Development of Compendium for Esophageal Squamous Cell Carcinoma

Resumo

This protocol describes a useful tool for identifying significant molecular changes in cancer and leads to the development of new diagnostic and therapeutic approaches for esophageal squamous cell carcinoma.

Resumo

Esophageal cancer (EC) ranks as the 8^th most aggressive malignancy, and its treatment remains challenging due to the lack of biomarkers facilitating early detection. EC manifests in two major histological forms - adenocarcinoma (EAD) and squamous cell carcinoma (ESCC) - both exhibiting variations in incidence across geographically distinct populations. High-throughput technologies are transforming the understanding of diseases, including cancer. A significant challenge for the scientific community is dealing with scattered data in the literature. To address this, a simple pipeline is proposed for the analysis of publicly available microarray datasets and the collection of differentially regulated molecules between cancer and normal conditions. The pipeline can serve as a standard approach for differential gene expression analysis, identifying genes differentially expressed between cancer and normal tissues or among different cancer subtypes. The pipeline involves several steps, including Data preprocessing (involving quality control and normalization of raw gene expression data to remove technical variations between samples), Differential expression analysis (identifying genes differentially expressed between two or more groups of samples using statistical tests such as t-tests, ANOVA, or linear models), Functional analysis (using bioinformatics tools to identify enriched biological pathways and functions in differentially expressed genes), and Validation (involving validation using independent datasets or experimental methods such as qPCR or immunohistochemistry). Using this pipeline, a collection of differentially expressed molecules (DEMs) can be generated for any type of cancer, including esophageal cancer. This compendium can be utilized to identify potential biomarkers and drug targets for cancer and enhance understanding of the molecular mechanisms underlying the disease. Additionally, population-specific screening of esophageal cancer using this pipeline will help identify specific drug targets for distinct populations, leading to personalized treatments for the disease.

Introdução

It is alarming that EC is the eighth most common cancer worldwide and the sixth leading cause of death worldwide. China, India, and Iran have alarmingly high incidence and mortality rates. There are two main types of EC: esophageal adenocarcinoma (EAC or EAD), and esophageal squamous cell carcinoma (ESCC)¹. EAC is more common in the Western world, whereas ESCC is more common in Eastern countries, especially China and Iran². Several risk factors are associated with EC, including tobacco and alcohol use, obesity, and gastroesophageal reflux disease (GERD). Additionally, dietary factors such as lack of fruits and vegetables

Protocolo

1. Manual curation of the differentially regulated molecules in ESCC

1. Finding relevant low-throughput studies using PubMed
   NOTE: It is important to understand the basic difference between low-throughput versus high-throughput techniques. In the former, only a limited number of samples are studied, and the process is usually time-consuming, on contrast later is faster and the number of samples can be analyzed in one go which is significantly higher than in low-throughput methods such as Northern blot, and Western blot are low-throughput techniques, while cDNA microarray, and LC-MS/MS based quantitative proteomics are high-throughput t

Resultados

As an example, GEO accession GSE161533 was used to study differentially explored genes in ESCC. The representative results of the analysis have been shown in the Figure 3. GEO2R generates a volcano plot that is useful for identifying events that differ significantly between two groups of experimental subjects. Volcano plot presents overall gene distribution with -log₁₀ transformed significance (p-value) on the y-axis, and fold changes (with log₂ transformed fol...

Discussão

Since the involvement of high-throughput OMICS techniques in cancer biology, the rate of generation of data has been significantly increased. This poses a challenge for researchers especially those without a computer-savvy nature. To overcome over the years bioinformaticians come up with the idea of developing a database to provide data in an organized manner. This generated a positive response from researchers, especially those who are not interested in technology. Furthermore, scattered OMICS data here and there in the...

Materiais

Name	Company	Catalog Number	Comments
NCBI-PUBMED	NCBI	https://ncbi.nlm.nih.gov/pubmed	Referring to section 1. required for searching the literature
A laptop/macbook or personal computer with internet facility and a web browser.
g:Profiler	ELIXIR infrastructure	https://biit.cs.ut.ee/gprofiler/gost	Referring to section 4.10. required for enrichment of GO:MF, GO:BP, and GO:CC
Gene expression omnibus	NCBI	https://www.ncbi.nlm.nih.gov/geo/	Referring to section 3.1. required for searching the microarray study database
GEO2R	NCBI	https://www.ncbi.nlm.nih.gov/geo/geo2r/	Referring to section 3.2. required for analyzing the data using GEO2R tool
Google	Google	https://www.google.com	Referring to section 1.1. required for searching the literature
HGNC	HGNC is a committee of the Human Genome Organisation (HUGO)	https://www.genenames.org	Referring to section 6.1 required to know the official gene symbol of the DEGs
HPRD	Institute of Bioinformatics, Bangluru	http://hprd.org	Referring to section 5.1 required for informationn about protein architecture
OMIM	Johns Hopkins University, Baltimore	http://www.omim.org/entry	Referring to section 8.1 required to know the OMIM ID of a particular gene / DEG
Pangloss Program	Developed by Chris Seidel	http://www.pangloss.com/seidel/Protocols/venn.cgi	Referring to section 4.9. required for generating the Venn diagram
PANTHER	Thomas lab at the University of Southern California	http://www.pantherdb.org/geneListAnalysis.do	Referring to section 4.10. required for enrichment of GO:MF, GO:BP, and GO:CC
ShinyGO	South Dakota State University	http://bioinformatics.sdstate.edu/go	Referring to section 4.10. required for allocation of DEGs on the chromosomes