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Identification of potential serum biomarkers for breast cancer using a functional proteomics technology

Overview of attention for article published in Biomarker Research, March 2017
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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 (#11 of 114)
  • High Attention Score compared to outputs of the same age (82nd percentile)

Mentioned by

news
1 news outlet
twitter
2 tweeters

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
17 Mendeley
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Title
Identification of potential serum biomarkers for breast cancer using a functional proteomics technology
Published in
Biomarker Research, March 2017
DOI 10.1186/s40364-017-0092-9
Pubmed ID
Authors

David L. Wang, Chuanguang Xiao, Guofeng Fu, Xing Wang, Liang Li

Abstract

Cancer is a genetic disease; its development and metastasis depend on the function of many proteins. Human serum contains thousands of proteins; it is a window for the homeostasis of individual's health. Many of the proteins found in the human serum could be potential biomarkers for cancer early detection and drug efficacy evaluation. In this study, a functional proteomics technology was used to systematically monitor metabolic enzyme and protease activities from resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP. All the experiments were repeated at least twice to ensure the validity of the findings. For the first time, significant differences were found between breast cancer patient serum and normal serum in two families of enzymes known to be involved in cancer development and metastasis: metabolic enzymes and proteases. Multiple enzyme species were identified in the serum assayed directly or after enrichment. Both qualitative and quantitative differences in the metabolic enzyme and protease activity were detected between breast cancer patient and control group, providing excellent biomarker candidates for breast cancer diagnosis and drug development. This study identified several potential functional protein biomarkers from breast cancer patient serum. It also demonstrated that the functional proteomics technology, PEP, can be applied to the analysis of any functional proteins in human serum which contains thousands of proteins. The study indicated that the functional domain of the human serum could be unlocked with the PEP technology, pointing to a novel alternative for the development of diagnosis biomarkers for breast cancer and other diseases.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 18%
Student > Master 2 12%
Student > Ph. D. Student 2 12%
Student > Bachelor 1 6%
Student > Doctoral Student 1 6%
Other 3 18%
Unknown 5 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 41%
Chemistry 2 12%
Nursing and Health Professions 1 6%
Medicine and Dentistry 1 6%
Unknown 6 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 11 July 2019.
All research outputs
#1,546,710
of 14,104,163 outputs
Outputs from Biomarker Research
#11
of 114 outputs
Outputs of similar age
#44,487
of 258,330 outputs
Outputs of similar age from Biomarker Research
#1
of 2 outputs
Altmetric has tracked 14,104,163 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 114 research outputs from this source. They receive a mean Attention Score of 3.2. 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 258,330 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 82% of its contemporaries.
We're also able to compare this research output to 2 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