Identification of Potential Non-invasive Biomarkers for Breast Cancer Prognosis and Treatment by Systematic Bioinformatics Analysis

Abstract

Objective: To observe the changes of gene expression in breast cancer stroma and peripheral blood mononuclear cells(PBMCs) of breast cancer patients. To investigate similarities and differences between them. Method: Datasets of gene expression profilings were downloaded from the Gene Expression Omnibus (GEO) database, including profilings of breast cancer vs. normal stroma and breast cancer patients' vs. healthy volunteers' PBMCs. BRB-Array Tools was used to analyze the data to identify the differentially-expressed genes (DEGs). Function of DEGs were annotated by the Database for Annotation, Visualization and Integrated Discovery (DAVID). Protein interaction analysis were then performed for the commonly deregulated genes. Results: 1565 and 1382 DEGs respectively were identified. Genes up-regulated in the two dataset involved in biological processes, such as cell cycle, protein kinase cascade, negative regulation of programmed cell death, vasculature development.84 common genes were selected (74 up-and 10 down-regulated) to constructed the protein-protein interaction (PPI)network, from which the hub genes, including JUN, FOS, FOSB, early growth response 1 (EGR1), dual specificity phosphatase 1 (DUSP1)were extracted. Conclusion: The data suggests that gene expression pattern of these two profilings are similar at a certain degree. PBMCs maybe a better noninvasive material for biomarker detection of breast cancer.