1. Extracellular vesicle miRNAs from three-dimensional ovarian cancer in vitro models and their implication in overall cancer survival
- Author
-
Nihar Godbole, Andrew Lai, Flavio Carrion, Katherin Scholz-Romero, Akhilandeshwari Ravichandran, Priyakshi Kalita-de Croft, Amy E. McCart Reed, Vaibhavi Joshi, Sunil R. Lakhani, Mostafa Kamal Masud, Yusuke Yamauchi, Lewis Perrin, John Hooper, Laura Bray, Dominic Guanzon, and Carlos Salomon
- Subjects
Ovarian cancer ,Cell culture models ,Extracellular vesicles ,Three-dimensional culture ,Science (General) ,Q1-390 ,Social sciences (General) ,H1-99 - Abstract
Ovarian cancer is the most common gynaecological malignancy and the seventh most diagnosed cancer in females worldwide. Currently, it is the sixth leading cause of cancer related mortality among patients globally. The heterogenous origin of the disease and unambiguous nature of the clinical symptoms leading to delayed detection has been one of the key reasons for increasing mortality. Hence new approaches are required to understand the biology of ovarian cancer, where the use of cell culture models that mimic the physiology of the disease is fundamental. Cell culture serves as a crucial in vitro tool, contributing to our comprehension of various aspects of cell biology, tissue morphology, disease mechanisms, drug responses, protein production, and tissue engineering. A significant portion of in vitro studies rely on two-dimensional (2D) cell cultures, however, these cultures present notable limitations, for example disruptions in cellular and extracellular interactions, alterations in cell morphology, polarity, and division mechanisms. Recently, extracellular vesicles have been identified as crucial players in cell biology as part of the communication system that cancer cells use to metastasize. We optimized and compared three-dimensional (3D) culture of ovarian cancer cells lines (SKOV-3 and OVCAR-3) with two-dimensional models based on their protein and miRNA content. We further investigated whether extracellular vesicles from these models reflect changes in cancer cells, and aid in the identification of overall survival in women with ovarian cancer.
- Published
- 2025
- Full Text
- View/download PDF