Transcriptomic Analysis of Histological Subtypes in Esophageal Carcinoma

About Esophageal Carcinoma

Did you know esophageal carcinoma is the sixth most common cause of cancer death globally? Carcinoma is a type of cancer that arises in the tissues lining or covering the internal organs of the body. When cancer cells develop in the inner lining of the esophagus – a tube that connects your throat to your stomach, they are referred to as esophageal carcinoma

Based on the type of cells from which cancer originates, there are two subtypes of esophageal carcinoma. While esophageal squamous cell carcinoma arises from the squamous cells lining the inner esophagus, esophageal adenocarcinoma starts in glandular cells and occurs in the lower esophagus near the stomach.  

Even with two distinct histological subtypes, the therapeutic approaches to managing the disease remain the same. To get insights into the differentially expressed genes and pathways involved, Dhruv Mehra, a Research Fellow at Pine Biotech performed a research project that aims to compare the transcriptomic profile of the esophageal carcinoma subtypes. 

 

OmicsLogic Bioinformatics Research Fellowship Program

Being a precision medicine enthusiast, Dhruv Mehra, a student of M.Sc. Precision Medicine at the University of Manchester, United Kingdom opted for the OmicsLogic Bioinformatics Research Fellowship Program at Pine Biotech to conduct a research project on the transcriptomic analysis of histological subtypes in esophageal carcinoma. 

He completed his research project under the guidance of Dr. Harpreet Kaur, Research Consultant, and Mentor at Pine Biotech, Dr. Mohit Mazumder, Global Business Development Head, and Mr. Elia Brodsky, CEO of Pine Biotech.  

The OmicsLogic Learn Portal offers several courses and example projects on the introductory and advanced level concepts in bioinformatics. To gain an introductory knowledge of the various roles of big data bioinformatics in genomics, transcriptomics, metagenomics, precision medicine, space omics, and R programming language, he completed the following courses on the portal:

After completing the introductory courses, he went on to complete courses on several in-depth topics ranging from: 

Further, he also completed a few example projects related to precision medicine: 

To view Dhruv’s OmicsLogic Learn profile and learn more about the courses and projects he has completed, visit the link - https://learn.omicslogic.com/user/omNKs1Av4EelHTWc96cjnLgcpGm1 

 

Project Overview: Transcriptomic Analysis of Histological Subtypes in Esophageal Carcinoma

For the research project, Esophageal Carcinoma RNA-Seq data was collected from The Cancer Genome Atlas (TCGA) and consisted of normal samples, esophageal squamous cell carcinoma samples, and esophageal adenocarcinoma samples. 

The dataset was first quantile normalized and log-transformed. Followed by this, the principal component analysis was done to visualize clustering based on the cancer subtype. Then, differential gene expression analysis and pathway enrichment analysis were carried out to study the differentially expressed genes enriched in various biological pathways. All the bioinformatics tools used for the study are available on the T-BioInfo Platform – a bioinformatics platform that combines statistical analysis modules into pipelines to deal with heterogeneous big data. 

 

Volcano Plot

Figure - Volcano plot of differentially expressed genes in Esophageal Squamous Cell Carcinoma generated by DeSEQ2 pipeline on the T-BioInfo Platform  

 

The results revealed a number of microRNAs (miRNA) and long noncoding RNAs (lncRNA) to be differentially expressed between the two histological subtypes. For instance, miR-4261 was found to be upregulated in esophageal adenocarcinoma samples. This is interesting since miR-4261 has been proven to be a probable drug target, whose suppression in colorectal cancer had a therapeutic effect. Likewise, dysregulation of the gene LINC00470 was found in esophageal squamous cell carcinoma samples. This lncRNA has been reported to play an important role in chemoresistance in chronic myelocytic leukemia. The results obtained in the study can be used in the development of common or subtype-specific therapeutic approaches or can be used as diagnostic or prognostic biomarkers.  

To learn more about the findings from his project, visit - https://learn.omicslogic.com/projects/omNKs1Av4EelHTWc96cjnLgcpGm1/transcriptomic-analysis-of-histological-subtypes-in-esophageal-adenocarcinoma

 

Here is what Dhruv Mehra had to say about his research fellowship experience: 

 

Testimonial

 

Are you feeling motivated to work on your own research project? Then don't miss the chance to register yourself for the OmicsLogic Bioinformatics Research Fellowship Program

 

Research fellowship Program

 

The OmicsLogic Bioinformatics Research Fellowship Program is a structured program that guides students through various areas of Big Data Bioinformatics Research using practical examples. During this program, we go through several high-quality research publications and learn about applications of computational biology in projects these publications describe. This allows beginners to try computational biology techniques on public domain data, making it possible to work with large files and extract meaningful information from patient samples, animal models, cell lines, and microbiota. The program provides support and mentorship, therefore it is an intensive research program involving training tasks, access to our online sessions, and a guide on the implementation of learned skills to proposed research ideas. 

To learn more about the program, please visit the link: https://learn.omicslogic.com/blog/post/research-fellowship-with-pine-biotech-independent-research-projects-using-bioinformatics   

 

Reference Links  

  1. Course 1: Introduction to Bioinformatics - https://learn.omicslogic.com/courses/course/course-1-introduction-to-bioinformatics 
  2. Course 2: Bytes and Molecules - https://learn.omicslogic.com/courses/course/course-2-bytes-and-molecules 
  3. Course 3: Genomics - https://learn.omicslogic.com/courses/course/course-3-genomics 
  4. Course 4: Metagenomics - https://learn.omicslogic.com/courses/course/course-4-metagenomics 
  5. Course 5: Transcriptomics - https://learn.omicslogic.com/courses/course/course-5-transcriptomics 
  6. Course 6: Single Cell Transcriptomics - https://learn.omicslogic.com/courses/course/course-6-single-cell-transcriptomics 
  7. Course 7: BioML-Machine Learning for Biomedical Data - https://learn.omicslogic.com/courses/course/course-7-bioml-machine-learning-for-biomedical-data 
  8. Course 9: Designing a Bioinformatics Research Project - https://learn.omicslogic.com/courses/course/course-9-designing-a-bioinformatics-research-project  
  9. Project 03: TCGA Liver Cancer - Precision Oncology - https://learn.omicslogic.com/courses/course/project-03-tcga-liver-cancer-precision-oncology 
  10. Project 05: Modeling Cancer Precision Medicine - https://learn.omicslogic.com/courses/course/project-05-modeling-cancer-precision-medicine 
  11. Project 06: Patient -Derived Xenograft Models - https://learn.omicslogic.com/courses/course/project-06-patient-derived-xenograft-models 
  12. Project 07: Changing Immune Response in Cancer - https://learn.omicslogic.com/courses/course/project-07-changing-immune-response-in-cancer