|Title||Plasma metabolites associated with colorectal cancer: A discovery-replication strategy|
|Author(s)||Geijsen, Anne J.M.R.; Brezina, Stefanie; Keski-Rahkonen, Pekka; Baierl, Andreas; Bachleitner-Hofmann, Thomas; Bergmann, Michael M.; Boehm, Juergen; Brenner, Hermann; Chang-Claude, Jenny; Duijnhoven, Fränzel J.B. van; Gigic, Biljana; Gumpenberger, Tanja; Hofer, Philipp; Hoffmeister, Michael; Holowatyj, Andreana N.; Karner-Hanusch, Judith; Kok, Dieuwertje E.; Leeb, Gernot; Ulvik, Arve; Robinot, Nivonirina; Ose, Jennifer; Stift, Anton; Schrotz-King, Petra; Ulrich, Alexis B.; Ueland, Per Magne; Kampman, Ellen; Scalbert, Augustin; Habermann, Nina; Gsur, Andrea; Ulrich, Cornelia M.|
|Source||International Journal of Cancer 145 (2019)5. - ISSN 0020-7136 - p. 1221 - 1231.|
Nutrition and Disease
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||colorectal cancer - discovery-replication approach - metabolomics - UHPLC-QTOF-MS|
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.