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@ARTICLE{Stepien:157334,
author = {M. Stepien and P. Keski-Rahkonen and A. Kiss and N. Robinot
and T. Duarte-Salles and N. Murphy and G. Perlemuter and V.
Viallon and A. Tjønneland and A. L. Rostgaard-Hansen and C.
C. Dahm and K. Overvad and M.-C. Boutron-Ruault and F. R.
Mancini and Y. Mahamat-Saleh and K. Aleksandrova and R.
Kaaks$^*$ and T. Kühn$^*$ and A. Trichopoulou and A.
Karakatsani and S. Panico and R. Tumino and D. Palli and G.
Tagliabue and A. Naccarati and R. C. H. Vermeulen and H. B.
Bueno-de-Mesquita and E. Weiderpass and G. Skeie and J.
Ramón Quirós and E. Ardanaz and O. Mokoroa and N. Sala and
M.-J. Sánchez and J. M. Huerta and A. Winkvist and S.
Harlid and B. Ohlsson and K. Sjöberg and J. A. Schmidt and
N. Wareham and K.-T. Khaw and P. Ferrari and J. A. Rothwell
and M. Gunter and E. Riboli and A. Scalbert and M. Jenab},
title = {{M}etabolic perturbations prior to hepatocellular carcinoma
diagnosis - {F}indings from a prospective observational
cohort study.},
journal = {International journal of cancer},
volume = {148},
number = {3},
issn = {1097-0215},
address = {Bognor Regis},
publisher = {Wiley-Liss},
reportid = {DKFZ-2020-01563},
pages = {609-625},
year = {2020},
note = {2021 Feb 1;148(3):609-625},
abstract = {Hepatocellular carcinoma (HCC) development entails changes
in liver metabolism. Current knowledge on metabolic
perturbations in HCC is derived mostly from case-control
designs, with sparse information from prospective cohorts.
Our objective was to apply comprehensive metabolite
profiling to detect metabolites whose serum concentrations
are associated with HCC development, using biological
samples from within the prospective EPIC cohort (>520 000
participants,), where we identified 129 HCC cases matched
1:1 to controls. We conducted high resolution untargeted
liquid chromatography-mass spectrometry based metabolomics
on serum samples collected at recruitment prior to cancer
diagnosis. Multivariable conditional logistic regression was
applied controlling for dietary habits, alcohol consumption,
smoking, body size, hepatitis infection and liver
dysfunction. Corrections for multiple comparisons were
applied. Of 9206 molecular features detected, 220
discriminated HCC cases from controls. Detailed feature
annotation revealed 92 metabolites associated with HCC risk;
14 of which were unambiguously identified using pure
reference standards. Positive HCC risk associations were
observed for N1-acetylspermidine, isatin,
p-hydroxyphenyllactic acid, tyrosine, sphingosine,
L,L-cyclo(leucylprolyl), glycochenodeoxycholic acid,
glycocholic acid, and 7-methylguanine. Inverse risk
associations were observed for retinol,
dehydroepiandrosterone sulfate, glycerophosphocholine,
γ-carboxyethyl hydroxychroman, and creatine. Discernible
differences for these metabolites were observed between
cases and controls up to 10 years prior to diagnosis. Our
observations highlight the diversity of metabolic
perturbations involved in HCC development and replicate
previous observations (metabolism of bile acids, amino
acids, phospholipids) made in Asian and Scandinavian
populations. These findings emphasize the role of metabolic
pathways associated with steroid metabolism and immunity and
specific dietary and environmental exposures in HCC
development. This article is protected by copyright. All
rights reserved.},
cin = {C020},
ddc = {610},
cid = {I:(DE-He78)C020-20160331},
pnm = {313 - Cancer risk factors and prevention (POF3-313)},
pid = {G:(DE-HGF)POF3-313},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:32734650},
doi = {10.1002/ijc.33236},
url = {https://inrepo02.dkfz.de/record/157334},
}