Untargeted lipidomic features associated with colorectal cancer in a prospective cohort

Abstract Background Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that l...

Full description

Bibliographic Details
Main Authors: Kelsi Perttula, Courtney Schiffman, William M B Edmands, Lauren Petrick, Hasmik Grigoryan, Xiaoming Cai, Marc J Gunter, Alessio Naccarati, Silvia Polidoro, Sandrine Dudoit, Paolo Vineis, Stephen M Rappaport
Format: Article
Language:English
Published: BMC 2018-10-01
Series:BMC Cancer
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12885-018-4894-4
id doaj-e1e04efce6f54d61ad6d3ec1eaa5444f
record_format Article
spelling doaj-e1e04efce6f54d61ad6d3ec1eaa5444f2020-11-25T00:43:58ZengBMCBMC Cancer1471-24072018-10-0118111010.1186/s12885-018-4894-4Untargeted lipidomic features associated with colorectal cancer in a prospective cohortKelsi Perttula0Courtney Schiffman1William M B Edmands2Lauren Petrick3Hasmik Grigoryan4Xiaoming Cai5Marc J Gunter6Alessio Naccarati7Silvia Polidoro8Sandrine Dudoit9Paolo Vineis10Stephen M Rappaport11School of Public Health, University of CaliforniaSchool of Public Health, University of CaliforniaSchool of Public Health, University of CaliforniaSchool of Public Health, University of CaliforniaSchool of Public Health, University of CaliforniaSchool of Public Health, University of CaliforniaInternational Agency for Research on CancerItalian Institute for Genomic Medicine (IIGM)Italian Institute for Genomic Medicine (IIGM)School of Public Health, University of CaliforniaItalian Institute for Genomic Medicine (IIGM)School of Public Health, University of CaliforniaAbstract Background Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC. Methods Using an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy). Samples were analyzed by liquid chromatography-high-resolution mass spectrometry (LC-MS), resulting in 8690 features for statistical analysis. Results Rather than the usual multiple-hypothesis-testing approach, we based variable selection on an ensemble of regression methods, which found nine features to be associated with case-control status. We then regressed each selected feature on time-to-diagnosis to determine whether the feature was likely to be either a potentially causal biomarker or a reactive product of disease progression (reverse causality). Conclusions Of the nine selected LC-MS features, four appear to be involved in CRC etiology and merit further investigation in prospective studies of CRC. Four other features appear to be related to progression of the disease (reverse causality), and may represent biomarkers of value for early detection of CRC.http://link.springer.com/article/10.1186/s12885-018-4894-4Colorectal cancerLipidomicsMetabolomicsEPICUntargetedBiomarkers
collection DOAJ
language English
format Article
sources DOAJ
author Kelsi Perttula
Courtney Schiffman
William M B Edmands
Lauren Petrick
Hasmik Grigoryan
Xiaoming Cai
Marc J Gunter
Alessio Naccarati
Silvia Polidoro
Sandrine Dudoit
Paolo Vineis
Stephen M Rappaport
spellingShingle Kelsi Perttula
Courtney Schiffman
William M B Edmands
Lauren Petrick
Hasmik Grigoryan
Xiaoming Cai
Marc J Gunter
Alessio Naccarati
Silvia Polidoro
Sandrine Dudoit
Paolo Vineis
Stephen M Rappaport
Untargeted lipidomic features associated with colorectal cancer in a prospective cohort
BMC Cancer
Colorectal cancer
Lipidomics
Metabolomics
EPIC
Untargeted
Biomarkers
author_facet Kelsi Perttula
Courtney Schiffman
William M B Edmands
Lauren Petrick
Hasmik Grigoryan
Xiaoming Cai
Marc J Gunter
Alessio Naccarati
Silvia Polidoro
Sandrine Dudoit
Paolo Vineis
Stephen M Rappaport
author_sort Kelsi Perttula
title Untargeted lipidomic features associated with colorectal cancer in a prospective cohort
title_short Untargeted lipidomic features associated with colorectal cancer in a prospective cohort
title_full Untargeted lipidomic features associated with colorectal cancer in a prospective cohort
title_fullStr Untargeted lipidomic features associated with colorectal cancer in a prospective cohort
title_full_unstemmed Untargeted lipidomic features associated with colorectal cancer in a prospective cohort
title_sort untargeted lipidomic features associated with colorectal cancer in a prospective cohort
publisher BMC
series BMC Cancer
issn 1471-2407
publishDate 2018-10-01
description Abstract Background Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC. Methods Using an untargeted design, we measured lipophilic metabolites in prediagnostic serum from 66 CRC patients and 66 matched controls from the European Prospective Investigation into Cancer and Nutrition (Turin, Italy). Samples were analyzed by liquid chromatography-high-resolution mass spectrometry (LC-MS), resulting in 8690 features for statistical analysis. Results Rather than the usual multiple-hypothesis-testing approach, we based variable selection on an ensemble of regression methods, which found nine features to be associated with case-control status. We then regressed each selected feature on time-to-diagnosis to determine whether the feature was likely to be either a potentially causal biomarker or a reactive product of disease progression (reverse causality). Conclusions Of the nine selected LC-MS features, four appear to be involved in CRC etiology and merit further investigation in prospective studies of CRC. Four other features appear to be related to progression of the disease (reverse causality), and may represent biomarkers of value for early detection of CRC.
topic Colorectal cancer
Lipidomics
Metabolomics
EPIC
Untargeted
Biomarkers
url http://link.springer.com/article/10.1186/s12885-018-4894-4
work_keys_str_mv AT kelsiperttula untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT courtneyschiffman untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT williammbedmands untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT laurenpetrick untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT hasmikgrigoryan untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT xiaomingcai untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT marcjgunter untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT alessionaccarati untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT silviapolidoro untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT sandrinedudoit untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT paolovineis untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
AT stephenmrappaport untargetedlipidomicfeaturesassociatedwithcolorectalcancerinaprospectivecohort
_version_ 1725277330888196096