In-Silico Analysis Of Differentially Expressed Genes And Their Pathway Analysis In Prostate Cancer

About Prostate Cancer 

Prostate cancer is a form of cancer that starts in the gland cells of the prostate. It is the second most common cancer diagnosis in men and the fifth leading cause of death globally. As -omics technologies such as genomics, and transcriptomics continue to improve our understanding of the molecular complexity of various cancers, Dhaval Kumar Srivastava, a Research Fellow at Pine Biotech carried out an in-silico transcriptomics analysis to identify tumor biomarkers for early detection and risk assessment of prostate cancer.


OmicsLogic Bioinformatics Research Fellowship Program

Being cancer biology and data science enthusiast, Dhaval Kumar Srivastava, a student of M.Tech Biotechnology, Amity University, Noida, India opted for the OmicsLogic Bioinformatics Research Fellowship Program at Pine Biotech. He completed his research project under the guidance of Dr. Raghavendran Lakshminarayanan, Research Consultant and Mentor at Pine Biotech, 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, Dhaval Kumar’s OmicsLogic Learn profile and learn more about the courses and projects he has completed, visit the link - 


Project Overview: In-Silico Analysis Of Differentially Expressed Genes And Their Pathway Analysis In Prostate Cancer

For the research project, RNA-Seq data was collected from The Cancer Genome Atlas (TCGA) and consisted of normal samples, and prostate cancer samples. A correlation boxplot was obtained to check the outliers that could be run. The dataset was then quantile normalized and log-transformed. Followed by this, 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. 


Gene Matrix Expression

Figure: A gene expression matrix: (a) An array-array intensity correlation plot, defining the outliers that can be run and (b) Outlier Correlation Boxplot.

The set of genes that were found to be differentially expressed, were FABP4, AQP7, LEP, LIPE, ADIPOQ, and CD36. The CD36 gene holds the potential to serve as a diagnostic test for the purpose of early detection of prostate cancer. Finally, he also concluded that the PPRAG transcription factor, in association with MAPK1 and MAPK3 kinases can also be considered as potential proteomic biomarkers to facilitate the diagnosis of Prostate cancer. However, to understand if they can be used in fusion with some other genes to increase their selectivity and specificity,  further analysis needs to be performed.

To learn more about the findings from his project, visit - 


Here is what Dhaval Kumar had to say about his research fellowship experience: 




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 Internship


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:   


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