LAUSR

Breast cancer is a serious malignancy with a high incidence worldwide and a tendency to relapse. The aim of the present study was to identify potential biomarkers in breast cancer using integrated bioinformatics analysis. Microarray data for 127 breast tumor samples and 23 non-tumor samples were downloaded from the Gene Expression Omnibus database; 121 differentially expressed genes were identified. Functional analysis using DAVID revealed that these genes were significantly enriched in endodermal cell differentiation and proteinaceous extracellular matrix. Five bioactive compounds (prostaglandin J2, tanespimycin, semustine, 5182598, and flunarizine) were identified using Connectivity Map. Cytoscape software and the STRING database were used to construct a protein-protein interaction network. Kaplan-Meier Plotter, ONCOMINE, and Gene Expression Profiling Interactive Analysis showed that overexpression of CD24, MMP1, SDC1, and SPP1 was related to poor prognosis in breast cancer patients. The immunohistochemistry microarray was used to further confirm that protein expression of CD24, MMP1, SDC1, and SPP1 was much higher in tumor tissues than in non-tumor tissues. Expression of hub genes at the protein level was correlated with subtype and grade of breast cancer. Furthermore, immunity analysis indicated that the hub genes were significantly correlated with the infiltration of five immune cell types (CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells). Thus, the results of this study indicate potential biomarkers that could have applications in the development of immune therapy for breast cancer. However, more in vivo, and in vitro studies are required to further verify these results.