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Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer

  • Authors:
    • Yuzhi Wang
    • Yi Zhang
    • Qian Huang
    • Chengwen Li
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    Affiliations: School of Basic Medical Sciences, Southwest Medical University, Luzhou, Sichuan 646000, P.R. China
    Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 8091-8100
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    Published online on: April 19, 2018
       https://doi.org/10.3892/mmr.2018.8895
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Abstract

Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non‑tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t‑tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein‑protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in ʻcell divisionʼ, the ʻproteinaceous extracellular matrix (ECM)ʼ, ʻECM structural constituentsʼ and ʻECM‑receptor interactionʼ, whereas downregulated genes were mainly enriched in ʻresponse to drugsʼ, ʻextracellular spaceʼ, ʻtranscriptional activator activityʼ and the ʻperoxisome proliferator‑activated receptor signaling pathwayʼ. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2‑a, baculoviral inhibitor of apoptosis repeat‑containing protein 5, cyclin‑dependent kinase 1, G2/mitotic‑specific cyclin‑B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in ʻmitotic nuclear divisionʼ, ʻmid‑bodyʼ, ʻprotein bindingʼ and ʻcell cycleʼ. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC.
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Copy and paste a formatted citation
Spandidos Publications style
Wang Y, Zhang Y, Huang Q and Li C: Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer. Mol Med Rep 17: 8091-8100, 2018.
APA
Wang, Y., Zhang, Y., Huang, Q., & Li, C. (2018). Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer. Molecular Medicine Reports, 17, 8091-8100. https://doi.org/10.3892/mmr.2018.8895
MLA
Wang, Y., Zhang, Y., Huang, Q., Li, C."Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer". Molecular Medicine Reports 17.6 (2018): 8091-8100.
Chicago
Wang, Y., Zhang, Y., Huang, Q., Li, C."Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer". Molecular Medicine Reports 17, no. 6 (2018): 8091-8100. https://doi.org/10.3892/mmr.2018.8895
Copy and paste a formatted citation
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Spandidos Publications style
Wang Y, Zhang Y, Huang Q and Li C: Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer. Mol Med Rep 17: 8091-8100, 2018.
APA
Wang, Y., Zhang, Y., Huang, Q., & Li, C. (2018). Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer. Molecular Medicine Reports, 17, 8091-8100. https://doi.org/10.3892/mmr.2018.8895
MLA
Wang, Y., Zhang, Y., Huang, Q., Li, C."Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer". Molecular Medicine Reports 17.6 (2018): 8091-8100.
Chicago
Wang, Y., Zhang, Y., Huang, Q., Li, C."Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer". Molecular Medicine Reports 17, no. 6 (2018): 8091-8100. https://doi.org/10.3892/mmr.2018.8895
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