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

Breeur, Marie ; Ferrari, Pietro ; Dossus, Laure ; Jenab, Mazda ; Johansson, Mattias ; Rinaldi, Sabina ; Travis, Ruth C. ; His, Mathilde ; Key, Tim J. and Schmidt, Julie A. , et al. (2022) In BMC Medicine 20(1).
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... (More)

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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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Breast, Cancer, Colorectal, Endometrial, EPIC, Kidney, Lasso, Liver, Metabolomics, Prostate
in
BMC Medicine
volume
20
issue
1
article number
351
publisher
BioMed Central (BMC)
external identifiers
  • scopus:85140184323
  • pmid:36258205
ISSN
1741-7015
DOI
10.1186/s12916-022-02553-4
language
English
LU publication?
yes
id
821f045b-0aa5-4df0-91e3-54c8563b2479
date added to LUP
2022-12-06 15:50:29
date last changed
2024-06-14 23:56:42
@article{821f045b-0aa5-4df0-91e3-54c8563b2479,
  abstract     = {{<p>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.</p>}},
  author       = {{Breeur, Marie and Ferrari, Pietro and Dossus, Laure and Jenab, Mazda and Johansson, Mattias and Rinaldi, Sabina and Travis, Ruth C. and His, Mathilde and Key, Tim J. and Schmidt, Julie A. and Overvad, Kim and Tjønneland, Anne and Kyrø, Cecilie and Rothwell, Joseph A. and Laouali, Nasser and Severi, Gianluca and Kaaks, Rudolf and Katzke, Verena and Schulze, Matthias B. and Eichelmann, Fabian and Palli, Domenico and Grioni, Sara and Panico, Salvatore and Tumino, Rosario and Sacerdote, Carlotta and Bueno-de-Mesquita, Bas and Olsen, Karina Standahl and Sandanger, Torkjel Manning and Nøst, Therese Haugdahl and Quirós, J. Ramón and Bonet, Catalina and Barranco, Miguel Rodríguez and Chirlaque, María Dolores and Ardanaz, Eva and Sandsveden, Malte and Manjer, Jonas and Vidman, Linda and Rentoft, Matilda and Muller, David and Tsilidis, Kostas and Heath, Alicia K. and Keun, Hector and Adamski, Jerzy and Keski-Rahkonen, Pekka and Scalbert, Augustin and Gunter, Marc J. and Viallon, Vivian}},
  issn         = {{1741-7015}},
  keywords     = {{Breast; Cancer; Colorectal; Endometrial; EPIC; Kidney; Lasso; Liver; Metabolomics; Prostate}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{BMC Medicine}},
  title        = {{Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition}},
  url          = {{http://dx.doi.org/10.1186/s12916-022-02553-4}},
  doi          = {{10.1186/s12916-022-02553-4}},
  volume       = {{20}},
  year         = {{2022}},
}