Untargeted plasma metabolomics and risk of colorectal cancer-an analysis nested within a large-scale prospective cohort

被引:9
作者
Vidman, Linda [1 ]
Zheng, Rui [2 ]
Boden, Stina [1 ,3 ]
Ribbenstedt, Anton [4 ,5 ]
Gunter, Marc J. [6 ,7 ]
Palmqvist, Richard [8 ]
Harlid, Sophia [1 ]
Brunius, Carl [4 ,5 ]
Van Guelpen, Bethany [1 ,9 ]
机构
[1] Umea Univ, Dept Radiat Sci, Oncol, Umea, Sweden
[2] Uppsala Univ, Dept Surg Sci, Med Epidemiol, Uppsala, Sweden
[3] Umea Univ, Dept Clin Sci, Pediat, Umea, Sweden
[4] Chalmers Univ Technol, Dept Biol & Biol Engn, Gothenburg, Sweden
[5] Chalmers Univ Technol, Chalmers Mass Spectrometry Infrastruct, Gothenburg, Sweden
[6] WHO, Nutr & Metab Branch, Int Agcy Res Canc, Lyon, France
[7] Imperial Coll London, Sch Publ Hlth, Dept Epidemiol & Biostat, London, England
[8] Umea Univ, Dept Med Biosci, Pathol, Umea, Sweden
[9] Umea Univ, Wallenberg Ctr Mol Med, Umea, Sweden
基金
瑞典研究理事会;
关键词
Untargeted metabolomics; Colorectal cancer; Early detection; NORTHERN SWEDEN; METABOLITES; MONICA;
D O I
10.1186/s40170-023-00319-x
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
BackgroundColorectal cancer (CRC) is a leading cause of cancer-related death worldwide, but if discovered at an early stage, the survival rate is high. The aim of this study was to identify novel markers predictive of future CRC risk using untargeted metabolomics.MethodsThis study included prospectively collected plasma samples from 902 CRC cases and 902 matched cancer-free control participants from the population-based Northern Sweden Health and Disease Study (NSHDS), which were obtained up to 26 years prior to CRC diagnosis. Using reverse-phase liquid chromatography-mass spectrometry (LC-MS), data comprising 5015 metabolic features were obtained. Conditional logistic regression was applied to identify potentially important metabolic features associated with CRC risk. In addition, we investigated if previously reported metabolite biomarkers of CRC risk could be validated in this study population.ResultsIn the univariable analysis, seven metabolic features were associated with CRC risk (using a false discovery rate cutoff of 0.25). Two of these could be annotated, one as pyroglutamic acid (odds ratio per one standard deviation increase = 0.79, 95% confidence interval, 0.70-0.89) and another as hydroxytigecycline (odds ratio per one standard deviation increase = 0.77, 95% confidence interval, 0.67-0.89). Associations with CRC risk were also found for six previously reported metabolic biomarkers of prevalent and/or incident CRC: sebacic acid (inverse association) and L-tryptophan, 3-hydroxybutyric acid, 9,12,13-TriHOME, valine, and 13-OxoODE (positive associations).ConclusionsThese findings suggest that although the circulating metabolome may provide new etiological insights into the underlying causes of CRC development, its potential application for the identification of individuals at higher risk of developing CRC is limited.
引用
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页数:13
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共 47 条
  • [31] Untargeted lipidomic features associated with colorectal cancer in a prospective cohort
    Perttula, Kelsi
    Schiffman, Courtney
    Edmands, William M. B.
    Petrick, Lauren
    Grigoryan, Hasmik
    Cai, Xiaoming
    Gunter, Marc J.
    Naccarati, Alessio
    Polidoro, Silvia
    Dudoit, Sandrine
    Vineis, Paolo
    Rappaport, Stephen M.
    [J]. BMC CANCER, 2018, 18
  • [32] Evaluating Ultra-long-Chain Fatty Acids as Biomarkers of Colorectal Cancer Risk
    Perttula, Kelsi
    Edmands, William M. B.
    Grigoryan, Hasmik
    Cai, Xiaoming
    Iavarone, Anthony T.
    Gunter, Marc J.
    Naccarati, Alessio
    Polidoro, Silvia
    Hubbard, Alan
    Vineis, Paolo
    Rappaport, Stephen M.
    [J]. CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION, 2016, 25 (08) : 1216 - 1223
  • [33] Human plasma stability during handling and storage: impact on NMR metabolomics
    Pinto, Joana
    Domingues, M. Rosario M.
    Galhano, Eulalia
    Pita, Cristina
    Almeida, Maria do Ceu
    Carreira, Isabel M.
    Gil, Ana M.
    [J]. ANALYST, 2014, 139 (05) : 1168 - 1177
  • [34] Tryptophan Catabolism in Cancer: Beyond IDO and Tryptophan Depletion
    Platten, Michael
    Wick, Wolfgang
    Van den Eynde, Benoit J.
    [J]. CANCER RESEARCH, 2012, 72 (21) : 5435 - 5440
  • [35] R Core Team, 2017, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna
  • [36] MetFrag relaunched: incorporating strategies beyond in silico fragmentation
    Ruttkies, Christoph
    Schymanski, Emma L.
    Wolf, Sebastian
    Hollender, Juliane
    Neumann, Steffen
    [J]. JOURNAL OF CHEMINFORMATICS, 2016, 8
  • [37] Antibiotic exposure and the risk of colorectal adenoma and carcinoma: a systematic review and meta-analysis of observational studies
    Sanyaolu, L. N.
    Oakley, N. J.
    Nurmatov, U.
    Dolwani, S.
    Ahmed, H.
    [J]. COLORECTAL DISEASE, 2020, 22 (08) : 858 - 870
  • [38] Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence
    Schymanski, Emma L.
    Jeon, Junho
    Gulde, Rebekka
    Fenner, Kathrin
    Ruff, Matthias
    Singer, Heinz P.
    Hollender, Juliane
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2014, 48 (04) : 2097 - 2098
  • [39] Variable selection and validation in multivariate modelling
    Shi, Lin
    Westerhuis, Johan A.
    Rosen, Johan
    Landberg, Rikard
    Brunius, Carl
    [J]. BIOINFORMATICS, 2019, 35 (06) : 972 - 980
  • [40] Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families
    Shiao, S. Pamela K.
    Grayson, James
    Yu, Chong Ho
    [J]. JOURNAL OF PERSONALIZED MEDICINE, 2018, 8 (03):