Abstract
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.
Highlights
Ovarian cancer is a lethal disease in women
We found that the early passages of MOSE-I cells grew slowly with doubling time of 48 h, whereas the growth rate MOSE-I increased when it was passaged more than 35 passages
GMPS was highly expressed in ovary cancer tissues, which was associated with relapse-free survival (RFS), whereas the expression of PR, CD40, and p21 mRNA was reduced in ovarian cancer tissues, which was associated with poor RFS
Summary
Ovarian cancer is a lethal disease in women. Epithelial ovarian cancer accounts for approximately 90% of all ovarian malignancies (Siegel, Miller & Jemal, 2018) and is frequently diagnosed at the advanced stages when cancer cells have already metastasized to other organs (Jayson et al, 2014). Early tumor detection and effective prognosis prediction are the keys in improving survival of ovarian cancer patients. To this end, previous studies have searched and evaluated biomarkers to diagnose this deadly disease early and to predict survival or treatment responses or target the biomarker genes to develop novel therapies for ovarian cancer (Jayson et al, 2014) as well as analyzed the differential gene expression patterns between mucinous and clear cell ovarian cancers or between low-grade, low malignant potential and high-grade, metastatic ovarian cancer (Bonome et al, 2005; Meinhold-Heerlein et al, 2005). The current approaches or analyses of differential gene expression profiles usually compare ovarian cancer vs. normal tissues, which leads to limited data; the characterization of differential gene expression profiles between early stage precursor lesions and ovarian cancer could provide novel insights into identifying biomarkers for early detection or prognosis prediction and therapeutic targets of ovarian cancer
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