Oviductal Glycoprotein (OVGP1, MUC9)

Maines-Bandiera, Sarah; Woo, Michelle; Borugian, Marilyn J.; Molday, Laurie L.; Hii, Theresa; Gilks, C Blake; Leung, Peter C.K.; Molday, Robert S.; Auersperg, Nelly

doi:10.1111/igc.0b013e3181bcc96d

Cited by 32 publications

(6 citation statements)

References 23 publications

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“…For instance, OVGP1 has been suggested as a possible serum marker for the detection of low grade ovarian cancer [58]. Although the TCGA dataset is all high grade serous ovarian cancer, in our analysis, it was highly expressed in differentiated subtype.…”

Section: Resultsmentioning

confidence: 67%

A multivariate approach to the integration of multi-omics datasets

et al. 2014

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BackgroundTo leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis.ResultsWe demonstrate integration of multiple layers of information using MCIA, applied to two typical “omics” research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor “omicade4” package.ConclusionWe believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets.

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Section: Resultsmentioning

confidence: 67%

A multivariate approach to the integration of multi-omics datasets

et al. 2014

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“…The CD1 cells were then continuously cultured to up to 130 passages to generate two cell models, MOE LOW (passages 5–25) and MOE HIGH (passages 85–120). Both MOE LOW and MOE HIGH expressed oviductal glycoprotein 1 (OVGP1) and PAX8 based on RNA (Figure 1 A) and protein expression (Figure 1 B; Figure S1 in Supplementary Material) ( 42 , 43 ). MOE HIGH cells lost acetylated tubulin and expressed Pax8, consistent with other oviductal models cultured on plastic that tend to undergo senescence and lose cilia function that produce an outgrowth of the secretory subtype of epithelium.…”

Section: Resultsmentioning

confidence: 99%

Spontaneous Transformation of Murine Oviductal Epithelial Cells: A Model System to Investigate the Onset of Fallopian-Derived Tumors

et al. 2015

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High-grade serous carcinoma (HGSC) is the most lethal ovarian cancer histotype. The fallopian tube secretory epithelial cells (FTSECs) are a proposed progenitor cell type. Genetically altered FTSECs form tumors in mice; however, a spontaneous HGSC model has not been described. Apart from a subpopulation of genetically predisposed women, most women develop ovarian cancer spontaneously, which is associated with aging and lifetime ovulations. A murine oviductal cell line (MOELOW) was developed and continuously passaged in culture to mimic cellular aging (MOEHIGH). The MOEHIGH cellular model exhibited a loss of acetylated tubulin consistent with an outgrowth of secretory epithelial cells in culture. MOEHIGH cells proliferated significantly faster than MOELOW, and the MOEHIGH cells produced more 2D foci and 3D soft agar colonies as compared to MOELOW MOEHIGH were xenografted into athymic female nude mice both in the subcutaneous and the intraperitoneal compartments. Only the subcutaneous grafts formed tumors that were negative for cytokeratin, but positive for oviductal markers, such as oviductal glycoprotein 1 and Pax8. These tumors were considered to be poorly differentiated carcinoma. The differential molecular profiles between MOEHIGH and MOELOW were determined using RNA-Seq and confirmed by protein expression to uncover pathways important in transformation, like the p53 pathway, the FOXM1 pathway, WNT signaling, and splicing. MOEHIGH had enhanced protein expression of c-myc, Cyclin E, p53, and FOXM1 with reduced expression of p21. MOEHIGH were also less sensitive to cisplatin and DMBA, which induce lesions typically repaired by base-excision repair. A model of spontaneous tumorogenesis was generated starting with normal oviductal cells. Their transition to cancer involved alterations in pathways associated with high-grade serous cancer in humans.

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“…The CA-125 value is elevated in approximately 90% of late-stage cases of epithelial ovarian cancer (stages 3 and 4). However, it is only elevated in 50-60% of women with early stage disease and is also elevated in a number of benign conditions [2], [5], [7], [11]–[13].…”

Section: Introductionmentioning

confidence: 99%

Study of the Molecular Recognition of Aptamers Selected through Ovarian Cancer Cell-SELEX

et al. 2010

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BackgroundOvarian cancer is the most lethal gynecological malignancy, and the ovarian clear cell carcinoma subtype (OCCA) demonstrates a particularly poor response to standard treatment. Improvements in ovarian cancer outcomes, especially for OCCA, could be expected from a clearer understanding of the molecular pathology that might guide strategies for earlier diagnosis and more effective treatment.Methodology/Principal FindingsCell-SELEX technology was employed to develop new molecular probes for ovarian cancer cell surface markers. A total of thirteen aptamers with Kd's to ovarian cancer cells in the pico- to nanomolar range were obtained. Preliminary investigation of the targets of these aptamers and their binding characteristics was also performed.Conclusions/SignificanceWe have selected a series of aptamers that bind to different types of ovarian cancer, but not cervical cancer. Though binding to other cancer cell lines was observed, these aptamers could lead to identification of biomarkers that are related to cancer.

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Oviductal Glycoprotein (OVGP1, MUC9)

Cited by 32 publications

References 23 publications

A multivariate approach to the integration of multi-omics datasets

A multivariate approach to the integration of multi-omics datasets

Spontaneous Transformation of Murine Oviductal Epithelial Cells: A Model System to Investigate the Onset of Fallopian-Derived Tumors

Study of the Molecular Recognition of Aptamers Selected through Ovarian Cancer Cell-SELEX

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