Bioinformatics-Driven Investigations of Signature Biomarkers for Triple-Negative Breast Cancer.

Abstract

Breast cancer is a highly heterogeneous disorder characterized by dysregulated expression of number of genes and their cascades. It is one of the most common types of cancer in women posing serious health concerns globally. Recent developments and discovery of specific prognostic biomarkers have enabled its application toward developing personalized therapies. The basic premise of this study was to investigate key signature genes and signaling pathways involved in triple-negative breast cancer using bioinformatics approach. Microarray data set GSE65194 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus was used for identification of differentially expressed genes (DEGs) using R software. Gene ontology and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses were carried out using the ClueGO plugin in Cytoscape software. The up-regulated DEGs were primarily engaged in the regulation of cell cycle, overexpression of spindle assembly checkpoint, and so on, whereas down-regulated DEGs were employed in alteration to major signaling pathways and metabolic reprogramming. The hub genes were identified using cytoHubba from protein-protein interaction (PPI) network for top up-regulated and down-regulated DEG's plugin in Cytoscape software. The hub genes were validated as potential signature biomarkers by evaluating the overall survival percentage in breast cancer patients.