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Article Open Access

Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses

  • Authors:
    • Jin Long
    • Zhongbo Zhang
    • Zhe Liu
    • Yuanhong Xu
    • Chunlin Ge
  • View Affiliations / Copyright

    Affiliations: Department of General Surgery, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
    Copyright: © Long et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 1391-1397
    |
    Published online on: December 21, 2015
       https://doi.org/10.3892/ol.2015.4042
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Abstract

This study aimed to explore the underlying genes and pathways associated with pancreatic ductal adenocarcinoma (PDAC) by bioinformatics analyses. Gene expression profile GSE43795 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between six PDAC and five non‑neoplastic pancreatic tissue samples were analyzed using the limma package. Gene ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by functional annotation and protein‑protein interaction (PPI) network construction. Finally, the sub‑network was identified and pathway enrichment analysis was performed on the contained DEGs. A total of 374 downregulated and 559 upregulated DEGs were identified. The downregulated DEGs were enriched in GO terms associated with digestion and transport and pathways related to metabolism, while the upregulated DEGs were enriched in GO terms associated with the cell cycle and mitosis and pathways associated with the occurrence of cancer including the cell cycle pathway. Following functional annotation, the oncogene pituitary tumor‑transforming 1 (PTTG1) was upregulated. In the PPI network and sub‑network, cell division cycle 20 (CDC20) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) were hub genes with high connectivity degrees. Additionally, DEGs in the sub‑network including cyclin B1 (CCNB1) were mainly enriched in the cell cycle and p53 signaling pathways. In conclusion, the cell cycle and p53 signaling pathways may play significant roles in PDAC, and DEGs including CDC20, BUB1B, CCNB1 and PTTG1 may be potential targets for PDAC diagnosis and treatment.
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Copy and paste a formatted citation
Spandidos Publications style
Long J, Zhang Z, Liu Z, Xu Y and Ge C: Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses. Oncol Lett 11: 1391-1397, 2016.
APA
Long, J., Zhang, Z., Liu, Z., Xu, Y., & Ge, C. (2016). Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses. Oncology Letters, 11, 1391-1397. https://doi.org/10.3892/ol.2015.4042
MLA
Long, J., Zhang, Z., Liu, Z., Xu, Y., Ge, C."Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses". Oncology Letters 11.2 (2016): 1391-1397.
Chicago
Long, J., Zhang, Z., Liu, Z., Xu, Y., Ge, C."Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses". Oncology Letters 11, no. 2 (2016): 1391-1397. https://doi.org/10.3892/ol.2015.4042
Copy and paste a formatted citation
x
Spandidos Publications style
Long J, Zhang Z, Liu Z, Xu Y and Ge C: Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses. Oncol Lett 11: 1391-1397, 2016.
APA
Long, J., Zhang, Z., Liu, Z., Xu, Y., & Ge, C. (2016). Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses. Oncology Letters, 11, 1391-1397. https://doi.org/10.3892/ol.2015.4042
MLA
Long, J., Zhang, Z., Liu, Z., Xu, Y., Ge, C."Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses". Oncology Letters 11.2 (2016): 1391-1397.
Chicago
Long, J., Zhang, Z., Liu, Z., Xu, Y., Ge, C."Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses". Oncology Letters 11, no. 2 (2016): 1391-1397. https://doi.org/10.3892/ol.2015.4042
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