↓ Skip to main content

PLOS

LC/MS-Based Quantitative Proteomic Analysis of Paraffin-Embedded Archival Melanomas Reveals Potential Proteomic Biomarkers Associated with Metastasis

Overview of attention for article published in PLOS ONE, February 2009
Altmetric Badge

Mentioned by

patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
82 Mendeley
citeulike
1 CiteULike
connotea
1 Connotea
Title
LC/MS-Based Quantitative Proteomic Analysis of Paraffin-Embedded Archival Melanomas Reveals Potential Proteomic Biomarkers Associated with Metastasis
Published in
PLOS ONE, February 2009
DOI 10.1371/journal.pone.0004430
Pubmed ID
Authors

Sharon K. Huang, Marlene M. Darfler, Michael B. Nicholl, Jinsam You, Kerry G. Bemis, Tony J. Tegeler, Mu Wang, Jean-Pierre Wery, Kelly K. Chong, Linhda Nguyen, Richard A. Scolyer, Dave S. B. Hoon

Abstract

Melanoma metastasis status is highly associated with the overall survival of patients; yet, little is known about proteomic changes during melanoma tumor progression. To better understand the changes in protein expression involved in melanoma progression and metastasis, and to identify potential biomarkers, we conducted a global quantitative proteomic analysis on archival metastatic and primary melanomas.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Spain 1 1%
United States 1 1%
Unknown 79 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 24%
Student > Ph. D. Student 19 23%
Professor > Associate Professor 7 9%
Student > Bachelor 6 7%
Student > Doctoral Student 5 6%
Other 12 15%
Unknown 13 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 32%
Medicine and Dentistry 14 17%
Biochemistry, Genetics and Molecular Biology 6 7%
Chemistry 5 6%
Neuroscience 2 2%
Other 12 15%
Unknown 17 21%