scholarly journals Sphingolipid metabolites modulate dielectric characteristics of cells in a mouse ovarian cancer progression model

2013 ◽  
Vol 5 (6) ◽  
pp. 843-852 ◽  
Author(s):  
Alireza Salmanzadeh ◽  
Elizabeth S. Elvington ◽  
Paul C. Roberts ◽  
Eva M. Schmelz ◽  
Rafael V. Davalos
Keyword(s):  
Ovarian Cancer ◽  
Cancer Progression ◽  
Dielectric Characteristics ◽  
Progression Model

scholarly journals Changes in Gene Expression and Cellular Architecture in an Ovarian Cancer Progression Model

PLoS ONE ◽  
2011 ◽  
Vol 6 (3) ◽  
pp. e17676 ◽  
Author(s):  
Amy L. Creekmore ◽  
William T. Silkworth ◽  
Daniela Cimini ◽  
Roderick V. Jensen ◽  
Paul C. Roberts ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cancer Progression ◽  
Cellular Architecture ◽  
Progression Model

scholarly journals Prognostic values of GMPS, PR, CD40, and p21 in ovarian cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6301 ◽  
Author(s):  
Ping Wang ◽  
Zengli Zhang ◽  
Yujie Ma ◽  
Jun Lu ◽  
Hu Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Cancer Patients ◽  
Differential Expression ◽  
Cancer Progression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Progression Model ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Early detection and prediction of prognosis and treatment responses are all the keys in improving survival of ovarian cancer patients. This study profiled an ovarian cancer progression model to identify prognostic biomarkers for ovarian cancer patients. Mouse ovarian surface epithelial cells (MOSECs) can undergo spontaneous malignant transformation in vitro cell culture. These were used as a model of ovarian cancer progression for alterations in gene expression and signaling detected using the Illumina HiSeq2000 Next-Generation Sequencing platform and bioinformatical analyses. The differential expression of four selected genes was identified using the gene expression profiling interaction analysis (http://gepia.cancer-pku.cn/) and then associated with survival in ovarian cancer patients using the Cancer Genome Atlas dataset and the online Kaplan–Meier Plotter (http://www.kmplot.com) data. The data showed 263 aberrantly expressed genes, including 182 up-regulated and 81 down-regulated genes between the early and late stages of tumor progression in MOSECs. The bioinformatic data revealed four genes (i.e., guanosine 5′-monophosphate synthase (GMPS), progesterone receptor (PR), CD40, and p21 (cyclin-dependent kinase inhibitor 1A)) to play an important role in ovarian cancer progression. Furthermore, the Cancer Genome Atlas dataset validated the differential expression of these four genes, which were associated with prognosis in ovarian cancer patients. In conclusion, this study profiled differentially expressed genes using the ovarian cancer progression model and identified four (i.e., GMPS, PR, CD40, and p21) as prognostic markers for ovarian cancer patients. Future studies of prospective patients could further verify the clinical usefulness of this four-gene signature.

Quantitative Biophysical Metrics for Rapid Evaluation of Ovarian Cancer Metastatic Potential

2022 ◽  
Author(s):  
Apratim Mukherjee ◽  
Haonan Zhang ◽  
Katherine Ladner ◽  
Megan Brown ◽  
Jacob Urbanski ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Mouse Model ◽  
Cancer Progression ◽  
Metastatic Potential ◽  
Actin Stress Fiber ◽  
Fiber Network ◽  
Progression Model ◽  
Ovarian Cancer Cell Lines ◽  
Fiber Spacing ◽  
Cellular Markers

Ovarian cancer is routinely diagnosed long after the disease has metastasized through the fibrous sub-mesothelium. Despite extensive research in the field linking ovarian cancer progression to increasingly poor prognosis, there are currently no validated cellular markers or hallmarks of ovarian cancer that can predict metastatic potential. To discern disease progression across a syngeneic mouse ovarian cancer progression model, here, we fabricated extracellular-matrix mimicking suspended fiber networks: crosshatches of mismatch diameters for studying protrusion dynamics, aligned same diameter networks of varying inter-fiber spacing for studying migration, and aligned nanonets for measuring cell forces. We found that migration correlated with disease, while force-disease biphasic relationship exhibited f-actin stress-fiber network dependence. However, unique to suspended fibers, coiling occurring at tips of protrusions and not the length or breadth of protrusions displayed strongest correlation with metastatic potential. To confirm that our findings were more broadly applicable beyond the mouse model, we repeated our studies in human ovarian cancer cell lines and found that the biophysical trends were consistent with our mouse model results. Altogether, we report complementary high throughput and high content biophysical metrics capable of identifying ovarian cancer metastatic potential on time scale of hours. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text]

scholarly journals Oncogenic Role of miR-200c-3p in High-Grade Serous Ovarian Cancer Progression via Targeting the 3′-Untranslated Region of DLC1

2021 ◽  
Vol 18 (11) ◽  
pp. 5741
Author(s):  
Sheril June Ankasha ◽  
Mohamad Nasir Shafiee ◽  
Norhazlina Abdul Wahab ◽  
Raja Affendi Raja Ali ◽  
Norfilza Mohd Mokhtar
Keyword(s):  
Ovarian Cancer ◽  
Cancer Progression ◽  
Untranslated Region ◽  
Luciferase Reporter ◽  
Clinical Samples ◽  
Binding Motif ◽  
Skov3 Cell ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Dlc1 Expression

High-grade serous ovarian cancer (HGSC) is the most common ovarian cancer with highly metastatic properties. A small non-coding RNA, microRNA (miRNA) was discovered to be a major regulator in many types of cancers through binding at the 3′-untranslated region (3′UTR), leading to degradation of the mRNA. In this study, we sought to investigate the underlying mechanisms involved in the dysregulation of miR-200c-3p in HGSC progression and metastasis. We identified the upregulation of miR-200c-3p expression in different stages of HGSC clinical samples and the downregulation of the tumor suppressor gene, Deleted in Liver Cancer 1 (DLC1), expression. Over expression of miR-200c-3p in HGSC cell lines downregulated DLC1 but upregulated the epithelial marker, E-cadherin (CDH1). Based on in silico analysis, two putative binding sites were found within the 3′UTR of DLC1, and we confirmed the direct binding of miR-200c-3p to the target binding motif at position 1488–1495 bp of 3′UTR of DLC1 by luciferase reporter assay in a SKOV3 cell line co-transfected with vectors and miR-200c-3p mimic. These data showed that miR-200c-3p regulated the progression of HGSC by regulating DLC1 expression post-transcription and can be considered as a promising target for therapeutic purposes.

Sign in / Sign up

Export Citation Format

Share Document