This work was supported by Start-Up Funding, College of Science and Mathematics Research Grant, Summer Research Session Award, and Research Seed Funding Award from Georgia Southern University.

1. Prediction of Clinical Outcomes from Genomic Profiles (PRECOG)
NOTE: The PRECOG portal (precog.stanford.edu) accesses publicly available data from 165 cancer expression datasets, including gene expression levels and patient clinical outcomes17. It specifically provides the Meta&#8210;Z analysis, which incorporates large datasets to provide Z&#8210;scores of different genes in 39 cancer types to indicate patient overall survival. Poor and good survival rates are indicated by positive and negative Z&#8210;score values, respectively.
<ol>
	<li>Create an account with an academic affiliated email to access this database. Enter the email address and password associated with the account.</li>
	<li>Click on the View Details button located underneath the Meta-Z analysis heading.</li>
	<li>Input the gene of interest into the Search bar.</li>
	<li>Use the scroll bar located on the bottom of the screen to obtain the survival Z-score for the specific cancer type of interest.</li>
</ol>
2. CSIOVDB
NOTE: CSIOVDB (<a href="http://csibio.nus.edu.sg/CSIOVDB/CSIOVDB.html" target="_blank">csibio.nus.edu.sg/CSIOVDB/CSIOVDB.html</a>) is a microarray database developed by the Cancer Science Institute of Singapore to study ovarian cancer18. This database contains data of carcinomas from different tumor sites as well as normal ovary tissue data. In addition, CSIOVDB provides Kaplan&#8210;Meier survival plots to assess patient survival with differential gene expression levels. CSIOVDB can be applied to investigate the association between gene expression levels and ovarian cancer stages/grades.
<ol>
	<li>Input gene of interest, then click the Search button.</li>
	<li>Click on the Disease State tab. 
	NOTE: This tab provides summary statistics of gene expression of the target gene of interest in ovarian cancer disease states.</li>
	<li>Click on the Histology tab. 
	NOTE: This tab provides summary statistics of gene expression of the target gene of interest in major ovarian cancer histologies.</li>
	<li>Click on the Clinico-pathological Parameters tab. 
	NOTE: This tab provides a comparison of the gene expression levels among different ovarian cancer stages, grades, and clinical responses with Mann-Whitney tests.</li>
	<li>Click on the Survival tab. 
	NOTE: This tab provides Kaplan-Meier plots associated with Overall Survival and Disease-Free Survival. For this database, disease-free survival is considered progression- and recurrence-free survival18. Multivariate analyses for Overall Survival and Disease-Free Survival are also found under this tab. The multivariate analyses compare features that relate to ovarian cancer prognoses (stage, grade, surgical debulking, histology, age) and the gene of interest.</li>
	<li>Click on the Subtype tab. 
	NOTE: This tab provides summary statistics and Mann-Whitney tests for the expression level of the gene of interest in molecular subtypes of ovarian cancer. This tab also provides Kaplan-Meier plots associated with Overall Survival and Disease-Free Survival of the gene of interest in molecular subtypes of ovarian cancer.</li>
</ol>
3. Gene Expression across Normal and Tumor tissue (GENT)
NOTE: The GENT portal (medical&#8210;genome.kribb.re.kr/GENT) is developed and maintained by the Korea Research Institute of Bioscience and Biotechnology (KRIBB)19. It collects 16,400 (U133A; 241 datasets) and 24,300 (U133plus2; 306 datasets) publicly available samples. After standardization, GENT offers gene expression data across diverse tissues, which are further divided into tumor and normal tissues.
<ol>
	<li>Click on the Search tab at the top of the screen.</li>
	<li>In the section labeled 1. Keyword, select the Gene symbol for the Terms from the dropdown menu, input the gene symbol of the gene of interest in the blank area of the Keyword section, and select Tissue for the Type option.</li>
	<li>Click the Search button at the bottom of the 1. Keyword section. It shows the summary graphs of gene expression in normal and tumor tissues of different cancer types based on the U133A and U122Plus2 platforms. 
	NOTE: It is optional to select the Data Filtering option on the top of the summary graph to single out a particular database to study.</li>
	<li>Click the link next to Result Data Download to access the detailed information about the gene expression values, tissue types, and data sources.</li>
</ol>
4. Broad Institute Cancer Cell Line Encyclopedia (CCLE)
NOTE: CCLE (portals.broadinstitute.org/ccle) was created by the Broad Institute and provides genomic profiles and mutations of 947 human cancer cell lines20.
<ol>
	<li>Input the desired genes into the search bar and then click the Search button.</li>
	<li>In the section labeled Select Dataset, click the mRNA expression (RNAseq) option from the dropdown menu. 
	NOTE: Other options include mRNA expression (Affy), Achilles shRNA knockdown, and Copy Number.</li>
	<li>Click on the Toggle All Traces button. Select the tissue type of interest from the gray box on the right. Scroll down to the bottom of the screen and click the Download mRNA expression button.</li>
	<li>Open the downloaded text document. Copy and paste all the text into Sheet 1. Copy all the text in the Sheet 1.</li>
	<li>Click on the sheet in the spreadsheet software Sheet 2 tab on the bottom of the spreadsheet. Right click on the A column, select Paste Special, and then select the Transpose option in Sheet 2.</li>
	<li>Once the text is transposed into two columns on Sheet 2, click the dropdown arrow for the Sort &#38; Filter option heading and then select the Filter option. An arrow will appear in the heading area labeled Gene. Click on the arrow and type in the tissue type of interest. 
	NOTE: This step will filter all the data and only display gene expression levels for the tissue type of interest.</li>
</ol>
5. cBioPortal
NOTE: cBioPortal (www.cioportal.org) was developed at the Memorial Sloan Kettering Cancer Center (MSK), and accesses, analyzes, and visualizes large scale cancer genomic data21,22. Specifically, this portal allows researchers to search for genetic alterations and signaling networks.
<ol>
	<li>Using the query on the landing page, click the organs/tissues of interest under the section labeled Select Studies. Select the particular study of interest, then hit the Query By Gene button.</li>
	<li>In the section labeled Select Genomic Profiles, select from the three options: Mutations, Putative copy-number alterations from GISTIC, or mRNA Expression. Further select corresponding data from the dropdown menu for Select Patient/Case Set.</li>
	<li>Enter the target gene symbol(s) in the query box of Enter Genes. Click the Submit Query button.</li>
	<li>Click on the Network tab at the top of the page to retrieve the desired gene network. 
	NOTE: The signaling network is color-coded. The inputted genes are indicated by seed nodes with a thick border. Each gene is represented by a red circle, and the color intensity of the red circle reflects its mutation frequency. Genes are connected by differently colored lines. Brown lines mean &#34;In Same Component&#34;, indicating the involvement in the same biological component. Blue lines mean &#34;Reacts With&#34;, indicating gene reactions. Green lines mean &#34;State Change&#34;, suggesting that one gene might cause a state change of another gene.</li>
	<li>Click on the File tab at the top of the image to choose Save as Image (PNG) for network image downloading.</li>
</ol>
6. Dissection of Drosophila with desired genotypes and DAPI staining
NOTE: Collect the female Drosophila with the desired genotypes, then dissect the fly ovaries to undergo the procedures of DAPI staining for imaging.
<ol>
	<li>Prepare fly stocks tj-Gal4, Gal80ts/CyO; UAS-NICD-GFP/TM6B, w*; UAS-mam.A; and w[1118] to create flies with NICD-overexpression (tj-Gal4, Gal80ts/+; UAS-NICD-GFP/+) and NICD and mam-overexpression (tj-Gal4, Gal80ts/UAS-mam.A; UAS-NICD-GFP/+) capability.</li>
	<li>Apply the temporal and regional gene expression targeting (TARGET) technique to control spatiotemporal gene expression23. Raise flies at 18 &#176;C until adulthood, then shift to 29 &#176;C for 48 h with yeast before dissection. 
	NOTE: tj-Gal4 can only drive UAS expression under higher temperatures, when the inhibition by Gal80ts is relieved. The addition of yeast prior to dissection enlarges the ovaries for harvesting.</li>
	<li>Place 3 mL of 1x phosphate-buffered saline (PBS) (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4) in an embryo collection dish. Use a CO2 pad to anesthetize the flies.</li>
	<li>Choose a female fly, then carefully grab the lower thorax of the fly using a pair of dissecting forceps and submerge it into the 1x PBS solution in an embryo collection dish. Use a second pair of forceps to pinch the lower abdomen and pull gently to release the internal organs.</li>
	<li>Identify and detach the pair of ovaries from the fly body. Break the muscular sheath located at the posterior end of the ovaries and separate the ovarioles. 
	NOTE: Separating the ovarioles and breaking the muscular sheath is required in order to achieve higher quality staining results.</li>
	<li>Place the ovaries in a 1.5 mL centrifuge tube that contains 500 &#956;L of 1x PBS. The tube should remain on ice until all ovaries are collected.</li>
	<li>Remove the 1x PBS and place 0.5 mL of fix solution (4% formaldehyde) into the tube. Place the tube on the nutator for 10 min.</li>
	<li>Remove the fix solution from the tube and dispose of it in a suitable waste container. Use 1 mL of 1x PBT (1x PBS supplemented with 0.4% Triton&#8482; X-100) to wash the ovaries 3x for 15 min.</li>
	<li>Discard the final PBT wash and add 1 mL of PBTG (0.2% bovine serum albumin, 5% normal goat serum in 1x PBT) to prevent nonspecific binding. 
	NOTE: This step could be skipped for DAPI staining, but it is essential for antibody staining. Detailed immunohistochemistry staining can be found in Jia et al.24.</li>
	<li>Place 150 &#956;L of DAPI (10 &#956;g/mL) in the tube for 10&#8722;15 minutes nutation. Discard the DAPI and wash the ovaries 1x for 10 min using 1 mL of 1x PBT. Remove the PBT and wash 2x for 10 min using 1x PBS.</li>
	<li>Remove excess PBS until approximately 300 &#956;L of PBS remains in the tube with the ovaries. Pipet the ovaries up and down several times using a 200 &#956;L pipette, in order to free the egg chambers.</li>
	<li>Gently spin down the tube and carefully remove as much 1x PBS solution as possible without removing the ovaries. Place 120 &#956;L of mounting solution (1 g n-propyl gallate, 5 ml of 10X PBS, 40 ml of glycerol and 5 ml of dH2O) into the tube. 
	NOTE: Mounting solution is sticky, so it is difficult to transfer exactly 120 &#956;L of mounting solution into a tube. To alleviate this issue, a 1,000 &#956;L pipette tip can be used to add three drops of mounting solution into the tube.</li>
	<li>Remove approximately 0.33 mm from a 200 &#956;L pipette tip and use the newly cut pipette tip to place the mounting solution on a microscope glass slide.</li>
	<li>Gently place the coverslip glass on the mounting solution and seal the edges of the cover slip with transparent nail polish. 
	NOTE: Sealing the edges of the cover glass is needed to prevent the egg chambers from flowing inside of the mounting solution when taking confocal images.</li>
	<li>Acquire images with a confocal microscope using the following settings: objective lens = 10x magnification; numerical aperture = 0.8; DAPI emission wavelength = 410&#8722;513 nm.</li>
</ol>

<ol>
	<li>Bocchicchio, S., Tesone, M., Irusta, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Convergence+of+Wnt+and+Notch+signaling+controls+ovarian+cancer+cell+survival.">Convergence of Wnt and Notch signaling controls ovarian cancer cell survival.</a> Journal of Cellular Physiology. , (2019).</li><li>Hibdon, E. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Notch+and+mTOR+Signaling+Pathways+Promote+Human+Gastric+Cancer+Cell+Proliferation.">Notch and mTOR Signaling Pathways Promote Human Gastric Cancer Cell Proliferation.</a> Neoplasia. 21 (7), 702-712 (2019).</li><li>Kucukkose, C., Yalcin Ozuysal, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+Notch+signalling+on+the+expression+of+SEMA3C,+HMGA2,+CXCL14,+CXCR7,+and+CCL20+in+breast+cancer.">Effects of Notch signalling on the expression of SEMA3C, HMGA2, CXCL14, CXCR7, and CCL20 in breast cancer.</a> Turkish Journal of Biology. 43 (1), 70-76 (2019).</li><li>Lan, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Notch+pathway+is+involved+in+the+suppression+of+colorectal+cancer+by+embryonic+stem+cell+microenvironment.">Notch pathway is involved in the suppression of colorectal cancer by embryonic stem cell microenvironment.</a> OncoTargets and Therapy. 12, 2869-2878 (2019).</li><li>Lian, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Notch+signaling+promotes+serrated+neoplasia+pathway+in+colorectal+cancer+through+epigenetic+modification+of+EPHB2+and+EPHB4.">Notch signaling promotes serrated neoplasia pathway in colorectal cancer through epigenetic modification of EPHB2 and EPHB4.</a> Cancer Management and Research. 10, 6129-6141 (2018).</li><li>Salazar, J. L., Yamamoto, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integration+of+Drosophila+and+Human+Genetics+to+Understand+Notch+Signaling+Related+Diseases.">Integration of Drosophila and Human Genetics to Understand Notch Signaling Related Diseases.</a> Advances in Experimental Medicine and Biology. 1066, 141-185 (2018).</li><li>Bray, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Notch+signalling+in+context.">Notch signalling in context.</a> Nature Reviews Molecular Cell Biology. 17 (11), 722-735 (2016).</li><li>Andersson, E. R., Sandberg, R., Lendahl, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Notch+signaling:+simplicity+in+design,+versatility+in+function.">Notch signaling: simplicity in design, versatility in function.</a> Development. 138 (17), 3593-3612 (2011).</li><li>Brou, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+proteolytic+cleavage+involved+in+Notch+signaling:+the+role+of+the+disintegrin-metalloprotease+TACE.">A novel proteolytic cleavage involved in Notch signaling: the role of the disintegrin-metalloprotease TACE.</a> Molecular Cell. 5 (2), 207-216 (2000).</li><li>Oswald, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=p300+acts+as+a+transcriptional+coactivator+for+mammalian+Notch-1.">p300 acts as a transcriptional coactivator for mammalian Notch-1.</a> Molecular and Cellular Biology. 21 (22), 7761-7774 (2001).</li><li>Xiu, M. X., Liu, Y. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+oncogenic+Notch2+signaling+in+cancer:+a+novel+therapeutic+target.">The role of oncogenic Notch2 signaling in cancer: a novel therapeutic target.</a> American Journal of Cancer Research. 9 (5), 837-854 (2019).</li><li>Allenspach, E. J., Maillard, I., Aster, J. C., Pear, W. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Notch+signaling+in+cancer.">Notch signaling in cancer.</a> Cancer Biololgy &amp; Therapy. 1 (5), 466-476 (2002).</li><li>Jia, D., Underwood, J., Xu, Q., Xie, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NOTCH2/NOTCH3/DLL3/MAML1/ADAM17+signaling+network+is+associated+with+ovarian+cancer.">NOTCH2/NOTCH3/DLL3/MAML1/ADAM17 signaling network is associated with ovarian cancer.</a> Oncology Letters. 17 (6), 4914-4920 (2019).</li><li>Weng, J. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Novel+bioinformatics+approaches+for+analysis+of+high-throughput+biological+data.">Novel bioinformatics approaches for analysis of high-throughput biological data.</a> Biomed Research International. 2014, 814092 (2014).</li><li>Readhead, B., Dudley, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translational+Bioinformatics+Approaches+to+Drug+Development.">Translational Bioinformatics Approaches to Drug Development.</a> Advances in Wound Care (New Rochelle). 2 (9), 470-489 (2013).</li><li>Bayat, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Science,+medicine,+and+the+future:+Bioinformatics.">Science, medicine, and the future: Bioinformatics.</a> BMJ. 324 (7344), 1018-1022 (2002).</li><li>Gentles, A. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+prognostic+landscape+of+genes+and+infiltrating+immune+cells+across+human+cancers.">The prognostic landscape of genes and infiltrating immune cells across human cancers.</a> Nature Medicine. 21 (8), 938-945 (2015).</li><li>Tan, T. Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CSIOVDB:+a+microarray+gene+expression+database+of+epithelial+ovarian+cancer+subtype.">CSIOVDB: a microarray gene expression database of epithelial ovarian cancer subtype.</a> Oncotarget. 6 (41), 43843-43852 (2015).</li><li>Shin, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GENT:+gene+expression+database+of+normal+and+tumor+tissues.">GENT: gene expression database of normal and tumor tissues.</a> Cancer Informatics. 10, 149-157 (2011).</li><li>Barretina, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Cancer+Cell+Line+Encyclopedia+enables+predictive+modelling+of+anticancer+drug+sensitivity.">The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.</a> Nature. 483 (7391), 603-607 (2012).</li><li>Gao, J. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrative+Analysis+of+Complex+Cancer+Genomics+and+Clinical+Profiles+Using+the+cBioPortal.">Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal.</a> Science Signaling. 6 (269), (2013).</li><li>Cerami, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cBio+Cancer+Genomics+Portal:+An+Open+Platform+for+Exploring+Multidimensional+Cancer+Genomics+Data.">The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data.</a> Cancer Discovery. 2 (5), 401-404 (2012).</li><li>McGuire, S. E., Mao, Z., Davis, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatiotemporal+gene+expression+targeting+with+the+TARGET+and+gene-switch+systems+in+Drosophila.">Spatiotemporal gene expression targeting with the TARGET and gene-switch systems in Drosophila.</a> Science's STKE. 2004 (220), 6 (2004).</li><li>Jia, D., Huang, Y. C., Deng, W. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Cell+Cycle+Switches+in+Drosophila+Oogenesis.">Analysis of Cell Cycle Switches in Drosophila Oogenesis.</a> Methods in Molecular Biology. 1328, 207-216 (2015).</li><li>Lo, P. K., Huang, Y. C., Corcoran, D., Jiao, R., Deng, W. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inhibition+of+Notch+signaling+by+the+p105+and+p180+subunits+of+Drosophila+chromatin+assembly+factor+1+is+required+for+follicle+cell+proliferation.">Inhibition of Notch signaling by the p105 and p180 subunits of Drosophila chromatin assembly factor 1 is required for follicle cell proliferation.</a> Journal of Cell Science. 132 (2), (2019).</li><li>Keller Larkin, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+Notch+pathway+in+terminal+follicle+cell+differentiation+during+Drosophila+oogenesis.">Role of Notch pathway in terminal follicle cell differentiation during Drosophila oogenesis.</a> Development Genes and Evolution. 209 (5), 301-311 (1999).</li><li>Sun, J., Deng, W. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Notch-dependent+downregulation+of+the+homeodomain+gene+cut+is+required+for+the+mitotic+cycle/endocycle+switch+and+cell+differentiation+in+Drosophila+follicle+cells.">Notch-dependent downregulation of the homeodomain gene cut is required for the mitotic cycle/endocycle switch and cell differentiation in Drosophila follicle cells.</a> Development. 132 (19), 4299-4308 (2005).</li><li>Jia, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+large-scale+in+vivo+RNAi+screen+to+identify+genes+involved+in+Notch-mediated+follicle+cell+differentiation+and+cell+cycle+switches.">A large-scale in vivo RNAi screen to identify genes involved in Notch-mediated follicle cell differentiation and cell cycle switches.</a> Scientific Reports. 5, 12328 (2015).</li><li>Shcherbata, H. R., Althauser, C., Findley, S. D., Ruohola-Baker, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+mitotic-to-endocycle+switch+in+Drosophila+follicle+cells+is+executed+by+Notch-dependent+regulation+of+G1/S,+G2/M+and+M/G1+cell-cycle+transitions.">The mitotic-to-endocycle switch in Drosophila follicle cells is executed by Notch-dependent regulation of G1/S, G2/M and M/G1 cell-cycle transitions.</a> Development. 131 (13), 3169-3181 (2004).</li></ol>

The authors have nothing to disclose.

As there are countless approaches and methods for the utilization of bioinformatics, there are numerous databases available online to the general public. An abundance of information can be extracted from each of these databases, but some are best suited for particular purposes, such as assessing patient survival based on certain inputs. Systematic analyses of retrieved data from different individual databases can convincingly yield important scientific findings.
The current analysis focuses on...

The Notch signaling pathway is a highly conserved pathway that is important for many developmental processes within biological organisms. Notch signaling has been shown to play a significant role in cell proliferation and self-renewal, and defects in the Notch signaling pathway can lead to many types of cancers1,2,3,4,5,6. In some circumstances, the Notch signaling pathway has been linked to both tissue growth and cancer as well as cell death and tumor suppression7. Multiple Notch receptors (NOTCH 1&#8722;4) and co&#8210;activator Mastermind (MAML 1&#8722;3), all with diverse functions, add an additional level of complexity. While the Notch signaling pathway is sophisticated in terms of functions, its core pathway is simple on a molecular basis8. Notch receptors act as transmembrane proteins composed of extracellular and intracellular regions9. A ligand binding to the extracellular region of Notch receptors facilitates proteolytic cleavage, which allows the Notch intracellular domain (NICD) to be released into the nucleus. NICD then binds to co&#8210;activator Mastermind to activate downstream gene expression10.
In recent years, Notch signaling has been shown to play a variety of roles in the initiation and progression of several types of cancers across different species6,11. For instance, Notch signaling has been linked to tumorigenesis involving the human NOTCH1 gene12. Recently, the NOTCH2, NOTCH3, Delta-like 3 (DLL3), Mastermind&#8210;like protein 1 (MAML1), and a disintegrin and metalloproteinase domain&#8210;containing protein 17 (ADAM17) genes were shown to be strongly associated with ovarian cancer, especially with the poor overall survival of patients13.
As the amount of experimental and patient-associated data continuously increases, the demand for analysis of the available data increases as well. The available data are scattered across publications, and they may deliver inconsistent or even contradictory findings. With the development of new technology in recent decades, such as next-generation sequencing, the amount of available data has grown exponentially. Although this represents rapid advancements in science and opportunities for continued biological research, assessing the meaning of publicly available data to solve research questions is a great challenge14. We believe bioinformatics is a useful way to extract smaller pieces of information from large-scale datasets. Through the implementation of various bioinformatics approaches, researchers can quickly, reliably, and efficiently interpret these large datasets, yielding insightful discoveries. These discoveries may range from the identification of potential new drug therapy targets or disease biomarkers, to personalized patient treatments15,16.
Bioinformatics itself is rapidly evolving, and approaches are constantly changing as technological advances sweep medical and biological science. Currently, common bioinformatics approaches include the utilization of publicly accessible databases and software programs to analyze DNA or protein sequences, identify genes of particular relevance or importance, and determine the relevance of genes and gene products through functional genomics16. Although the field of bioinformatics is certainly not limited to these approaches, these are significant in helping clinicians and researchers manage biological data for the benefit of patients as a whole.
This study aims to highlight several important databases and their use for research about the Notch signaling pathway. NOTCH2, NOTCH3, and their co&#8210;activator MAML1 were used as examples for the database study. These genes were used because the importance of the Notch signaling pathway in ovarian cancer has been validated. Systematic analyses of retrieved data confirmed the importance of Notch signaling in ovarian cancer. In addition, because Notch signaling is well conserved across species, it was confirmed that overexpression of Drosophila melanogaster NICD and Mastermind together can induce tumors in Drosophila ovaries, supporting the database findings and the significant and conserved role of Notch signaling in ovarian cancer.

<table><tbody><tr><td>DAPI (4&#039;,6-Diamidino-2-Phenylindole, Dihydrochloride)</td><td>Invitrogen</td><td>D1306</td><td>1:1000 Dilution</td></tr><tr><td>PBS, Phosphate Buffered Saline, 10X Powder, pH 7.4</td><td>ThermoFisher</td><td>FLBP6651</td><td>Dissolved with ddH2O to make 1X PBS</td></tr><tr><td>Goat serum</td><td>Gibco</td><td>16210064</td><td>Serum</td></tr><tr><td>Embryo dish</td><td>Electron Microscopy Sciences</td><td>70543-45</td><td>Dissection Dish</td></tr><tr><td>Nutating mixers</td><td>Fisherbrand</td><td>88861041</td><td>Nutator</td></tr><tr><td>tj-Gal4, Gal80ts/ CyO; UAS-NICD-GFP/ TM6B</td><td>Dr. Wu-Min Deng at Florida State University</td><td>N/A</td><td>Fly stock</td></tr><tr><td>w*; UAS-mam.A</td><td>Bloomington Drosophila Stock Center</td><td>#27743</td><td>Fly stock</td></tr><tr><td>w[1118]</td><td>Bloomington Drosophila Stock Center</td><td>#5905</td><td>Fly stock</td></tr><tr><td>The PRECOG portal</td><td>Stanford University</td><td>precog.stanford.edu</td><td>Publicly accessible database of cancer expression datasets</td></tr><tr><td>CSIOVDB</td><td>Cancer Science Institute of Singapore</td><td>csibio.nus.edu.sg/CSIOVDB/CSIOVDB.html</td><td>Microarray database used to study ovarian cancer</td></tr><tr><td>The Gene Expression across Normal and Tumor tissue (GENT) Portal</td><td>Korea Research Institute of Bioscience and Biotechnology (KRIBB)</td><td>medical&amp;ndash;genome.kribb.re.kr/GENT</td><td>Publicly accessible database of gene expression data across diverse tissues, divided into tumor and normal tissues.</td></tr><tr><td>Broad Institute Cancer Cell Line Encyclopedia (CCLE)</td><td>Broad Institute and The Novartis Institutes for BioMedical Research</td><td>portals.broadinstitute.org/ccle</td><td>Provides genomic profiles and mutations of human cancer cell lines</td></tr><tr><td>cBioPortal</td><td>Memorial Sloan Kettering Cancer Center (MSK)</td><td>cioportal.org</td><td>Portal that allows researchers to search for genetic alterations and signaling networks</td></tr><tr><td>Zeiss 710 Inverted confocal microscope</td><td>Carl Zeiss</td><td>ID #M 210491</td><td>Examination and image collection of fluorescently labeled specimens</td></tr></tbody></table>

integration of bioinformatics approaches and experimental validations to understand the role of notch signaling in ovarian cancer

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to interference with proper development and may even result in initiation or progression of cancers in certain cases. Because this pathway serves complex and versatile functions, it can be studied extensively through many different approaches. Of these, bioinformatics provides an undeniably cost-efficient, approachable, and user-friendly method of study. Bioinformatics is a useful way to extract smaller pieces of information from large-scale datasets. Through the implementation of various bioinformatics approaches, researchers can quickly, reliably, and efficiently interpret these large datasets, yielding insightful applications and scientific discoveries. Here, a protocol is presented for integration of bioinformatics approaches to investigate the role of Notch signaling in ovarian cancer. Furthermore, bioinformatics findings are validated through experimentation.

Using the procedure mentioned in step 1 using the PRECOG portal, the Z-scores of NOTCH2, NOTCH3, and MAML1 in ovarian cancer were obtained (1.3, 2.32, 1.62, respectively). The negative Z&#8210;score values indicate the poor overall survival of patients with high expression levels of the three genes. Using Conditional Formatting of the spreadsheet software, the Z&#8210;score values are shown in a colored bar graph in Figure 1.
<p class="jove_con...

Watch this Scientific Journal Video about Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer at JoVE.com

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Bioinformatics is a useful way to process large-scale datasets. Through the implementation of bioinformatics approaches, researchers can quickly, reliably, and efficiently obtain insightful applications and scientific discoveries. This article demonstrates the utilization of bioinformatics in ovarian cancer research. It also successfully validates bioinformatics findings through experimentation.

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to ...

integration-bioinformatics-approaches-experimental-validations-to

Research

JoVE Journal

Cancer Research

6.6K Views.  Georgia Southern University. Bioinformatica is een handige manier om informatie uit gegevenssets te extraheren. Experimenteren is even belangrijk voor validatie. We combineren beide benaderingen om de rol van inkeping signalering bij eierstokkanker te onderzoeken.Het belangrijkste voordeel van deze techniek is dat onderzoekers de informatie uit de bioinformatica databases kunnen gebruiken om hun onderzoeksrichting te plannen voordat ze een experiment uitvoeren. Met geaccumuleerde bio-informatica gegevens van mense...

Video: Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

An Orthotopic Model of Serous Ovarian Cancer in Immunocompetent Mice for in vivo Tumor Imaging and Monitoring of Tumor Immune Responses

Background: Ovarian cancer is generally diagnosed at an advanced stage where the case/fatality ratio is high and thus remains the most lethal of all gynecologic malignancies among US women 1,2,3. Serous tumors are the most widespread forms of ovarian cancer and 4,5 the Tg-MISIIR-TAg transgenic represents the only mouse model that spontaneously develops this type of tumors. Tg-MISIIR-TAg mice express SV40 transforming region under control of the Mullerian Inhibitory Substance type II Receptor (MISIIR) gene promoter 6. Additional transgenic lines have been identified that express the SV40 TAg transgene, but do not develop ovarian tumors. Non-tumor prone mice exhibit typical lifespan for C57Bl/6 mice and are fertile. These mice can be used as syngeneic allograft recipients for tumor cells isolated from Tg-MISIIR-TAg-DR26 mice.
Objective: Although tumor imaging is possible 7, early detection of deep tumors is challenging in small living animals. To enable preclinical studies in an immunologically intact animal model for serous ovarian cancer, we describe a syngeneic mouse model for this type of ovarian cancer that permits in vivo imaging, studies of the tumor microenvironment and tumor immune responses.
Methods: We first derived a TAg+ mouse cancer cell line (MOV1) from a spontaneous ovarian tumor harvested in a 26 week-old DR26 Tg-MISIIR-TAg female. Then, we stably transduced MOV1 cells with TurboFP635 Lentivirus mammalian vector that encodes Katushka, a far-red mutant of the red fluorescent protein from sea anemone Entacmaea quadricolor with excitation/emission maxima at 588/635 nm 8,9,10. We orthotopically implanted MOV1Kat in the ovary 11,12,13,14 of non-tumor prone Tg-MISIIR-TAg female mice. Tumor progression was followed by in vivo optical imaging and tumor microenvironment was analyzed by immunohistochemistry.
Results: Orthotopically implanted MOV1Kat cells developed serous ovarian tumors. MOV1Kat tumors could be visualized by in vivo imaging up to three weeks after implantation (fig. 1) and were infiltrated with leukocytes, as observed in human ovarian cancers 15 (fig. 2).
Conclusions: We describe an orthotopic model of ovarian cancer suitable for in vivo imaging of early tumors due to the high pH-stability and photostability of Katushka in deep tissues. We propose the use of this novel syngeneic model of serous ovarian cancer for in vivo imaging studies and monitoring of tumor immune responses and immunotherapies.

An Orthotopic Model of Serous Ovarian Cancer in Immunocompetent Mice for in vivo Tumor Imaging and Monitoring of Tumor Immune Responses

To study in vivo tumor growth and tumor microenvironment, we used a syngeneic and orthotopic mouse model of ovarian cancer in immunocompetent animals. We transduced a mouse tumor cell line (MOV1) with Katushka fluorescent protein (MOV1KAT) and here we show its orthotopic implantation in ovary and in vivo imaging.

Background: Ovarian cancer is generally diagnosed at an advanced stage where the case/fatality ratio is high and thus remains the most lethal of all ...

Immunology and Infection

Stimulation of Notch Signaling in Mouse Osteoclast Precursors

Notch signaling is a key component of multiple physiological and pathological processes. The nature of Notch signaling, however, makes in vitro investigation of its varying and sometimes contradictory roles a challenge. As a component of direct cell-cell communication with both receptors and ligands bound to the plasma membrane, Notch signaling cannot be activated in vitro by simple addition of ligands to culture media, as is possible with many other signaling pathways. Instead, Notch ligands must be presented to cells in an immobilized state. 
Variations in methods of Notch signaling activation can lead to different outcomes in cultured cells. In osteoclast precursors, in particular, differences in methods of Notch stimulation and osteoclast precursor culture and differentiation have led to disagreement over whether Notch signaling is a positive or negative regulator of osteoclast differentiation. While closer comparisons of osteoclast differentiation under different Notch stimulation conditions in vitro and genetic models have largely resolved the controversy regarding Notch signaling and osteoclasts, standardized methods of continuous and temporary stimulation of Notch signaling in cultured cells could prevent such discrepancies in the future.
This protocol describes two methods for stimulating Notch signaling specifically in cultured mouse osteoclast precursors, though these methods should be applicable to any adherent cell type with minor adjustments. The first method produces continuous stimulation of Notch signaling and involves immobilizing Notch ligand to a tissue culture surface prior to the seeding of cells. The second, which uses Notch ligand bound to agarose beads allows for temporary stimulation of Notch signaling in cells that are already adhered to a culture surface. This protocol also includes methods for detecting Notch activation in osteoclast precursors as well as representative transcriptional markers of Notch signaling activation.

Notch signaling is a form of cellular communication that relies upon direct contact between cells. To properly induce Notch signaling in vitro, Notch ligands must be presented to cells in an immobilized state. This protocol describes methods for in vitro stimulation of Notch signaling in mouse osteoclast precursors.

Notch signaling is a key component of multiple physiological and pathological processes. The nature of Notch signaling, however, makes in vitro ...

Developmental Biology

Heterotypic Three-dimensional In Vitro Modeling of Stromal-Epithelial Interactions During Ovarian Cancer Initiation and Progression

Epithelial ovarian cancers (EOCs) are the leading cause of death from gynecological malignancy in Western societies. Despite advances in surgical treatments and improved platinum-based chemotherapies, there has been little improvement in EOC survival rates for more than four decades 1,2. Whilst stage I tumors have 5-year survival rates &gt;85%, survival rates for stage III/IV disease are &lt;40%. Thus, the high rates of mortality for EOC could be significantly decreased if tumors were detected at earlier, more treatable, stages 3-5. At present, the molecular genetic and biological basis of early stage disease development is poorly understood. More specifically, little is known about the role of the microenvironment during tumor initiation; but known risk factors for EOCs (e.g. age and parity) suggest that the microenvironment plays a key role in the early genesis of EOCs. We therefore developed three-dimensional heterotypic models of both the normal ovary and of early stage ovarian cancers. For the normal ovary, we co-cultured normal ovarian surface epithelial (IOSE) and normal stromal fibroblast (INOF)&nbsp;cells, immortalized by retrovrial transduction of the catalytic subunit of human telomerase holoenzyme (hTERT) to extend the lifespan of these cells in culture. To model the earliest stages of ovarian epithelial cell transformation, overexpression of the CMYC oncogene in IOSE cells, again co-cultured with INOF cells. These heterotypic models were used to investigate the effects of aging and senescence on the transformation and invasion of epithelial cells. Here we describe the methodological steps in development of these three-dimensional model; these methodologies aren't specific to the development of normal ovary and ovarian cancer tissues, and could be used to study other tissue types where stromal and epithelial cell interactions are a fundamental aspect of the tissue maintenance and disease development.

Heterotypic Three-dimensional In Vitro Modeling of Stromal-Epithelial Interactions During Ovarian Cancer Initiation and Progression

We describe methodologies for establishing in vitro heterotypic three-dimensional models comprising ovarian fibroblasts and normal ovarian surface or ovarian cancer epithelial cells. We discuss the use of these models to study stromal-epithelial interactions that occur during ovarian cancer development.

Epithelial ovarian cancers (EOCs) are the leading cause of death from gynecological malignancy in Western societies. Despite advances in surgical ...

Medicine

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Epithelial ovarian cancer is the most lethal gynecologic malignancy in the United States. Although patients initially respond to the current standard of care consisting of surgical debulking and combination chemotherapy consisting of platinum and taxane compounds, almost 90% of patients recur within a few years. In these patients the development of chemoresistant disease limits the efficacy of currently available chemotherapy agents and therefore contributes to the high mortality. To discover novel therapy options that can target recurrent disease, appropriate animal models that closely mimic the clinical profile of patients with recurrent ovarian cancer are required. The challenge in monitoring intra-peritoneal (i.p.) disease limits the use of i.p. models and thus most xenografts are established subcutaneously. We have developed a sensitive optical imaging platform that allows the detection and anatomical location of i.p. tumor mass. The platform includes the use of optical reporters that extend from the visible light range to near infrared, which in combination with 2-dimensional X-ray co-registration can provide anatomical location of molecular signals. Detection is significantly improved by the use of a rotation system that drives the animal to multiple angular positions for 360 degree imaging, allowing the identification of tumors that are not visible in single orientation. This platform provides a unique model to non-invasively monitor tumor growth and evaluate the efficacy of new therapies for the prevention or treatment of recurrent ovarian cancer.

We describe a non-invasive animal imaging platform that allows the detection, quantification, and monitoring of ovarian cancer growth and recurrence. This intra-peritoneal xenograft model mimics the clinical profile of patients with ovarian cancer.

Epithelial ovarian cancer is the most lethal gynecologic malignancy in the United States. Although patients initially respond to the current standard of ...

Establishing a 3D Ex Vivo Model for Ovarian Cancer-Omentum Interaction

An Ex Vivo Model of Ovarian Cancer Peritoneal Metastasis Using Human Omentum

Ovarian cancer is the deadliest gynecologic malignancy. The omentum plays a key role in providing a supportive microenvironment to metastatic ovarian cancer cells as well as immune modulatory signals that allow tumor tolerance. However, we have limited models that closely mimic the interaction between ovarian cancer cells and adipose-rich tissues. To further understand the cellular and molecular mechanisms by which the omentum provides a pro-tumoral microenvironment, we developed a unique 3D ex vivo model of cancer cell-omentum interaction. Using human omentum, we are able to grow ovarian cancer cells within this adipose-rich microenvironment and monitor the factors responsible for tumor growth and immune regulation. In addition to providing a platform for the study of this adipose-rich tumor microenvironment, the model provides an excellent platform for the development and evaluation of novel therapeutic approaches to target metastatic cancer cells in this niche. The proposed model is easy to generate, inexpensive, and applicable to translational investigations.

Author Spotlight: Advanced Ex Vivo Model for Investigating Cancer-Adipose Microenvironment Interaction 

This protocol describes the establishment of a three-dimensional (3D) ex vivo model of cancer cell-omentum interaction. The model provides a platform for elucidating pro-tumor mechanisms within the adipose niche and for testing novel therapies.

Ovarian cancer is the deadliest gynecologic malignancy. The omentum plays a key role in providing a supportive microenvironment to metastatic ovarian ...

Standardizing Ovarian Cancer Organoid Biobanking

Strategy for Biobanking of Ovarian Cancer Organoids: Addressing the Interpatient Heterogeneity across Histological Subtypes and Disease Stages

While the establishment of an ovarian cancer biobank from patient-derived organoids along with their clinical background information promises advances in research and patient care, standardization remains a challenge due to the heterogeneity of this lethal malignancy, combined with the inherent complexity of organoid technology. This adaptable protocol provides a systematic framework to realize the full potential of ovarian cancer organoids considering a patient-specific variability of progenitors. By implementing a structured experimental workflow to select optimal culture conditions and seeding methods, with parallel testing of direct 3D seeding versus a 2D/3D route, we obtain, in most cases, robust long-term expanding lines suitable for a broad range of downstream applications. 
Notably, the protocol has been tested and proven efficient in a great number of cases (N = 120) of highly heterogeneous starting material, including high-grade and low-grade ovarian cancer and stages of the disease with primary debulking, recurrent disease, and post-neoadjuvant surgical specimens. Within a low Wnt, high BMP exogenous signaling environment, we observed progenitors being differently susceptible to the activation of the Heregulin 1 &#223; (HER&#223;-1)-pathway, with HER&#223;-1 promoting organoid formation in some while inhibiting it in others. For a subset of the patient's samples, optimal organoid formation and long-term growth necessitate the addition of fibroblast growth factor 10 and R-Spondin 1 to the medium. 
Further, we highlight the critical steps of tissue digestion and progenitor isolation and point to examples where brief cultivation in 2D on plastic is beneficial for subsequent organoid formation in the Basement Membrane Extract type 2 matrix. Overall, optimal biobanking requires systematic testing of all main conditions in parallel to identify an adequate growth environment for individual lines. The protocol also describes the handling procedure for efficient embedding, sectioning, and staining to obtain high-resolution images of organoids, which is required for comprehensive phenotyping.

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer 

This protocol offers a systematic framework for the establishment of ovarian cancer organoids from different disease stages and addresses the challenges of patient-specific variability to increase yield and enable robust long-term expansion for subsequent applications. It includes detailed steps for tissue processing, seeding, adjusting media requirements, and immunofluorescence staining.

While the establishment of an ovarian cancer biobank from patient-derived organoids along with their clinical background information promises advances in ...

TMOD3 Expression and Its Role in Ovarian Cancer Progression

Tropomodulin 3 Overexpression as a Marker for Platinum Resistance and Immune Infiltration in Ovarian Cancer

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, but its role in the drug resistance of ovarian cancer remains unexplored. By analyzing data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, this study compared TMOD3 expression in ovarian cancer and normal tissues, and examined the expression of TMOD3 after platinum treatment in platinum-sensitive and platinum-resistant ovarian cancers. The Kaplan-Meier method was used to assess the effect of TMOD3 on overall survival (OS) and progression-free survival (PFS) in ovarian cancer patients. microRNAs (miRNAs) targeting TMOD3 were predicted using TargetScan and analyzed using the TCGA database. Tumor Immune Estimation Resource (TIMER) and an integrated repository portal for tumor-immune system interactions (TISIDB) were used to determine the relationship between TMOD3 expression and immune infiltration. TMOD3 coexpression networks in ovarian cancer were explored using LinkedOmics, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and The Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics. The results showed that TMOD3 was highly expressed in ovarian cancer and was associated with the grading, staging, and metastasis of ovarian cancer. TMOD3 expression was significantly reduced in platinum-treated ovarian cancer cells and patients. However, TMOD3 expression was higher in platinum-resistant ovarian cancer cells and tissues compared to platinum-sensitive ones. Higher TMOD3 expression was significantly associated with lower OS and PFS in ovarian cancer patients treated with platinum-based chemotherapy. miRNA-mediated post-transcriptional regulation is likely responsible for high TMOD3 expression in ovarian cancer and platinum-resistant ovarian tissues. The expression of TMOD3 mRNA was associated with immune infiltration in ovarian cancer. These findings indicate that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration.

Author Spotlight: Unveiling the Role of TMOD3 in Platinum Resistance and Immune Infiltration in Ovarian Cancer

Tropomodulin 3 (TMOD3) has been increasingly studied in tumors in recent years. This study is the first to report that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration. These results could help improve the therapeutic outcomes for ovarian cancer.

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, ...

Transplantation Into the Mouse Ovarian Fat Pad

Orthotopic transplantation assays in mice are invaluable for studies of cell regeneration and neoplastic transformation. Common approaches for orthotopic transplantation of ovarian surface and tubal epithelia include intraperitoneal and intrabursal administration of cells. The respective limitations of these methods include poorly defined location of injected cells and limited space volume. Furthermore, they are poorly suited for long-term structural preservation of transplanted organs. To address these challenges, we have developed an alternative approach, which is based on the introduction of cells and tissue fragments into the mouse fat pad. The mouse ovarian fat pad is located in the immediate vicinity of the ovary and uterine tube (aka oviduct, fallopian tube), and provides a familiar microenvironment for cells and tissues of these organs. In our approach fluorescence-labeled mouse and human cells, and fragments of the uterine tube are engrafted by using minimally traumatic dorsal incision surgery. Transplanted cells and their outgrowths are easily located in the ovarian fat pad for over 40 days. Long-term transplantation of the entire uterine tube allows correct preservation of all principle tissue components, and does not result in adverse side effects, such as fibrosis and inflammation. Our approach should be uniquely applicable for answering important biological questions such as differentiation, regenerative and neoplastic potential of specific cell populations. Furthermore, it should be suitable for studies of microenvironmental factors in normal development and cancer.

We describe an ovarian fad pad transplantation assay suitable for studies of normal and transformed epithelia of the female reproductive tract. The mouse fat pad allows transplantation of large tissue fragments, is easily accessible for surgery and imaging, and provides the most favorable native environment for tissues of M&#252;llerian origin.

Orthotopic transplantation assays in mice are invaluable for studies of cell regeneration and neoplastic transformation. Common approaches for orthotopic ...

A Proximal Culture Method to Study Paracrine Signaling Between Cells

Intercellular interactions play an important role in many biological processes, including tumor progression, immune responses, angiogenesis, and development. Paracrine or juxtacrine signaling mediates such interactions. The use of a conditioned medium and coculture studies are the most common methods to discriminate between these two types of interactions. However, the effect of localized high concentrations of secreted factors in the microenvironment during the paracrine interactions is not accurately recapitulated by conditioned medium and, thus, may lead to imprecise conclusions. To overcome this problem, we have devised a proximal culture method to study paracrine signaling. The two cell types are grown on either surface of a 10 &#181;m-thick polycarbonate membrane with 0.4 &#181;m pores. The pores allow the exchange of secreted factors and, at the same time, inhibit juxtacrine signaling. The cells can be collected and lysed at the endpoint to determine the effects of the paracrine signaling. In addition to allowing for localized concentration gradients of secreted factors, this method is amenable to experiments involving prolonged periods of culture, as well as the use of inhibitors. While we use this method to study the interactions between ovarian cancer cells and the mesothelial cells they encounter at the site of metastasis, it can be adapted to any two adherent cell types for researchers to study paracrine signaling in various fields, including tumor microenvironment, immunology, and development.

Paracrine and juxtacrine cellular interactions play an important role in many biological processes, including tumor progression, immune responses, angiogenesis, and development. Here, a proximal culture method is used to study paracrine signaling where the localized concentrations of the secreted factors are maintained while preventing direct cellular contact.

Intercellular interactions play an important role in many biological processes, including tumor progression, immune responses, angiogenesis, and ...

Biology

Evaluating the Angiogenetic Properties of Ovarian Cancer Stem-Like Cells using the Three-Dimensional Co-Culture System, NICO-1

Cancer stem cells (CSCs) reside in a supportive niche, constituting a microenvironment comprised of adjacent stromal cells, vessels, and extracellular matrix. The ability of CSCs to participate in the development of endothelium constitutes an important characteristic that directly contributes to the general understanding of the mechanisms of tumorigenesis and tumor metastasis. The purpose of this work is to establish a reproducible methodology to investigate the tumor-initiation capability of ovarian cancer stem cells (OCSCs). Herein, we examined the neovascularization mechanism between endothelial cells and OCSCs along with the morphological changes of endothelial cells using the in vitro co-culture model NICO-1. This protocol allows visualization of the neovascularization step surrounding the OCSCs in a time course manner. The technique can provide insight regarding the angiogenetic properties of OCSCs in tumor metastasis.

Ovarian cancer stem cells (OCSC) are responsible for cancer initiation, recurrence, therapeutic resistance, and metastasis. The OCSC vascular niche is considered to promote self-renewal of OCSCs, leading to chemoresistance. This protocol provides the basis for establishing a reproducible OCSC vascular niche model in vitro.

Cancer stem cells (CSCs) reside in a supportive niche, constituting a microenvironment comprised of adjacent stromal cells, vessels, and extracellular ...

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Samenvatting

Meer video's verkennen