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Development and Validation of a Novel Plasma Protein Signature for Breast Cancer Diagnosis by Using Multiple Reaction Monitoring-based Mass Spectrometry.

Overview of attention for article published in Anticancer Research: International Journal of Cancer Research and Treatment, November 2015
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Title
Development and Validation of a Novel Plasma Protein Signature for Breast Cancer Diagnosis by Using Multiple Reaction Monitoring-based Mass Spectrometry.
Published in
Anticancer Research: International Journal of Cancer Research and Treatment, November 2015
Pubmed ID
Authors

Lee, Han-Byoel, Kang, Un-Beom, Moon, Hyeong-Gon, Lee, Jiwoo, Lee, Kyung-Min, Yi, Minju, Park, Yong Sun, Lee, Jong Won, Yu, Jong-Han, Choi, Seung Ho, Cho, Sang Heon, Lee, Cheolju, Han, Wonshik, Noh, Dong-Young

Abstract

We aimed to develop a plasma protein signature for breast cancer diagnosis by using multiple reaction monitoring (MRM)-based mass spectrometry. Based on our previous studies, we selected 124 proteins for MRM. Plasma samples from 80 patients with breast cancer and 80 healthy women were used to develop a plasma proteomic signature by an MRM approach. The proteomic signature was then validated in plasma samples from 100 patients with breast cancer and 100 healthy women. A total of 56 proteins were optimized for MRM. In the verification cohort, 11 proteins exhibited significantly differential expression in plasma from patients with breast cancer. Three proteins (neural cell adhesion molecule L1-like protein, apolipoprotein C-1 and carbonic anhydrase-1) with highest statistical significance which gave consistent results for patients of stage I and II breast cancer were selected and a 3-protein signature was developed using binary logistic regression analysis [area under the curve (AUC)=0.851, sensitivity=80.6%]. The 3-protein signature showed similar performance in an independent validation cohort with an AUC of 0.797 and sensitivity of 77.2% for detection of stage I and II breast cancer. We developed a distinct plasma protein signature for breast cancer diagnosis based on an MRM-based approach, and the clinical value of the 3-protein signature was validated in an independent cohort.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 27%
Researcher 3 20%
Student > Master 3 20%
Lecturer 1 7%
Student > Postgraduate 1 7%
Other 4 27%
Readers by discipline Count As %
Medicine and Dentistry 5 33%
Biochemistry, Genetics and Molecular Biology 4 27%
Agricultural and Biological Sciences 3 20%
Chemistry 2 13%
Physics and Astronomy 1 7%
Other 1 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 October 2015.
All research outputs
#5,544,770
of 6,500,718 outputs
Outputs from Anticancer Research: International Journal of Cancer Research and Treatment
#1,038
of 1,577 outputs
Outputs of similar age
#164,934
of 207,322 outputs
Outputs of similar age from Anticancer Research: International Journal of Cancer Research and Treatment
#52
of 91 outputs
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We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.