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...
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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 |
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