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Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. Increasing molecular targets have been discovered for breast cancer prognosis and therapy. However, there is still an urgent need to identify new biomarkers. Therefore, we evaluated biomarkers that may aid the diagnosis and treatment of breast cancer. We searched three mRNA microarray datasets (GSE134359, GSE31448 and GSE42568) and identified differentially expressed genes (DEGs) by comparing tumor and non-tumor tissues using GEO2R. Functional and pathway enrichment analyses of the DEGs were performed using the DAVID database. The protein–protein interaction (PPI) network was plotted with STRING and visualized using Cytoscape. Module analysis of the PPI network was done using MCODE. The associations between the identified genes and overall survival (OS) were analyzed using an online Kaplan–Meier tool. The redundancy analysis was conducted by DepMap. Finally, we verified the screened HUB gene at the protein level. A total of 268 DEGs were identified, which were mostly enriched in cell division, cell proliferation, and signal transduction. The PPI network comprised 236 nodes and 2132 edges. Two significant modules were identified in the PPI network. Elevated expression of the genes Discs large-associated protein 5 ( DLGAP5 ), aurora kinase A ( AURKA ), ubiquitin-conjugating enzyme E2 C ( UBE2C ), ribonucleotide reductase regulatory subunit M2( RRM2 ), kinesin family member 23( KIF23 ), kinesin family member 11( KIF11 ), non-structural maintenance of chromosome condensin 1 complex subunit G ( NCAPG ), ZW10 interactor ( ZWINT ), and denticleless E3 ubiquitin protein ligase homolog( DTL)  are associated with poor OS of breast cancer patients. The enriched functions and pathways included cell cycle, oocyte meiosis and the p53 signaling pathway. The DEGs in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

Screening and predicted value of potential biomarkers for breast cancer using bioinformatics analysis