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

Summary

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.

Abstract

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.

Introduction

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 cancers^1,2,3,4,5,6. In some circumstances, the Notch signaling pathway has been linked to both tissue growth and cancer as well as cell death and tumor suppression

Protocol

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‒Z analysis, which incorporates large datasets to provide Z‒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‒score values, respectively.

1. Create an account with an academic affiliated email to access this datab....

Results

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‒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‒score values are shown in a colored bar graph in Figure 1.

The CS.......

Discussion

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.......

Materials

List of materials used in this article

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