Prediagnostic circulating metabolites in female breast cancer cases with low and high mammographic breast density View Full Text


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Article Info

DATE

2021-06-22

AUTHORS

Benedetta Bendinelli, Alessia Vignoli, Domenico Palli, Melania Assedi, Daniela Ambrogetti, Claudio Luchinat, Saverio Caini, Calogero Saieva, Paola Turano, Giovanna Masala

ABSTRACT

Mammographic breast density (MBD) is a strong independent risk factor for breast cancer (BC). We designed a matched case-case study in the EPIC Florence cohort, to evaluate possible associations between the pre-diagnostic metabolomic profile and the risk of BC in high- versus low-MBD women who developed BC during the follow-up. A case-case design with 100 low-MBD (MBD ≤ 25%) and 100 high-MDB BC cases (MBD > 50%) was performed. Matching variables included age, year and type of mammographic examination. 1H NMR metabolomic spectra were available for 87 complete case-case sets. The conditional logistic analyses showed an inverse association between serum levels of alanine, leucine, tyrosine, valine, lactic acid, pyruvic acid, triglycerides lipid main fraction and 11 VLDL lipid subfractions and high-MBD cases. Acetic acid was directly associated with high-MBD cases. In models adjusted for confounding variables, tyrosine remained inversely associated with high-MBD cases while 3 VLDL subfractions of free cholesterol emerged as directly associated with high-MBD cases. A pathway analysis showed that the "phenylalanine, tyrosine and tryptophan pathway" emerged and persisted after applying the FDR procedure. The supervised OPLS-DA analysis revealed a slight but significant separation between high- and low-MBD cases. This case-case study suggested a possible role for pre-diagnostic levels of tyrosine in modulating the risk of BC in high- versus low-MBD women. Moreover, some differences emerged in the pre-diagnostic concentration of other metabolites as well in the metabolomic fingerprints among the two groups of patients. More... »

PAGES

13025

References to SciGraph publications

  • 2013-11-04. Mammographic density and risk of breast cancer by age and tumor characteristics in BREAST CANCER RESEARCH
  • 2016-12-05. Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease in SCIENTIFIC REPORTS
  • 2011-11-01. Mammographic density and breast cancer risk: current understanding and future prospects in BREAST CANCER RESEARCH
  • 2005-10-20. Biomarkers in Cancer Staging, Prognosis and Treatment Selection in NATURE REVIEWS CANCER
  • 2020-01-18. Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data in COMPUTATIONAL METHODS AND DATA ANALYSIS FOR METABOLOMICS
  • 2018-04-05. Breast density and breast cancer-specific survival by detection mode in BMC CANCER
  • 2019-09-24. Prospective analysis of circulating metabolites and breast cancer in EPIC in BMC MEDICINE
  • 2011-12-06. Clinical and epidemiological issues in mammographic density in NATURE REVIEWS CLINICAL ONCOLOGY
  • 2016-11-08. A Comparative Metabolomics Approach Reveals Early Biomarkers for Metabolic Response to Acute Myocardial Infarction in SCIENTIFIC REPORTS
  • 2008-10-22. Metabonomics in NATURE
  • 2017-03-13. Plasma-free amino acid profiles are predictors of cancer and diabetes development in NUTRITION & DIABETES
  • 2019-08-29. Metabolomic analysis of serum may refine 21-gene expression assay risk recurrence stratification in NPJ BREAST CANCER
  • 2013-09-04. Association between mammographic density and basal-like and luminal A breast cancer subtypes in BREAST CANCER RESEARCH
  • 1989-06. Ki67 immunostaining in primary breast cancer: pathological and clinical associations. in BRITISH JOURNAL OF CANCER
  • 2002-11-25. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics in NATURE MEDICINE
  • 2019-06-08. A review of the influence of mammographic density on breast cancer clinical and pathological phenotype in BREAST CANCER RESEARCH AND TREATMENT
  • 2019-03-01. Updating the role of obesity and cholesterol in breast cancer in BREAST CANCER RESEARCH
  • 2017-05-06. Mammographic breast density and breast cancer risk in a Mediterranean population: a nested case–control study in the EPIC Florence cohort in BREAST CANCER RESEARCH AND TREATMENT
  • 2017-10-17. Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer in BIOMARKER RESEARCH
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-021-92508-1

    DOI

    http://dx.doi.org/10.1038/s41598-021-92508-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1139030919

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34158597


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