Evaluating ultra-long-chain fatty acids as biomarkers of colorectal cancer risk

TitleEvaluating ultra-long-chain fatty acids as biomarkers of colorectal cancer risk
Publication TypeJournal Article
Year of Publication2016
AuthorsPettula K, Edmands WMB, Grigoryan H, Cai X, Iavarone AT, Gunter MJ, Naccarati A, Polidoro S, Hubbard A, Vineis P, Rappaport SM
Date Published2016 AUG


Cross-sectional studies reported a novel set of hydroxylated ultra-long-chain fatty acids (ULCFA) that were present at significantly lower levels in colorectal cancer cases than controls. Follow-up studies suggested that these molecules were potential biomarkers of protective exposure for colorectal cancer. To test the hypothesis that ULCFAs reflect causal pathways, we measured their levels in prediagnostic serum from incident colorectal cancer cases and controls.


Serum from 95 colorectal cancer patients and 95 matched controls was obtained from the Italian arm of the European Prospective Investigation into Cancer and Nutrition cohort and analyzed by liquid chromatography-high-resolution mass spectrometry. Levels of 8 ULCFAs were compared between cases and controls with paired t tests and a linear model that used time to diagnosis (TTD) to determine whether case-control differences were influenced by disease progression.


Although paired t tests detected significantly lower levels of four ULCFAs in colorectal cancer cases, confirming earlier reports, the case-control differences diminished significantly with increasing TTD (7 days-14 years).


Levels of several ULCFAs were lower in incident colorectal cancer cases than controls. However, because case-control differences decreased with increasing TTD, we conclude that these molecules were likely consumed by processes related to cancer progression rather than causal pathways.


ULCFA levels are unlikely to represent exposures that protect individuals from colorectal cancer. Future research should focus on the diagnostic potential and origins of these molecules. Our use of TTD as a covariate in a linear model provides an efficient method for distinguishing causal and reactive biomarkers in biospecimens from prospective cohorts. Cancer Epidemiol Biomarkers Prev; 25(8); 1216-23. ©2016 AACR.

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