Identification of Hub Genes in Pancreatic Ductal Adenocarcinoma Using Bioinformatics Analysis
Abstract
Background: To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis.
Methods: The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation,Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by CytoHubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA.
Results: Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1.
Conclusion: we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.
2. Jiang P, Liu XS (2015). Big data mining yields novel insights on cancer. Nat Genet, 47(2): 103-104.
3. Barrett T, Suzek TO, Troup DB, et al (2005). NCBI GEO: mining millions of expres-sion profiles—database and tools. Nucleic Acids Res, 33(Database issue): D562–D566.
4. Dennis GJ, Sherman BT, Hosack DA, et al (2003). DAVID: Datebase for Annota-tion, Visualization, and Integrated Dis-covery. Genome Biol, 4(5): P3.
5. Szklarczyk D, Morris JH, Cook H, et al (2017). The STRING database in 2017: Quality-cantrolled protein-protein associ-ation networks, made broadly accessible. Nucleic Acids Res, 45(D1): D362-D368.
6. Kanehisa M, Goto S, Sato Y, et al (2012). KEGG for integration and interpretation of large scale molecular data sets. Nucleic Acids Res, 40(Database issue): D109–D114.
7. Tang Y, Zhang Z, Tang Y, et al (2018). Identification of potential target genes in pancreatic ductal adenocarcinoma by bio-informatics analysis. Oncol Lett, 16(2): 2453-2461.
8. Peng T, Zhou W, Guo F, et al (2017). Cen-trosomal protein 55 activates NF-κB sig-nalling and promotes pancreatic cancer cells aggressiveness. Sci Rep, 7(1): 5925.
9. Tao J, Zhi X, Tian Y, et al (2014). CEP55 contributes to human gastric carcinoma by regulating cell proliferation. Tumour Bi-ol, 35(5): 4389–4399.
10. Wang Y, Jin T, Dai X, et al (2016). Lentivi-rus-mediated knockdown of CEP55 suppresses cell proliferation of breast cancer cells. Biosci Trends, 10(1): 67–73.
11. Zhang W, Niu C, He W, et al (2016). Upreg-ulation of centrosomal protein 55 is as-sociated with unfavorable prognosis and tumor invasion in epithelial ovarian carci-noma. Tumour Biol, 37(5): 6239–6254.
12. Jeffery J, Sinha D, Srihari S, et al (2016). Be-yond cytokinesis: the emerging roles of CEP55 in tumorigenesis. Oncogene, 35(6): 683–690.
13. Fabbro M, Zhou BB, Takahashi M, et al (2005). Cdk1/Erk2- and Plk1-dependent phosphorylation of a centrosome pro-tein, Cep55, is required for its recruitment to midbody and cytokinesis. Dev Cell, 9(4): 477–488.
14. Oegema K, Savoian MS, Mitchison TJ, et al (2000). Functional analysis of a human homologue of the Drosophila actin bind-ing protein anillin suggests a role in cyto-kinesis. J Cell Biol, 150(3): 539-552.
15. Hall PA, Todd CB, Hyland PL, et al (2005). The septin-binding protein anillin is overexpressed in diverse human tumors. Clin Cancer Res, 11(19 Pt 1): 6780-6786.
16. Liang PI, Chen WT, Li CF, et al (2015). Sub-cellular localisation of anillin is associated with different survival outcomes in upper urinary tract urothelial carcinoma. J Clin Pathol, 68(12): 1026-1032.
17. Wang S, Mo Y, Midorikawa K, et al (2015). The potent tumor suppressor miR-497 inhibits cancer phenotypes in nasopha-ryngeal carcinoma by targeting ANLN and HSPA4L. Oncotarget, 6(34): 35893–35907.
18. Suzuki C, Daigo Y, Ishikawa N, et al (2005). ANLN plays a critical role in human lung carcinogenesis through the activation of RHOA and by involvement in the phos-phoinositide 3-kinase/AKT pathway. Cancer Res, 65(24): 11314-11325.
19. Magnusson K, Gremel G, Ryden L, et al (2016). ANLN is a prognostic biomarker independent of Ki-67 and essential for cell cycle progression in primary breast cancer. BMC Cancer, 16(1): 904.
20. Idichi T, Seki N, Kurahara H, et al (2017). Regulation of actin-binding protein ANLN by antitumor miR-217 inhibits cancer cell aggressiveness in pancreatic ductal adenocarcinoma. Oncotarget, 8(32): 53180-53193.
21. Benitz S, Regel I, Reinhard T, et al (2016). Polycomb repressor complex 1 promotes gene silencing through H2AK119 mono-ubiquitination in acinar-to-ductal metapla-sia and pancreatic cancer cells. Oncotarget, 7(10): 11424-11433.
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Issue | Vol 50 No 11 (2021) | |
Section | Original Article(s) | |
DOI | https://doi.org/10.18502/ijph.v50i11.7578 | |
Keywords | ||
Bioinformatics analysis Differently expressed genes Hub genes Pancreatic ductal adenocarcinoma |
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