Abstract
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.
Highlights
Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women
Targeted therapies have been developed for epidermal growth factor receptor (EGFR), BRCA1/2-mutated polyadenosine diphosphate ribose polymerase (PARP), cyclin-dependent kinase 4/6 (CDK4/6), BTB and CNC homology[1] (BACH1), and so o n14–18
Gene ontology (GO) enrichment and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed on the differentially expressed genes (DEGs) using the DAVID database
Summary
Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. Several genes have been identified as predictive and prognostic biomarkers for breast cancer, which play important roles in targeted therapy. Vascular endothelial growth factor (VEGF) has been identified as a key target for anti-angiogenic therapy, and its inhibitors bevacizumab, sorafenib, and sunitinib are used for breast cancer t herapy[11,12]. Because of tumor heterogeneity, low ratios of responders, relapse and drug resistance, there is still an urgent need to identify new biomarkers that may aid the diagnosis and treatment of breast cancer. We performed functional and pathway enrichment analyses of the identified DEGs using the DAVID database. Conduct module analyses of the PPI network were performed using MCODE The associations of these genes with OS were determined using an online Kaplan–Meier analysis tool. Several breast cancer-related molecules were selected to investigate their potential role in a breast cancer diagnostic system
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