Journal Article

DKFZ-2019-01796

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Plasma metabolites associated with colorectal cancer: A discovery-replication strategy.

Geijsen, A. J. M. R. ; Brezina, S. ; Keski-Rahkonen, P. ; Baierl, A. ; Bachleitner-Hofmann, T. ; Bergmann, M. M. ; Boehm, J. ; Brenner, H.DKFZ* ; Chang-Claude, J.DKFZ* ; van Duijnhoven, F. J. B. ; Gigic, B. ; Gumpenberger, T. ; Hofer, P. ; Hoffmeister, M.DKFZ* ; Holowatyj, A. N. ; Karner-Hanusch, J. ; Kok, D. E. ; Leeb, G. ; Ulvik, A. ; Robinot, N. ; Ose, J. ; Stift, A. ; Schrotz-King, P.DKFZ* ; Ulrich, A. B. ; Ueland, P. M. ; Kampman, E. ; Scalbert, A. ; Habermann, N.DKFZ* ; Gsur, A. ; Ulrich, C. M.

2019
Wiley-Liss Bognor Regis

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Please use a persistent id in citations: doi:10.1002/ijc.32146

Abstract: 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.

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Contributing Institute(s):

1. Präventive Onkologie (C120)
2. Klinische Epidemiologie und Alternsforschung (C070)
3. DKTK Heidelberg (L101)
4. Epidemiologie von Krebserkrankungen (C020)

Research Program(s):

1. 313 - Cancer risk factors and prevention (POF3-313) (POF3-313)

Appears in the scientific report 2019

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Database coverage:
; BIOSIS Previews ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; Nationallizenz ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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