Identification of key genes associated with bladder cancer using gene expression profiles

Han,Yuping; Jin,Xuefei; Zhou,Hui; Liu,Bin

doi:10.3892/ol.2017.7310

Identification of key genes associated with bladder cancer using gene expression profiles

Authors:
- Yuping Han
- Xuefei Jin
- Hui Zhou
- Bin Liu
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Affiliations: Department of Urology, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China, Department of Gastrointestinal Colorectal and Anal Surgery, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
Published online on: October 31, 2017 https://doi.org/10.3892/ol.2017.7310
Pages: 297-303
Copyright: © Han et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aim of the present study was to further investigate the molecular mechanisms of bladder cancer. The microarray data GSE52519 were downloaded from Gene Expression Omnibus, comprising 9 bladder cancer and 3 normal bladder tissue samples. Differentially expressed genes (DEGs) were identified using Limma package analysis. Subsequently, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and Reactome pathway enrichment analyses were performed for down‑ and upregulated DEGs. Transcription factors and genes associated with cancer from DEGs were identified. Protein‑protein interaction (PPI) networks were constructed using STRING, and pathway enrichment analysis was also conducted for genes in the core sub‑network that was identified using BioNet. In total, 420 downregulated and 335 upregulated DEGs were identified. Functional and pathway enrichment analyses identified that a number of DEGs, including AURKA, CCNA2, CCNE1, CDC20 and CCNB2, were enriched in the cell cycle. Furthermore, a total of 12 upregulated proto‑oncogenes were identified, including AURKA and CCNA2. In the PPI sub‑network, a number of DEGs (e.g., CCNB2, CDC20, CCNA2 and MCM6) with higher degrees were enriched in the KEGG pathway of the cell cycle. In conclusion, the DEGs associated with the cell cycle (e.g., CDC20, CCNA2, CCNB2 and AURKA) may serve pivotal roles in the pathogenesis of bladder cancer.

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1	Di Pierro GB, Gulia C, Cristini C, Fraietta G, Marini L, Grande P, Gentile V and Piergentili R: Bladder cancer: A simple model becomes complex. Curr Genomics. 13:3952012. View Article : Google Scholar : PubMed/NCBI
2	Ye F, Wang L, Castillo-Martin M, McBride R, Galsky MD, Zhu J, Boffetta P, Zhang DY and Cordon-Cardo C: Biomarkers for bladder cancer management: Present and future. Am J Clin Exp Urol. 2:1–14. 2014.PubMed/NCBI
3	Sanchez-Carbayo M, Socci ND, Lozano J, Saint F and Cordon-Cardo C: Defining molecular profiles of poor outcome in patients with invasive bladder cancer using oligonucleotide microarrays. J Clin Oncol. 24:778–789. 2006. View Article : Google Scholar : PubMed/NCBI
4	Morrison CD, Liu P, Woloszynska-Read A, Zhang J, Luo W, Qin M, Bshara W, Conroy JM, Sabatini L, Vedell P, et al: Whole-genome sequencing identifies genomic heterogeneity at a nucleotide and chromosomal level in bladder cancer. Proc Natl Acad Sci USA. 111:pp. E672–E681. 2014; View Article : Google Scholar : PubMed/NCBI
5	Liu JY, Qian D, He LR, Li YH, Liao YJ, Mai SJ, Tian XP, Liu YH, Zhang JX, Kung HF, et al: PinX1 suppresses bladder urothelial carcinoma cell proliferation via the inhibition of telomerase activity and p16/cyclin D1 pathway. Mol Cancer. 12:1482013. View Article : Google Scholar : PubMed/NCBI
6	Florl AR and Schulz WA: Chromosomal instability in bladder cancer. Arch Toxicol. 82:173–182. 2008. View Article : Google Scholar : PubMed/NCBI
7	Zieger K, Wiuf C, Jensen KM, Ørntoft TF and Dyrskjøt L: Chromosomal imbalance in the progression of high-risk non-muscle invasive bladder cancer. BMC Cancer. 9:1492009. View Article : Google Scholar : PubMed/NCBI
8	Diboun I, Wernisch L, Orengo CA and Koltzenburg M: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics. 7:2522006. View Article : Google Scholar : PubMed/NCBI
9	Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, et al: Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33:e1752005. View Article : Google Scholar : PubMed/NCBI
10	da W Huang, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009.PubMed/NCBI
11	da W Huang, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI
12	Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
13	Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
14	Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar MR, et al: The Reactome pathway knowledgebase. Nucleic Acids Res. 42(Database Issue): D472–D477. 2014. View Article : Google Scholar : PubMed/NCBI
15	Matys V, Fricke E, Geffers R, Gößling E, Haubrock M, Hehl R, Hornischer K, Karas D, Kel AE, Kel-Margoulis OV, et al: TRANSFAC: Transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 31:374–378. 2003. View Article : Google Scholar : PubMed/NCBI
16	Zhao M, Sun J and Zhao Z: TSGene: A web resource for tumor suppressor genes. Nucleic Acids Res. 41(Database Issue): D970–D976. 2013. View Article : Google Scholar : PubMed/NCBI
17	Chen JS, Hung WS, Chan HH, Tsai SJ and Sun HS: In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma. Bioinformatics. 29:420–427. 2013. View Article : Google Scholar : PubMed/NCBI
18	Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C and Jensen LJ: STRING v9. 1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41(Database Issue): D808–D815. 2013.PubMed/NCBI
19	Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD and Ideker T: A travel guide to Cytoscape plugins. Nat Methods. 9:1069–1076. 2012. View Article : Google Scholar : PubMed/NCBI
20	Beisser D, Klau GW, Dandekar T, Müller T and Dittrich MT: BioNet: An R-Package for the functional analysis of biological networks. Bioinformatics. 26:1129–1130. 2010. View Article : Google Scholar : PubMed/NCBI
21	Wang Y, Ji P, Liu J, Broaddus RR, Xue F and Zhang W: Centrosome-associated regulators of the G2/M checkpoint as targets for cancer therapy. Mol Cancer. 8:82009. View Article : Google Scholar : PubMed/NCBI
22	Choi JW, Kim Y, Lee JH and Kim YS: High expression of spindle assembly checkpoint proteins CDC20 and MAD2 is associated with poor prognosis in urothelial bladder cancer. Virchows Arch. 463:681–687. 2013. View Article : Google Scholar : PubMed/NCBI
23	Kidokoro T, Tanikawa C, Furukawa Y, Katagiri T, Nakamura Y and Matsuda K: CDC20, a potential cancer therapeutic target, is negatively regulated by p53. Oncogene. 27:1562–1571. 2008. View Article : Google Scholar : PubMed/NCBI
24	Lu Y, Liu P, Wen W, Grubbs CJ, Townsend RR, Malone JP, Lubet RA and You M: Cross-species comparison of orthologous gene expression in human bladder cancer and carcinogen-induced rodent models. Am J Transl Res. 3:8–27. 2010.PubMed/NCBI
25	Lee SJ, Lee EJ, Kim SK, Jeong P, Cho YH, Yun SJ, Kim S, Kim GY, Choi YH, Cha EJ, et al: Identification of pro-inflammatory cytokines associated with muscle invasive bladder cancer; the roles of IL-5, IL-20, and IL-28A. PLoS One. 7:e402672012. View Article : Google Scholar : PubMed/NCBI
26	Blaveri E, Simko JP, Korkola JE, Brewer JL, Baehner F, Mehta K, Devries S, Koppie T, Pejavar S, Carroll P and Waldman FM: Bladder cancer outcome and subtype classification by gene expression. Clin Cancer Res. 11:4044–4055. 2005. View Article : Google Scholar : PubMed/NCBI
27	Puzio-Kuter AM, Castillo-Martin M, Kinkade CW, Wang X, Shen TH, Matos T, Shen MM, Cordon-Cardo C and Abate-Shen C: Inactivation of p53 and Pten promotes invasive bladder cancer. Genes Dev. 23:675–680. 2009. View Article : Google Scholar : PubMed/NCBI
28	Medina-Aguilar R, Marchat LA, Ocampo E Arechaga, Gariglio P, García Mena J, Sepúlveda N Villegas, Martínez Castillo M and López-Camarillo C: Resveratrol inhibits cell cycle progression by targeting Aurora kinase A and Polo-like kinase 1 in breast cancer cells. Oncol Rep. 35:3696–3704. 2016. View Article : Google Scholar : PubMed/NCBI
29	Park HS, Park WS, Bondaruk J, Tanaka N, Katayama H, Lee S, Spiess PE, Steinberg JR, Wang Z, Katz RL, et al: Quantitation of Aurora kinase A gene copy number in urine sediments and bladder cancer detection. J Natl Cancer Inst. 100:1401–1411. 2008. View Article : Google Scholar : PubMed/NCBI
30	Wu X, Gu J, Grossman HB, Amos CI, Etzel C, Huang M, Zhang Q, Millikan RE, Lerner S, Dinney CP and Spitz MR: Bladder cancer predisposition: A multigenic approach to DNA-repair and cell-cycle-control genes. Am J Hum Genet. 78:464–479. 2006. View Article : Google Scholar : PubMed/NCBI
31	de Martino M, Shariat SF, Hofbauer SL, Lucca I, Taus C, Wiener HG, Haitel A, Susani M and Klatte T: Aurora a kinase as a diagnostic urinary marker for urothelial bladder cancer. World J Urol. 33:105–110. 2015. View Article : Google Scholar : PubMed/NCBI