Plasma metabolites associated with colorectal cancer: A discovery-replication strategy

Anne J.M.R. Geijsen, Stefanie Brezina, Pekka Keski-Rahkonen, Andreas Baierl, Thomas Bachleitner-Hofmann, Michael M. Bergmann, Juergen Boehm, Hermann Brenner, Jenny Chang-Claude, Fränzel J.B. van Duijnhoven, Biljana Gigic, Tanja Gumpenberger, Philipp Hofer, Michael Hoffmeister, Andreana N. Holowatyj, Judith Karner-Hanusch, Dieuwertje E. Kok, Gernot Leeb, Arve Ulvik, Nivonirina RobinotJennifer Ose, Anton Stift, Petra Schrotz-King, Alexis B. Ulrich, Per Magne Ueland, Ellen Kampman, Augustin Scalbert, Nina Habermann, Andrea Gsur*, Cornelia M. Ulrich

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)


Colorectal cancer is known to arise from multiple tumorigenic pathways; however, the underlying mechanisms remain not completely understood. Metabolomics is becoming an increasingly popular tool in assessing biological processes. Previous metabolomics research focusing on colorectal cancer is limited by sample size and did not replicate findings in independent study populations to verify robustness of reported findings. Here, we performed a ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) screening on EDTA plasma from 268 colorectal cancer patients and 353 controls using independent discovery and replication sets from two European cohorts (ColoCare Study: n = 180 patients/n = 153 controls; the Colorectal Cancer Study of Austria (CORSA) n = 88 patients/n = 200 controls), aiming to identify circulating plasma metabolites associated with colorectal cancer and to improve knowledge regarding colorectal cancer etiology. Multiple logistic regression models were used to test the association between disease state and metabolic features. Statistically significant associated features in the discovery set were taken forward and tested in the replication set to assure robustness of our findings. All models were adjusted for sex, age, BMI and smoking status and corrected for multiple testing using False Discovery Rate. Demographic and clinical data were abstracted from questionnaires and medical records.

Original languageEnglish
Pages (from-to)1221-1231
JournalInternational Journal of Cancer
Issue number5
Early online date21 Jan 2019
Publication statusPublished - 1 Sep 2019


  • colorectal cancer
  • discovery-replication approach
  • metabolomics

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