<p>Identification of key pathways and hub genes in basal-like breast cancer using bioinformatics analysis</p>

• DOI: 10.2147/ott.s158619
• Publication Date: 2019-02-17T16:51:41Z
• Authors: Kaidi Yang; Jian Gao; Mao Luo
• Keywords: KEGG

Background: Basal-like breast cancer (BLBC) is the most aggressive subtype of (BC) and links to poor outcomes. As molecular mechanism BLBC has not yet been completely discovered, identification key pathways hub genes this disease an important way for providing new insights into exploring mechanisms initiation progression. Objective: The aim study was identify potential gene signatures development progression via bioinformatics analysis. Methods results: differential expressed (DEGs) including 40 up-regulated 21 down-regulated DEGs were identified between GSE25066 GSE21422 microarrays, these significantly enriched in terms related oncogenic or suppressive roles In addition, KEGG pathway GSEA (Gene Set Enrichment Analysis) enrichment analyses performed basal type non-basal-type from microarray. These such as cell cycle, cytokine-cytokine receptor interaction, chemokine signaling pathway, central carbon metabolism TNF pathway. Moreover, protein-protein interaction (PPI) network constructed with those 61 using Cytoscape software, biological significance putative modules established MCODE. module 1 found be closely a term mitosis regulation cycle thus confirmed pathological characteristic high mitotic index. Furthermore, prediction values top 10 CCNB2 , BUB1 NDC80 CENPE KIF2C TOP2A MELK TPX2 CKS2 KIF20A validated Oncomine Kaplan-Meier plotter. Conclusion: Our results suggest intriguing possibility that PPI contributed in-depth knowledge about BLBC, paving more accurate discovery treatment targets patients. Keywords: basal-like cancer, bioinformatics, differentially genes,