Literature DB >> 21462378

13C high-resolution-magic angle spinning MRS reveals differences in glucose metabolism between two breast cancer xenograft models with different gene expression patterns.

Maria T Grinde1, Siver A Moestue, Eldrid Borgan, Øystein Risa, Olav Engebraaten, Ingrid S Gribbestad.   

Abstract

Tumor cells have increased glycolytic activity, and glucose is mainly used to form lactate and alanine, even when high concentrations of oxygen are present (Warburg effect). The purpose of the present study was to investigate glucose metabolism in two xenograft models representing basal-like and luminal-like breast cancer using (13) C high-resolution-magic angle spinning (HR-MAS) MRS and gene expression analysis. Tumor tissue was collected from two groups for each model: untreated mice (n=19) and a group of mice (n=16) that received an injection of [1-(13) C]-glucose 10 or 15 min before harvesting the tissue. (13) C HR-MAS MRS was performed on the tumor samples and differences in the glucose/alanine (Glc/Ala), glucose/lactate (Glc/Lac) and alanine/lactate (Ala/Lac) ratios between the models were studied. The expression of glycolytic genes was studied using tumor tissue from the same models. In the natural abundance MR spectra, a significantly lower Glc/Ala and Glc/Lac ratio (p<0.001) was observed in the luminal-like model compared with the basal-like model. In the labeled samples, the predominant glucose metabolites were lactate and alanine. Significantly lower Glc/Ala and Glc/Lac ratios were observed in the luminal-like model (p<0.05). Most genes contributing to glycolysis were expressed at higher levels in the luminal-like model (fdr<0.001). The lower Glc/Ala and Glc/Lac ratios and higher glycolytic gene expression observed in the luminal-like model indicates that the transformation of glucose to lactate and alanine occurred faster in this model than in the basal-like model, which has a growth rate several times faster than that of the luminal-like model. The results from the present study suggest that the tumor growth rate is not necessarily a determinant of glycolytic activity.
Copyright © 2011 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21462378     DOI: 10.1002/nbm.1683

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  10 in total

1.  Dynamic (18) F-FDG PET for Assessment of Tumor Physiology in Two Breast Carcinoma Xenografts.

Authors:  Alexandr Kristian; Line B Nilsen; Kathrine Røe; Mona-Elisabeth Revheim; Olav Engebråten; Gunhild M Mælandsmo; Ruth Holm; Eirik Malinen; Therese Seierstad
Journal:  Nucl Med Mol Imaging       Date:  2013-06-21

Review 2.  Cancer insights from magnetic resonance spectroscopy of cells and excised tumors.

Authors:  Marie-France Penet; Raj Kumar Sharma; Santosh Bharti; Noriko Mori; Dmitri Artemov; Zaver M Bhujwalla
Journal:  NMR Biomed       Date:  2022-03-09       Impact factor: 4.478

3.  Metabolomics Analysis of Hormone-Responsive and Triple-Negative Breast Cancer Cell Responses to Paclitaxel Identify Key Metabolic Differences.

Authors:  Delisha A Stewart; Jason H Winnike; Susan L McRitchie; Robert F Clark; Wimal W Pathmasiri; Susan J Sumner
Journal:  J Proteome Res       Date:  2016-08-03       Impact factor: 4.466

4.  Proteomic characterization of breast cancer xenografts identifies early and late bevacizumab-induced responses and predicts effective drug combinations.

Authors:  Evita M Lindholm; Marit Krohn; Sergio Iadevaia; Alexandr Kristian; Gordon B Mills; Gunhild M Mælandsmo; Olav Engebraaten
Journal:  Clin Cancer Res       Date:  2013-11-05       Impact factor: 12.531

5.  Impact of Freezing Delay Time on Tissue Samples for Metabolomic Studies.

Authors:  Tonje H Haukaas; Siver A Moestue; Riyas Vettukattil; Beathe Sitter; Santosh Lamichhane; Remedios Segura; Guro F Giskeødegård; Tone F Bathen
Journal:  Front Oncol       Date:  2016-01-28       Impact factor: 6.244

Review 6.  Metabolic Portraits of Breast Cancer by HR MAS MR Spectroscopy of Intact Tissue Samples.

Authors:  Tonje H Haukaas; Leslie R Euceda; Guro F Giskeødegård; Tone F Bathen
Journal:  Metabolites       Date:  2017-05-16

7.  Glutamine to proline conversion is associated with response to glutaminase inhibition in breast cancer.

Authors:  Maria T Grinde; Bylgja Hilmarsdottir; Hanna Maja Tunset; Ida Marie Henriksen; Jana Kim; Mads H Haugen; Morten Beck Rye; Gunhild M Mælandsmo; Siver A Moestue
Journal:  Breast Cancer Res       Date:  2019-05-14       Impact factor: 6.466

8.  Metabolic biomarkers for response to PI3K inhibition in basal-like breast cancer.

Authors:  Siver A Moestue; Cornelia G Dam; Saurabh S Gorad; Alexandr Kristian; Anna Bofin; Gunhild M Mælandsmo; Olav Engebråten; Ingrid S Gribbestad; Geir Bjørkøy
Journal:  Breast Cancer Res       Date:  2013-02-28       Impact factor: 6.466

9.  Metabolomics of Breast Cancer Using High-Resolution Magic Angle Spinning Magnetic Resonance Spectroscopy: Correlations with 18F-FDG Positron Emission Tomography-Computed Tomography, Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging MRI.

Authors:  Haesung Yoon; Dahye Yoon; Mijin Yun; Ji Soo Choi; Vivian Youngjean Park; Eun-Kyung Kim; Joon Jeong; Ja Seung Koo; Jung Hyun Yoon; Hee Jung Moon; Suhkmann Kim; Min Jung Kim
Journal:  PLoS One       Date:  2016-07-26       Impact factor: 3.240

10.  Hormone-Independent Mouse Mammary Adenocarcinomas with Different Metastatic Potential Exhibit Different Metabolic Signatures.

Authors:  Daniela Bispo; Victoria Fabris; Caroline A Lamb; Claudia Lanari; Luisa A Helguero; Ana M Gil
Journal:  Biomolecules       Date:  2020-08-27
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.