Functional and Network Exploration of RNA Seq data of Breast Cancer

Abstract

This study comprised of RNASeq data analysis of breast cancer. It includes statistical, functional and network analysis by various bioinformatics tools. Breast cancer is the most frequent cancer in women and affects everyone, including the young and elderly, rich and poor, women and children. Objective: To explore dataset of breast cancer, network and functional wise. Although there is extensive research on breast cancer, in silico studies on this topic are very rare. Methods: The study makes use of GEO (Gene Expression Omnibus) database from where data was collected. The data obtained of Breast cancer samples was normalized for which R language was used (using Limma, RPKM values) which eventually gave differentially expressed genes which were mainly involved in causing this Breast cancer and up- and down-regulatory genes were found using logFC values. Then functional analysis of these up- and down-regulated genes was performed using David Software. Then network analysis was performed, which showed the co-relation between the genes in making this Breast cancer prevalent in patients. Finally, importance of our genes was studied by using cBioPortal database. Results: Six important and novel genes were identified as differentially expressing through R software. Functional and network analysis and their significance studied by cBioportal dictated several potential genes taking part in important cancer and other pathways paving way for further research. Conclusions:  The pathways and candidate genes were selected based on high enrichment score and these genes and pathways play a significant role in breast cancer.