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Plasma metabolites associated with colorectal cancer: A discovery‐replication strategy
Author(s) -
Geijsen Anne J.M.R.,
Brezina Stefanie,
KeskiRahkonen Pekka,
Baierl Andreas,
BachleitnerHofmann Thomas,
Bergmann Michael M.,
Boehm Juergen,
Brenner Hermann,
ChangClaude Jenny,
Duijnhoven Fränzel J.B.,
Gigic Biljana,
Gumpenberger Tanja,
Hofer Philipp,
Hoffmeister Michael,
Holowatyj Andrea.,
KarnerHanusch Judith,
Kok Dieuwertje E.,
Leeb Gernot,
Ulvik Arve,
Robinot Nivonirina,
Ose Jennifer,
Stift Anton,
SchrotzKing Petra,
Ulrich Alexis B.,
Ueland Per Magne,
Kampman Ellen,
Scalbert Augustin,
Habermann Nina,
Gsur Andrea,
Ulrich Cornelia M.
Publication year - 2019
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.32146
Subject(s) - colorectal cancer , metabolomics , medicine , logistic regression , oncology , disease , bioinformatics , cancer , computational biology , biology
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.