Journal Article

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

Sandsveden, Malte, Bueno-de-Mesquita, Bas, Ferrari, Pietro, His, Mathilde, Olsen, Karina Standahl, Manjer, Jonas, Bonet, Catalina, Rentoft, Matilda, Keski-Rahkonen, Pekka, Viallon, Vivian, Adamski, Jerzy, Laouali, Nasser, Quirós, J. Ramón, Travis, Ruth C., Tsilidis, Kostas, Schulze, Matthias B., Nøst, Therese Haugdahl, Overvad, Kim, Key, Tim J., Eichelmann, Fabian, Palli, Domenico, Chirlaque, María-Dolores, Heath, Alicia K., Muller, David, Breeur, Marie, Rothwell, Joseph A., Severi, Gianluca, Tjønneland, Anne, Barranco, Miguel Rodríguez, Jenab, Mazda, Schmidt, Julie A., Kyrø, Cecilie, Tumino, Rosario, Johansson, Mattias, Vidman, Linda, Grioni, Sara, Keun, Hector, Dossus, Laure, Katzke, Verena, Sandanger, Torkjel Manning, Sacerdote, Carlotta, Rinaldi, Sabina, Scalbert, Augustin, Ardanaz, Eva, Kaaks, Rudolf, Panico, Salvatore, Gunter, Marc J.

2022

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Amino acids / Biomedicine / Blood / Blood levels / Body mass / Body mass index / Body size / Breast / Breast cancer / Cancer / Cancer and Oncology / Cancer och onkologi / Carcinoma, Hepatocellular - diagnosis / Carcinoma, Hepatocellular - epidemiology / Case-Control Studies / Chronic illnesses / Clinical Medicine / Cluster analysis / Clustering / Colorectal / Colorectal cancer / Diagnostic systems / Disease / Endometrial / Endometrial cancer / Endometrium / EPIC / Epidemiology / Gallbladder / Glutamine / Health risks / Hepatocellular carcinoma / Histidine / Hormone replacement therapy / Humans / Kidney / Klinisk medicin / Laboratories / Lasso / Life Sciences / Liver / Liver cancer / Liver Neoplasms / Lysophosphatidylcholine / Lysophosphatidylcholines / Male / Mass spectrometry / Medical and Health Sciences / Medical diagnosis / Medicin och hälsovetenskap / Medicine / Medicine & Public Health / Metabolism / Metabolites / Metabolomics / Nutrition / Nutrition research / Phosphatidylcholines / Proline / Prospective Studies / Prostate / Prostate cancer / Regression analysis / Regression models / Research Article / Risk / Risk Factors / Santé publique et épidémiologie / Scientific imaging / Skin cancer / Sphingomyelins / Womens health