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

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.