Using TCGAbiolinks package in ranking breast cancer genes from The Cancer Genome Atlas (TCGA) to predict disease-associated genes

Predicting genes which may associate with disease is one of the important goals of biomedical research. There have been many computational methods developed to rank genes involved in a particular disease. However, due to the complex relationship between genes and the diseases, many genes that cause genetic diseases have not yet been discovered. The problem of ranking genes to identify the disease-associated gene has drawn attention of many researchers. The Genomic Data Commons (GDC) Data Portal is a platform that contains different cancer genomic studies. Such platforms have often the primary focus on the data storage and they do not provide a comprehensive toolkit for analyses. In this study, we used the new functions of the R/Bioconductor TCGAbiolinks package to search and analyze differentially expressed genes between breast cancer samples with primary solid tumors (TP) and solid tissue normal (NT) samples to retrieve list of 100 high-ranking genes associated with breast cancer.