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Updated: Feb 19, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Identification of breast cancer hub genes and analysis of prognostic values using integrated bioinformatics analysis
Background:
Breast cancer (BC) is the second most common cause of death from cancer in women in the United States. As the molecular mechanism of BC has not yet been completely discovered, identification of hub genes and pathways of this disease is of importance for revealing molecular mechanism of breast cancer initiation and progression.
Objective:
This study aimed to identify potential biomarkers and survival analysis of hub genes for BC treatment.
Methods:
The differentially expressed genes (DEGs) between breast cancer and normal cells were screened using microarray data obtained from the Gene Expression Omnibus (GEO) database. Gene ontology (GO) and KEGG pathway enrichment analyses were performed for DEGs using DAVID database, the protein-protein interaction (PPI) network was constructed using the Cytoscape software, and module analysis was performed using MCODE. Then, overall survival (OS) analysis of hub genes was performed by the Kaplan-Meier plotter online tool. Finally, the potential molecular agents were identified with Connectivity Map (cMap) database.
Results:
A total of 585 DEGs were obtained, which were significantly enriched in the terms related to positive regulation of cell migration, regulation of cell proliferation and focal adhesion. KEGG pathway analysis showed that the significant pathways included Focal adhesion, Pathways in cancer, ECM-receptor interaction, Ribosome, Transcriptional misregulation in cancer and other signaling pathways about cancer. The PPI network was established with 576 nodes and 1943 edges. A significant module was found from the PPI network, the enriched functions and pathways included ECM-receptor interaction and Focal adhesion.
Conclusions:
Fifteen genes were selected as hub genes because of high degrees, among which, low expression of four genes was associated with worse OS of patients with BC, including RPS9, RPL11, RPS14 and RPL10A. Additionally, the small molecular agent emetine may be a potential drug for BC.
