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Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer

  • Authors:
    • Jinguang Wang
    • Qi Du
    • Chen Li
  • View Affiliations / Copyright

    Affiliations: Department of Thyroid and Breast Surgery, Linyi People's Hospital, Linyi, Shandong 272100, P.R. China, Department of Prevention and Health Care, Lanshan District Center for Disease Control and Prevention, Linyi, Shandong 276000, P.R. China, Department of General Surgery, Linyi People's Hospital, Linyi, Shandong 272100, P.R. China
    Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 7880-7888
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    Published online on: October 23, 2017
       https://doi.org/10.3892/ol.2017.7256
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Abstract

Patients with the luminal B subtype of breast cancer exhibit a poor prognosis, high metastatic risk and high incidence of chemotherapy resistance. Luminal B breast cancer is sub‑classified into B1 and B2. The pathophysiological mechanism of luminal B2 breast cancer (LB2BC) progression has yet to be characterized. Therefore, the present study aimed to identify the genes involved in the pathogenesis of LB2BC. The data of 117 LB2BC expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and differentially expressed genes (DEGs) were identified by comparison with non‑tumor tissue expression profiles. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein‑protein interaction (PPI) networks were used to obtain insight into the functions of DEGs. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) analysis was performed to validate the expression level of DEGs in tissue samples. A total of 2,251 DEGs, including 759 upregulated and 1,492 downregulated genes, were identified between LB2BC and non‑tumor tissues. The top 15 upregulated and downregulated genes were used to construct a PPI network: Epidermal growth factor receptor (EGFR), fibronectin‑1 (FN1) and Polo‑like kinase‑1 had the highest connectivity degrees. KEGG analysis identified that DEGs were most significantly enriched in ‘focal adhesion’, ‘pathways in cancer’ and ‘ECM‑receptor interaction’ pathways. The results of RT‑qPCR demonstrated that EGFR was significantly downregulated in LB2BC, whereas FN1 was significantly upregulated, whereas neurotrophic receptor tyrosine kinase 2 (NTRK2) trended towards downregulation. In conclusion, the DEGs identified in the present study, including NTRK2, FN1 and EGFR, may serve pivotal roles in the tumorigenesis of LB2BC by affecting the ‘focal adhesion’, ‘pathways in cancer’ and ‘ECM‑receptor interaction’ KEGG pathways.
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Copy and paste a formatted citation
Spandidos Publications style
Wang J, Du Q and Li C: Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer. Oncol Lett 14: 7880-7888, 2017.
APA
Wang, J., Du, Q., & Li, C. (2017). Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer. Oncology Letters, 14, 7880-7888. https://doi.org/10.3892/ol.2017.7256
MLA
Wang, J., Du, Q., Li, C."Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer". Oncology Letters 14.6 (2017): 7880-7888.
Chicago
Wang, J., Du, Q., Li, C."Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer". Oncology Letters 14, no. 6 (2017): 7880-7888. https://doi.org/10.3892/ol.2017.7256
Copy and paste a formatted citation
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Spandidos Publications style
Wang J, Du Q and Li C: Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer. Oncol Lett 14: 7880-7888, 2017.
APA
Wang, J., Du, Q., & Li, C. (2017). Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer. Oncology Letters, 14, 7880-7888. https://doi.org/10.3892/ol.2017.7256
MLA
Wang, J., Du, Q., Li, C."Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer". Oncology Letters 14.6 (2017): 7880-7888.
Chicago
Wang, J., Du, Q., Li, C."Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer". Oncology Letters 14, no. 6 (2017): 7880-7888. https://doi.org/10.3892/ol.2017.7256
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