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

Authors Yang K, Gao J, Luo M

Received 1 December 2017

Accepted for publication 7 February 2018

Published 18 February 2019 Volume 2019:12 Pages 1319—1331

DOI https://doi.org/10.2147/OTT.S158619

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Cristina Weinberg

Peer reviewer comments 3

Editor who approved publication: Dr Ingrid Espinoza


Kaidi Yang,1,2 Jian Gao,3 Mao Luo1,4,5

1Key Laboratory of Medical Electrophysiology of Ministry of Education, Collaborative Innovation Center for Prevention and Treatment of Cardiovascular Disease of Sichuan Province, Southwest Medical University, Luzhou, China; 2Key Laboratory of Tumor Immunology, The First Affiliated Hospital, Army Medical University, Chongqing, China; 3Department of Life Sciences and Technology, Yangtze Normal University, Chongqing, China; 4Drug Discovery Research Center, Southwest Medical University, Luzhou, Sichuan, China; 5Laboratory for Cardiovascular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou, Sichuan, China

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

Keywords: basal-like breast cancer, bioinformatics, differentially expressed genes, hub genes, molecular mechanism
 

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