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Cell Stiffness Is a Biomarker of the Metastatic Potential of Ovarian Cancer Cells

Overview of attention for article published in PLOS ONE, October 2012
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Title
Cell Stiffness Is a Biomarker of the Metastatic Potential of Ovarian Cancer Cells
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0046609
Pubmed ID
Authors

Wenwei Xu, Roman Mezencev, Byungkyu Kim, Lijuan Wang, John McDonald, Todd Sulchek

Abstract

The metastatic potential of cells is an important parameter in the design of optimal strategies for the personalized treatment of cancer. Using atomic force microscopy (AFM), we show, consistent with previous studies conducted in other types of epithelial cancer, that ovarian cancer cells are generally softer and display lower intrinsic variability in cell stiffness than non-malignant ovarian epithelial cells. A detailed examination of highly invasive ovarian cancer cells (HEY A8) relative to their less invasive parental cells (HEY), demonstrates that deformability is also an accurate biomarker of metastatic potential. Comparative gene expression analyses indicate that the reduced stiffness of highly metastatic HEY A8 cells is associated with actin cytoskeleton remodeling and microscopic examination of actin fiber structure in these cell lines is consistent with this prediction. Our results indicate that cell stiffness may be a useful biomarker to evaluate the relative metastatic potential of ovarian and perhaps other types of cancer cells.

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Mendeley readers

The data shown below were compiled from readership statistics for 636 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 1%
France 3 <1%
Switzerland 2 <1%
Canada 2 <1%
Malaysia 1 <1%
Ukraine 1 <1%
India 1 <1%
Singapore 1 <1%
United Kingdom 1 <1%
Other 0 0%
Unknown 616 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 184 29%
Researcher 95 15%
Student > Master 66 10%
Student > Bachelor 56 9%
Student > Doctoral Student 40 6%
Other 93 15%
Unknown 102 16%
Readers by discipline Count As %
Engineering 152 24%
Agricultural and Biological Sciences 107 17%
Biochemistry, Genetics and Molecular Biology 79 12%
Physics and Astronomy 76 12%
Medicine and Dentistry 21 3%
Other 76 12%
Unknown 125 20%