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Integrated bioinformatics analysis to identify 15 hub genes in breast cancer

  • Authors:
    • Haoxuan Jin
    • Xiaoyan Huang
    • Kang Shao
    • Guibo Li
    • Jian Wang
    • Huanming Yang
    • Yong Hou
  • View Affiliations / Copyright

    Affiliations: BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, Guangdong 518083, P.R. China, BGI‑Shenzhen, Shenzhen, Guangdong 518083, P.R. China
    Copyright: © Jin et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 1023-1034
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    Published online on: May 30, 2019
       https://doi.org/10.3892/ol.2019.10411
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Abstract

The aim of the present study was to identify the hub genes and provide insight into the tumorigenesis and development of breast cancer. To examine the hub genes in breast cancer, integrated bioinformatics analysis was performed. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the ‘limma’ package in R. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis was used to determine the functional annotations and potential pathways of the DEGs. Subsequently, a protein‑protein interaction network analysis and weighted correlation network analysis (WGCNA) were conducted to identify hub genes. To confirm the reliability of the identified hub genes, RNA gene expression profiles were obtained from The Cancer Genome Atlas (TCGA)‑breast cancer database, and WGCNA was used to screen for genes that were markedly correlated with breast cancer. By combining the results from the GEO and TCGA datasets, 15 hub genes were identified to be associated with breast cancer pathophysiology. Overall survival analysis was performed to examine the association between the expression of hub genes and the overall survival time of patients with breast cancer. Higher expression of all hub genes was associated with significantly shorter overall survival in patients with breast cancer compared with patients with lower levels of expression of the respective gene.
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Copy and paste a formatted citation
Spandidos Publications style
Jin H, Huang X, Shao K, Li G, Wang J, Yang H and Hou Y: Integrated bioinformatics analysis to identify 15 hub genes in breast cancer. Oncol Lett 18: 1023-1034, 2019.
APA
Jin, H., Huang, X., Shao, K., Li, G., Wang, J., Yang, H., & Hou, Y. (2019). Integrated bioinformatics analysis to identify 15 hub genes in breast cancer. Oncology Letters, 18, 1023-1034. https://doi.org/10.3892/ol.2019.10411
MLA
Jin, H., Huang, X., Shao, K., Li, G., Wang, J., Yang, H., Hou, Y."Integrated bioinformatics analysis to identify 15 hub genes in breast cancer". Oncology Letters 18.2 (2019): 1023-1034.
Chicago
Jin, H., Huang, X., Shao, K., Li, G., Wang, J., Yang, H., Hou, Y."Integrated bioinformatics analysis to identify 15 hub genes in breast cancer". Oncology Letters 18, no. 2 (2019): 1023-1034. https://doi.org/10.3892/ol.2019.10411
Copy and paste a formatted citation
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Spandidos Publications style
Jin H, Huang X, Shao K, Li G, Wang J, Yang H and Hou Y: Integrated bioinformatics analysis to identify 15 hub genes in breast cancer. Oncol Lett 18: 1023-1034, 2019.
APA
Jin, H., Huang, X., Shao, K., Li, G., Wang, J., Yang, H., & Hou, Y. (2019). Integrated bioinformatics analysis to identify 15 hub genes in breast cancer. Oncology Letters, 18, 1023-1034. https://doi.org/10.3892/ol.2019.10411
MLA
Jin, H., Huang, X., Shao, K., Li, G., Wang, J., Yang, H., Hou, Y."Integrated bioinformatics analysis to identify 15 hub genes in breast cancer". Oncology Letters 18.2 (2019): 1023-1034.
Chicago
Jin, H., Huang, X., Shao, K., Li, G., Wang, J., Yang, H., Hou, Y."Integrated bioinformatics analysis to identify 15 hub genes in breast cancer". Oncology Letters 18, no. 2 (2019): 1023-1034. https://doi.org/10.3892/ol.2019.10411
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