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@ARTICLE{Breeur:182151,
      author       = {M. Breeur and P. Ferrari and L. Dossus and M. Jenab and M.
                      Johansson and S. Rinaldi and R. C. Travis and M. His and T.
                      J. Key and J. A. Schmidt and K. Overvad and A. Tjønneland
                      and C. Kyrø and J. A. Rothwell and N. Laouali and G. Severi
                      and R. Kaaks$^*$ and V. Katzke$^*$ and M. B. Schulze and F.
                      Eichelmann and D. Palli and S. Grioni and S. Panico and R.
                      Tumino and C. Sacerdote and B. Bueno-de-Mesquita and K. S.
                      Olsen and T. M. Sandanger and T. H. Nøst and J. R. Quirós
                      and C. Bonet and M. R. Barranco and M.-D. Chirlaque and E.
                      Ardanaz and M. Sandsveden and J. Manjer and L. Vidman and M.
                      Rentoft and D. Muller and K. Tsilidis and A. K. Heath and H.
                      Keun and J. Adamski and P. Keski-Rahkonen and A. Scalbert
                      and M. J. Gunter and V. Viallon},
      title        = {{P}an-cancer analysis of pre-diagnostic blood metabolite
                      concentrations in the {E}uropean {P}rospective
                      {I}nvestigation into {C}ancer and {N}utrition.},
      journal      = {BMC medicine},
      volume       = {20},
      number       = {1},
      issn         = {1741-7015},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer},
      reportid     = {DKFZ-2022-02462},
      pages        = {351},
      year         = {2022},
      abstract     = {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.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.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.These
                      results could provide novel insights for the identification
                      of pathways for cancer development, in particular those
                      shared across different cancer types.},
      keywords     = {Breast (Other) / Cancer (Other) / Colorectal (Other) / EPIC
                      (Other) / Endometrial (Other) / Kidney (Other) / Lasso
                      (Other) / Liver (Other) / Metabolomics (Other) / Prostate
                      (Other)},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36258205},
      doi          = {10.1186/s12916-022-02553-4},
      url          = {https://inrepo02.dkfz.de/record/182151},
}