc Med. 2022 Oct 19; 20 (1): 351351.

BackgroundEpidemiological 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.MethodsWe 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.ResultsOut 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.ConclusionsThese results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.© 2022. The Author(s).

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