Open Access

Bioinformatics analysis of gene expression data for the identification of critical genes in breast invasive carcinoma

  • Authors:
    • Yi Li
    • Yongsheng Wang
  • View Affiliations

  • Published online on: October 4, 2017     https://doi.org/10.3892/mmr.2017.7717
  • Pages:8657-8664
  • Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Gene expression data were analyzed in order to identify critical genes in breast invasive carcinoma (BRCA). Data from 1,073 BRCA samples and 99 normal samples were analyzed, which were obtained from The Cancer Genome Atlas. Differentially expressed genes (DEGs) were identified using the significance analysis of microarrays method and a functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery. Relevant microRNAs (miRNAs), transcription factors (TFs) and associated small molecule drugs were revealed by Fisher's exact test. Furthermore, protein‑protein interaction (PPI) information was downloaded from the Human Protein Reference Database. Interactions with a Pearson's correlation coefficient >0.5 were identified and PPI networks were subsequently constructed. A survival analysis was also conducted according to the Kaplan‑Meier method. Initially, the 1,073 BRCA samples were clustered into seven groups, and 5,394 DEGs that were identified in ≥4 groups were selected. These DEGs were involved in the cell cycle, ubiquitin‑mediated proteolysis, oxidative phosphorylation and human immunodeficiency virus infection. In addition, TFs, including Sp1 transcription factor, DAN domain BMP antagonist family member 5, MYCN proto‑oncogene, bHLH transcription factor and cAMP responsive element binding protein (CREB)1, were identified in the BRCA groups. Seven PPI networks were subsequently constructed and the top 10 hub genes were acquired, including RB transcriptional corepressor 1, inhibitor of nuclear factor (NF)‑κB kinase subunit γ, NF‑κB subunit 2, transporter 1, ATP binding cassette subfamily B member, CREB binding protein and proteasome subunit α3. A significant difference in survival was observed between the two combined groups (groups‑2, ‑4 and ‑5 vs. groups‑1, ‑3, ‑6 and ‑7). In conclusion, numerous critical genes were detected in BRCA, and relevant miRNAs, TFs and small molecule drugs were identified. These findings may advance understanding regarding the pathogenesis of BRCA.

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December 2017
Volume 16 Issue 6

Print ISSN: 1791-2997
Online ISSN:1791-3004

2016 Impact Factor: 1.692
Ranked #19/128 Medicine Research and Experimental
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APA
Li, Y., & Li, Y. (2017). Bioinformatics analysis of gene expression data for the identification of critical genes in breast invasive carcinoma. Molecular Medicine Reports, 16, 8657-8664. https://doi.org/10.3892/mmr.2017.7717
MLA
Li, Y., Wang, Y."Bioinformatics analysis of gene expression data for the identification of critical genes in breast invasive carcinoma". Molecular Medicine Reports 16.6 (2017): 8657-8664.
Chicago
Li, Y., Wang, Y."Bioinformatics analysis of gene expression data for the identification of critical genes in breast invasive carcinoma". Molecular Medicine Reports 16, no. 6 (2017): 8657-8664. https://doi.org/10.3892/mmr.2017.7717