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Metabolomics of oncogene-specific metabolic reprogramming during breast cancer

Overview of attention for article published in Cancer & Metabolism, April 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#44 of 206)
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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1 blog
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5 X users
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1 Facebook page

Citations

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37 Dimensions

Readers on

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53 Mendeley
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Title
Metabolomics of oncogene-specific metabolic reprogramming during breast cancer
Published in
Cancer & Metabolism, April 2018
DOI 10.1186/s40170-018-0175-6
Pubmed ID
Authors

Chen Dai, Jennifer Arceo, James Arnold, Arun Sreekumar, Norman J. Dovichi, Jun Li, Laurie E. Littlepage

Abstract

The complex yet interrelated connections between cancer metabolism and oncogenic driver genes are relatively unexplored but have the potential to identify novel biomarkers and drug targets with prognostic and therapeutic value. The goal of this study was to identify global metabolic profiles of breast tumors isolated from multiple transgenic mouse models and to identify unique metabolic signatures driven by these oncogenes. Using mass spectrometry (GC-MS, LC-MS/MS, and capillary zone electrophoresis (CZE)-MS platforms), we quantified and compared the levels of 374 metabolites in breast tissue from normal and transgenic mouse breast cancer models overexpressing a panel of oncogenes (PyMT, PyMT-DB, Wnt1, Neu, and C3-TAg). We also compared the mouse metabolomics data to published human metabolomics data already linked to clinical data. Through analysis of our metabolomics data, we identified metabolic differences between normal and tumor breast tissues as well as metabolic differences unique to each initiating oncogene. We also quantified the metabolic profiles of the mammary fat pad versus mammary epithelium by CZE-MS/MS. However, the differences between the tissues did not account for the majority of the metabolic differences between the normal mammary gland and breast tumor tissues. Therefore, the differences between the cohorts were unlikely due to cellular heterogeneity. Of the mouse models used in this study, C3-TAg was the only cohort with a tumor metabolic signature composed of ten metabolites that had significant prognostic value in breast cancer patients. Gene expression analysis identified candidate genes that may contribute to the metabolic reprogramming. This study identifies oncogene-induced metabolic reprogramming within mouse breast tumors and compares the results to that of human breast tumors, providing a unique look at the relationship between and clinical value of oncogene initiation and metabolism during breast cancer.

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The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 26%
Student > Bachelor 7 13%
Researcher 5 9%
Student > Master 3 6%
Student > Postgraduate 2 4%
Other 6 11%
Unknown 16 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 26%
Agricultural and Biological Sciences 7 13%
Chemistry 5 9%
Engineering 3 6%
Immunology and Microbiology 3 6%
Other 3 6%
Unknown 18 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 April 2018.
All research outputs
#3,684,592
of 23,041,514 outputs
Outputs from Cancer & Metabolism
#44
of 206 outputs
Outputs of similar age
#73,330
of 329,118 outputs
Outputs of similar age from Cancer & Metabolism
#1
of 5 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 206 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,118 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them