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Platelet protein biomarker panel for ovarian cancer diagnosis

Overview of attention for article published in Biomarker Research, January 2018
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Title
Platelet protein biomarker panel for ovarian cancer diagnosis
Published in
Biomarker Research, January 2018
DOI 10.1186/s40364-018-0118-y
Pubmed ID
Authors

Marta Lomnytska, Rui Pinto, Susanne Becker, Ulla Engström, Sonja Gustafsson, Christina Björklund, Markus Templin, Jan Bergstrand, Lei Xu, Jerker Widengren, Elisabeth Epstein, Bo Franzén, Gert Auer

Abstract

Platelets support cancer growth and spread making platelet proteins candidates in the search for biomarkers. Two-dimensional (2D) gel electrophoresis, Partial Least Squares Discriminant Analysis (PLS-DA), Western blot, DigiWest. PLS-DA of platelet protein expression in 2D gels suggested differences between the International Federation of Gynaecology and Obstetrics (FIGO) stages III-IV of ovarian cancer, compared to benign adnexal lesions with a sensitivity of 96% and a specificity of 88%. A PLS-DA-based model correctly predicted 7 out of 8 cases of FIGO stages I-II of ovarian cancer after verification by western blot. Receiver-operator curve (ROC) analysis indicated a sensitivity of 83% and specificity of 76% at cut-off >0.5 (area under the curve (AUC) = 0.831, p < 0.0001) for detecting these cases. Validation on an independent set of samples by DigiWest with PLS-DA differentiated benign adnexal lesions and ovarian cancer, FIGO stages III-IV, with a sensitivity of 70% and a specificity of 83%. We identified a group of platelet protein biomarker candidates that can quantify the differential expression between ovarian cancer cases as compared to benign adnexal lesions.

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Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 19%
Student > Bachelor 11 18%
Student > Master 7 11%
Student > Doctoral Student 6 10%
Researcher 5 8%
Other 7 11%
Unknown 14 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 27%
Medicine and Dentistry 11 18%
Agricultural and Biological Sciences 5 8%
Pharmacology, Toxicology and Pharmaceutical Science 4 6%
Nursing and Health Professions 2 3%
Other 6 10%
Unknown 17 27%