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Quantification of mutant SPOP proteins in prostate cancer using mass spectrometry-based targeted proteomics

Overview of attention for article published in Journal of Translational Medicine, August 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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1 news outlet
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2 X users
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1 patent

Citations

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28 Mendeley
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Title
Quantification of mutant SPOP proteins in prostate cancer using mass spectrometry-based targeted proteomics
Published in
Journal of Translational Medicine, August 2017
DOI 10.1186/s12967-017-1276-7
Pubmed ID
Authors

Hui Wang, Christopher E. Barbieri, Jintang He, Yuqian Gao, Tujin Shi, Chaochao Wu, Athena A. Schepmoes, Thomas L. Fillmore, Sung-Suk Chae, Dennis Huang, Juan Miguel Mosquera, Wei-Jun Qian, Richard D. Smith, Sudhir Srivastava, Jacob Kagan, David G. Camp, Karin D. Rodland, Mark A. Rubin, Tao Liu

Abstract

Speckle-type POZ protein (SPOP) is an E3 ubiquitin ligase adaptor protein that functions as a potential tumor suppressor, and SPOP mutations have been identified in ~10% of human prostate cancers. However, it remains unclear if mutant SPOP proteins can be utilized as biomarkers for early detection, diagnosis, prognosis or targeted therapy of prostate cancer. Moreover, the SPOP mutation sites are distributed in a relatively short region with multiple lysine residues, posing significant challenges for bottom-up proteomics analysis of the SPOP mutations. To address this issue, PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing)-SRM (selected reaction monitoring) mass spectrometry assays have been developed for quantifying wild-type SPOP protein and 11 prostate cancer-derived SPOP mutations. Despite inherent limitations due to amino acid sequence constraints, all the PRISM-SRM assays developed using Arg-C digestion showed a linear dynamic range of at least two orders of magnitude, with limits of quantification ranged from 0.1 to 1 fmol/μg of total protein in the cell lysate. Applying these SRM assays to analyze HEK293T cells with and without expression of the three most frequent SPOP mutations in prostate cancer (Y87N, F102C or F133V) led to confident detection of all three SPOP mutations in corresponding positive cell lines but not in the negative cell lines. Expression of the F133V mutation and wild-type SPOP was at much lower levels compared to that of F102C and Y87N mutations; however, at present, it is unknown if this also affects the biological activity of the SPOP protein. In summary, PRISM-SRM enables multiplexed, isoform-specific detection of mutant SPOP proteins in cell lysates, providing significant potential in biomarker development for prostate cancer.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 25%
Professor > Associate Professor 4 14%
Student > Bachelor 3 11%
Researcher 2 7%
Professor 1 4%
Other 1 4%
Unknown 10 36%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 36%
Medicine and Dentistry 3 11%
Agricultural and Biological Sciences 2 7%
Chemistry 2 7%
Computer Science 1 4%
Other 2 7%
Unknown 8 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 02 November 2023.
All research outputs
#2,556,942
of 25,008,338 outputs
Outputs from Journal of Translational Medicine
#438
of 4,537 outputs
Outputs of similar age
#46,657
of 321,733 outputs
Outputs of similar age from Journal of Translational Medicine
#6
of 47 outputs
Altmetric has tracked 25,008,338 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,537 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one has done particularly well, scoring higher than 90% 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 321,733 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 85% of its contemporaries.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.