Mining TCGA Database for Genes with Prognostic Value in Breast Cancer

The aim of the study was to use transcriptomics data to identify genes associated with advanced/aggressive breast cancer and their effect on survival outcomes. We used the publicly available The Cancer Genome Atlas (TCGA) database to obtain RNA sequence data from patients with less than five years survival (Poor Prognosis, n = 101), patients with greater than five years survival (Good Prognosis, n = 200), as well as unpaired normal tissue data (normal, n = 105). The data analyses performed included differential expression between groups and selection of subsets of genes, gene ontology, cell enrichment analysis, and survival analyses. Gene ontology results showed significantly reduced enrichment in gene sets related to tumor immune microenvironment in Poor Prognosis and cell enrichment analysis confirmed significantly reduced numbers of macrophages M1, CD8 T cells, plasma cells and dendritic cells in samples in the Poor Prognosis samples compared with Good Prognosis. A subset of 742 genes derived from differential expression analysis as well as genes coding for immune checkpoint molecules was evaluated for their effect on overall survival. In conclusion, this study may contribute to the better understanding of breast cancer transcriptomics and provide possible targets for further research and eventual therapeutic interventions.

Automated summary

The study aimed to identify genes associated with advanced/aggressive breast cancer and their effect on survival outcomes using transcriptomics data. The publicly available The Cancer Genome Atlas (TCGA) database was used to analyze RNA sequence data from patients with different survival periods, as well as normal tissue data. The results showed reduced enrichment in gene sets related to tumor immune microenvironment in the group with poor prognosis, indicating potential targets for further research and therapeutic interventions.