% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Gumpenberger:167835,
      author       = {T. Gumpenberger and S. Brezina and P. Keski-Rahkonen and A.
                      Baierl and N. Robinot and G. Leeb and N. Habermann$^*$ and
                      D. E. G. Kok and A. Scalbert and P.-M. Ueland and C. M.
                      Ulrich and A. Gsur},
      title        = {{U}ntargeted {M}etabolomics {R}eveals {M}ajor {D}ifferences
                      in the {P}lasma {M}etabolome between {C}olorectal {C}ancer
                      and {C}olorectal {A}denomas.},
      journal      = {Metabolites},
      volume       = {11},
      number       = {2},
      issn         = {2218-1989},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {DKFZ-2021-00579},
      pages        = {119},
      year         = {2021},
      abstract     = {Sporadic colorectal cancer is characterized by a multistep
                      progression from normal epithelium to precancerous low-risk
                      and high-risk adenomas to invasive cancer. Yet, the
                      underlying molecular mechanisms of colorectal carcinogenesis
                      are not completely understood. Within the 'Metabolomic
                      profiles throughout the continuum of colorectal cancer'
                      (MetaboCCC) consortium we analyzed data generated by
                      untargeted, mass spectrometry-based metabolomics using
                      plasma from 88 colorectal cancer patients, 200 patients with
                      high-risk adenomas and 200 patients with low-risk adenomas
                      recruited within the 'Colorectal Cancer Study of Austria'
                      (CORSA). Univariate logistic regression models comparing
                      colorectal cancer to adenomas resulted in 442 statistically
                      significant molecular features. Metabolites discriminating
                      colorectal cancer patients from those with adenomas in our
                      dataset included acylcarnitines, caffeine, amino acids,
                      glycerophospholipids, fatty acids, bilirubin, bile acids and
                      bacterial metabolites of tryptophan. The data obtained
                      discovers metabolite profiles reflecting metabolic
                      differences between colorectal cancer and colorectal
                      adenomas and delineates a potentially underlying biological
                      interpretation.},
      keywords     = {adenoma (Other) / colorectal cancer (Other) / metabolite
                      profiling (Other) / untargeted metabolomics (Other)},
      cin          = {C120},
      ddc          = {540},
      cid          = {I:(DE-He78)C120-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33669644},
      doi          = {10.3390/metabo11020119},
      url          = {https://inrepo02.dkfz.de/record/167835},
}