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>

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	<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. 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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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>DAPI (4&#39;,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&#8211;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.
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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. 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.

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

Murine Experimental Model of Original Tumor Development and Peritoneal Metastasis via Orthotopic Inoculation with Ovarian Carcinoma Cells

Epithelial ovarian carcinoma (EOC) is associated with a poor prognosis because it shows peritoneal dissemination. To improve the prognosis, it is important to control peritoneal dissemination. However, it is still unclear how tumor cells detach from primary lesions and attach to the mesothelium. The establishment of an appropriate animal model is needed to gain an understanding of the mechanism of peritoneal dissemination in vivo. In the current study, we introduce the process from the local injection of EOC cells into the murine ovarian surface to the development of metastasis, including the peritoneum and distant organs. Female nude mice (BALB/c nu/nu) at 8 weeks of age were used. Under a microscopic field of view, EOC cells (1 x 105 cells/&#181;l of medium-extracellular matrix (ECM)-based hydrogel/unilateral ovary/mouse) were injected into murine ovaries through a retroperitoneal approach from the dorsal flank. This proposed method is a less invasive procedure for the mouse and minimizes damage to the ovary. Here, we describe the methodological steps in the development of the original and metastatic tumor formation of EOC.

Murine Experimental Model of Original Tumor Development and Peritoneal Metastasis via Orthotopic Inoculation with Ovarian Carcinoma Cells

To clarify the multistep process of peritoneal dissemination of ovarian carcinoma, the current study presents a murine experimental model of original tumor development and peritoneal metastasis via orthotopic inoculation with these tumor cells.

Epithelial ovarian carcinoma (EOC) is associated with a poor prognosis because it shows peritoneal dissemination. To improve the prognosis, it is ...

Imaging Approaches to Assessments of Toxicological Oxidative Stress Using Genetically-encoded Fluorogenic Sensors

While oxidative stress is a commonly cited toxicological mechanism, conventional methods to study it suffer from a number of shortcomings, including destruction of the sample, introduction of potential artifacts, and a lack of specificity for the reactive species involved. Thus, there is a current need in the field of toxicology for non-destructive, sensitive, and specific methods that can be used to observe and quantify intracellular redox perturbations, more commonly referred to as oxidative stress. Here, we present a method for the use of two genetically-encoded fluorogenic sensors, roGFP2 and HyPer, to be used in live-cell imaging studies to observe xenobiotic-induced oxidative responses. roGFP2 equilibrates with the glutathione redox potential (EGSH), while HyPer directly detects hydrogen peroxide (H2O2). Both sensors can be expressed into various cell types via transfection or transduction, and can be targeted to specific cellular compartments. Most importantly, live-cell microscopy using these sensors offers high spatial and temporal resolution that is not possible using conventional methods. Changes in the fluorescence intensity monitored at 510 nm serves as the readout for both genetically-encoded fluorogenic sensors when sequentially excited by 404 nm and 488 nm light. This property makes both sensors ratiometric, eliminating common microscopy artifacts and correcting for differences in sensor expression between cells. This methodology can be applied across a variety of fluorometric platforms capable of exciting and collecting emissions at the prescribed wavelengths, making it suitable for use with confocal imaging systems, conventional wide-field microscopy, and plate readers. Both genetically-encoded fluorogenic sensors have been used in a variety of cell types and toxicological studies to monitor cellular EGSH and H2O2 generation in real-time. Outlined here is a standardized method that is widely adaptable across cell types and fluorometric platforms for the application of roGFP2 and HyPer in live-cell toxicological assessments of oxidative stress.

This manuscript describes the use of genetically-encoded fluorogenic reporters in an application of live-cell imaging for the examination of xenobiotic-induced oxidative stress. This experimental approach offers unparalleled spatiotemporal resolution, sensitivity, and specificity while avoiding many of the shortcomings of conventional methods used for the detection of toxicological oxidative stress.

While oxidative stress is a commonly cited toxicological mechanism, conventional methods to study it suffer from a number of shortcomings, including ...

Evaluating the Role of Mitochondrial Function in Cancer-related Fatigue

Fatigue is a common and debilitating condition that affects most cancer patients. To date, fatigue remains poorly characterized with no diagnostic test to objectively measure the severity of this condition. Here we describe an optimized method for assessing mitochondrial function of PBMCs collected from fatigued cancer patients. Using a compact extracellular flux system and sequential injection of respiratory inhibitors, we examined PBMC mitochondrial functional status by measuring basal mitochondrial respiration, spare respiratory capacity, and energy phenotype, which describes the preferred energy pathway to respond to stress. Fresh PBMCs are readily available in the clinical setting using standard phlebotomy. The entire assay described in this protocol can be completed in less than 4 hours without the involvement of complex biochemical techniques. Additionally, we describe a normalization method that is necessary for obtaining reproducible data. The simple procedure and normalization methods presented allow for repeated sample collection from the same patient and generation of reproducible data that can be compared between time points to evaluate potential treatment effects.

Our goal was to develop a practical protocol to evaluate mitochondrial dysfunction associated with fatigue in cancer patients. This innovative protocol is optimized for clinical use involving only standard phlebotomy and basic laboratory procedures.

Fatigue is a common and debilitating condition that affects most cancer patients. To date, fatigue remains poorly characterized with no diagnostic test to ...

Methods for Evaluating the Role of c-Fos and Dusp1 in Oncogene Dependence

The demonstration of tyrosine kinase inhibitors (TKIs) in treating chronic myeloid leukemia (CML) has heralded a new era in cancer therapeutics. However, a small population of cells does not respond to TKI treatment, resulting in minimal residual disease (MRD); even the most potent TKIs fail to eradicate these cells. These MRD cells serve as a reservoir to develop resistance to therapy. Why TKI treatment is ineffective against MRD cells is not known. Growth factor signaling is implicated in supporting the survival of MRD cells during TKI treatment, but a mechanistic understanding is lacking. Recent studies demonstrated that an elevated c-Fos and Dusp1 expression as a result of convergent oncogenic and growth factor signaling in MRD cells mediate TKI resistance. The genetic and chemical inhibition of c-Fos and Dusp1 renders CML exquisitely sensitive to TKIs and cures CML in both genetic and humanized mouse models. We identified these target genes using multiple microarrays from TKI-sensitive and -resistant cells. Here, we provide methods for target validation using in vitro and in vivo mouse models. These methods can easily be applied to any target for genetic validation and therapeutic development.

Here, we describe protocols for the genetic and chemical validation of c-Fos and Dusp1 as a drug target in leukemia using in vitro and in vivo genetic and humanized mouse models. This method can be applied to any target for genetic validation and therapeutic development.

The demonstration of tyrosine kinase inhibitors (TKIs) in treating chronic myeloid leukemia (CML) has heralded a new era in cancer therapeutics. However, ...

Characterize Disease-related Mutants of RAF Family Kinases by Using a Set of Practical and Feasible Methods

The rapidly accelerated fibrosarcoma (RAF) family kinases play a central role in cell biology and their dysfunction leads to cancers and developmental disorders. A characterization of disease-related RAF mutants will help us select appropriate therapeutic strategies for treating these diseases. Recent studies have shown that RAF family kinases have both catalytic and allosteric activities, which are tightly regulated by dimerization. Here, we constructed a set of practical and feasible methods to determine the catalytic and allosteric activities and the relative dimer affinity/stability of RAF family kinases and their mutants. Firstly, we amended the classical in vitro kinase assay by reducing the detergent concentration in buffers, utilizing a gentle quick wash procedure, and employing a glutathione S-transferase (GST) fusion to prevent RAF dimers from dissociating during purification. This enables us to measure the catalytic activity of constitutively active RAF mutants appropriately. Secondly, we developed a novel RAF co-activation assay to evaluate the allosteric activity of kinase-dead RAF mutants by using N-terminal truncated RAF proteins, eliminating the requirement of active Ras in current protocols and thereby achieving a higher sensitivity. Lastly, we generated a unique complementary split luciferase assay to quantitatively measure the relative dimer affinity/stability of various RAF mutants, which is more reliable and sensitive compared to the traditional co-immunoprecipitation assay. In summary, these methods have the following advantages: (1) user-friendly; (2) able to carry out effectively without advanced equipment; (3) cost-effective; (4) highly sensitive and reproducible.

In this article, we presented a set of practical and feasible methods for characterizing disease-related mutants of RAF family kinases, which include in vitro kinase assay, RAF co-activation assay, and complementary split luciferase assay.

The rapidly accelerated fibrosarcoma (RAF) family kinases play a central role in cell biology and their dysfunction leads to cancers and developmental ...

Combined Use of Tail Vein Metastasis Assays and Real-Time In Vivo Imaging to Quantify Breast Cancer Metastatic Colonization and Burden in the Lungs

Metastasis is the main cause of cancer-related deaths and there are limited therapeutic options for patients with metastatic disease. The identification and testing of novel therapeutic targets that will facilitate the development of better treatments for metastatic disease requires preclinical in vivo models. Demonstrated here is a syngeneic mouse model for assaying breast cancer metastatic colonization and subsequent growth. Metastatic cancer cells are stably transduced with viral vectors encoding firefly luciferase and ZsGreen proteins. Candidate genes are then stably manipulated in luciferase/ZsGreen-expressing cancer cells and then the cells are injected into mice via the lateral tail vein to assay metastatic colonization and growth. An in vivo imaging device is then used to measure the bioluminescence or fluorescence of the tumor cells in the living animals to quantify changes in metastatic burden over time. The expression of the fluorescent protein allows the number and size of metastases in the lungs to be quantified at the end of the experiment without the need for sectioning or histological staining. This approach offers a relatively quick and easy way to test the role of candidate genes in metastatic colonization and growth, and provides a great deal more information than traditional tail vein metastasis assays. Using this approach, we show that simultaneous knockdown of Yes associated protein (YAP) and transcriptional co-activator with a PDZ-binding motif (TAZ) in breast cancer cells leads to reduced metastatic burden in the lungs and that this reduced burden is the result of significantly impaired metastatic colonization and reduced growth of metastases.

The described approach combines experimental tail vein metastasis assays with in vivo live animal imaging to allow real-time monitoring of breast cancer metastasis formation and growth in addition to the quantification of metastasis number and size in the lungs.

Metastasis is the main cause of cancer-related deaths and there are limited therapeutic options for patients with metastatic disease. The identification ...

Characterization and Functional Prediction of Bacteria in Ovarian Tissues

The theory of a "sterile" female upper reproductive tract has been encountering increasing opposition due to advancements in bacterial detection. However, whether ovaries contain bacteria has not yet been confirmed yet. Herein, an experiment to detect bacteria in ovarian tissues was introduced. We chose ovarian cancer patients in the cancer group and noncancerous patients in the control group. 16S rRNA gene sequencing was used to differentiate bacteria in ovarian tissues from the cancer and control groups. Furthermore, we predicted the functional composition of the identified bacteria by using BugBase and PICRUSt. This method can also be used in other viscera and tissues since many organs have been proven to harbor bacteria in recent years. The presence of bacteria in viscera and tissues may help scientists evaluate cancerous and normal tissues and may be aid in the treatment of cancer.

Immunohistochemistry staining and 16S ribosomal RNA gene (16S rRNA gene) sequencing were performed in order to discover and distinguish bacteria in cancerous and noncancerous ovarian tissues in situ. The compositional and functional differences of the bacteria were predicted by using BugBase and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt).

The theory of a "sterile" female upper reproductive tract has been encountering increasing opposition due to advancements in bacterial detection. However, ...

A Mouse Model to Investigate the Role of Cancer-Associated Fibroblasts in Tumor Growth

Cancer-associated fibroblasts (CAFs) can play an important role in tumor growth by creating a tumor-promoting microenvironment. Models to study the role of CAFs in the tumor microenvironment can be helpful for understanding the functional importance of fibroblasts, fibroblasts from different tissues, and specific genetic factors in fibroblasts. Mouse models are essential for understanding the contributors to tumor growth and progression in an in vivo context. Here, a protocol in which cancer cells are mixed with fibroblasts and introduced into mice to develop tumors is provided. Tumor sizes over time and final tumor weights are determined and compared among groups. The protocol described can provide more insight into the functional role of CAFs in tumor growth and progression.

A protocol to co-inject cancer cells and fibroblasts and monitor tumor growth over time is provided. This protocol can be used to understand the molecular basis for the role of fibroblasts as regulators of tumor growth.

Cancer-associated fibroblasts (CAFs) can play an important role in tumor growth by creating a tumor-promoting microenvironment. Models to study the role ...

Exploring the Arginine Methylome by Nuclear Magnetic Resonance Spectroscopy

Protein-bound arginine is commonly methylated in many proteins and regulates their function by altering the physicochemical properties, their interaction with other molecules, including other proteins or nucleic acids. This work presents an easily implementable protocol for quantifying arginine and its derivatives, including asymmetric and symmetric dimethylarginine (ADMA and SDMA, respectively) and monomethyl arginine (MMA). Following protein isolation from biological body fluids, tissues, or cell lysates, a simple method for homogenization, precipitation of proteins, and protein hydrolysis is described. Since the hydrolysates contain many other components, such as other amino acids, lipids, and nucleic acids, a purification step using solid-phase extraction (SPE) is essential. SPE can either be performed manually using centrifuges or a pipetting robot. The sensitivity for ADMA using the current protocol is about 100 nmol/L. The upper limit of detection for arginine is 3 mmol/L due to SPE saturation. In summary, this protocol describes a robust method, which spans from biological sample preparation to NMR-based detection, providing valuable hints and pitfalls for successful work when studying the arginine methylome.

The present protocol describes the preparation and quantitative measurement of free and protein-bound arginine and methyl-arginines by 1H-NMR spectroscopy.

Protein-bound arginine is commonly methylated in many proteins and regulates their function by altering the physicochemical properties, their interaction ...

Generation and Culturing of High-Grade Serous Ovarian Cancer Patient-Derived Organoids

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the discovery of novel therapeutics and predictive biomarkers for ovarian cancer. These models recapitulate clonal heterogeneity, the tumor microenvironment, and cell-cell and cell-matrix interactions. Additionally, they have been shown to match the primary tumor morphologically, cytologically, immunohistochemically, and genetically. Thus, organoids facilitate research on tumor cells and the tumor microenvironment and are superior to cell lines. The present protocol describes distinct methods to generate patient-derived ovarian cancer organoids from patient tumors, ascites, and pleural fluid samples with a higher than 97% success rate. The patient samples are separated into cellular suspensions by both mechanical and enzymatic digestion. The cells are then plated utilizing a basement membrane extract (BME) and are supported with optimized growth media containing supplements specific to the culturing of high-grade serous ovarian cancer (HGSOC). After forming initial organoids, the PDOs can sustain long-term culture, including passaging for expansion for subsequent experiments.

Patient-derived organoids (PDO) are a three-dimensional (3D) culture that can mimic the tumor environment in vitro. In high-grade serous ovarian cancer, PDOs represent a model to study novel biomarkers and therapeutics.

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the ...