Identification of gene markers associated with metastasis in clear cell renal cell carcinoma

Yang,Hailing; Huo,Pengfei; Hu,Guozhang; Wei,Bo; Kong,Dalian; Li,Hongjun

doi:10.3892/ol.2017.6084

Identification of gene markers associated with metastasis in clear cell renal cell carcinoma

Authors:
- Hailing Yang
- Pengfei Huo
- Guozhang Hu
- Bo Wei
- Dalian Kong
- Hongjun Li
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Affiliations: Emergency Department, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China, Intensive Care Unit, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China, Department of Neurosurgery, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China, Department of Orthopedics, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China, Health Examination Center, China‑Japan Union Hospital, Jilin University, Changchun, Jilin 130033, P.R. China
Published online on: April 24, 2017 https://doi.org/10.3892/ol.2017.6084
Pages: 4755-4761
Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The present study aimed to screen potential target genes for the early diagnosis and treatment of early metastatic clear cell renal cell carcinoma (ccRCC) using the microarray data of early metastatic and non‑metastatic ccRCC samples. The DNA microarray dataset GSE47352 was downloaded from Gene Expression Omnibus and included 4 early metastatic and 5 non‑metastatic ccRCC samples. Differentially expressed genes (DEGs) were screened using the limma package. Then, pheatmap package was used to conduct two‑way clustering for the DEGs. Subsequently, MAPPFinder and GenMAPP were employed separately to perform functional and pathway enrichment analysis for the DEGs. Additionally, a protein‑protein interaction (PPI) network was constructed using Cytoscape, and small drug molecules were searched using Connectivity map (cmap). In total, 196 upregulated and 163 downregulated genes were identified. DEGs, including JUN, tumor necrosis factor (TNF), Ras homolog family member B (RHOB) and transforming growth factor β2 (TGFβ2) were significantly enriched in the signaling pathway of renal cell carcinoma. Furthermore, nuclear receptor subfamily 4 group A member 1 (NR4A1) was significantly enriched in the mitogen‑activated protein kinase signaling pathway; in addition, laminin subunit α (LAMA) 1, LAMA2 and LAMA4 were significantly enriched in extracellular matrix‑receptor interaction. JUN (degree=6) had the highest degree in the PPI network. Thapsigargin (score=‑0.913) possessed the highest performance in terms of the treatment of early metastatic ccRCC. In the present study, it was discovered that certain DEGs, including JUN, TNF, RHOB, NR4A1, TGFβ2, LAMA1, LAMA2 and LAMA4 were potential target genes associated with early metastatic ccRCC. In addition, thapsigargin could be used as an efficient small drug molecule for the treatment of early metastatic ccRCC.

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1	Jemal A, Bray F, Center MM, Ferlay J, Ward E and Forman D: Global cancer statistics. CA Cancer J Clin. 61:69–90. 2011. View Article : Google Scholar : PubMed/NCBI
2	Rosner I, Bratslavsky G, Pinto PA and Linehan WM: The clinical implications of the genetics of renal cell carcinoma. Urol Oncol. 27:131–136. 2009. View Article : Google Scholar : PubMed/NCBI
3	Novara G, Ficarra V, Antonelli A, Artibani W, Bertini R, Carini M, Cunico S Cosciani, Imbimbo C, Longo N, et al: Validation of the 2009 TNM version in a large multi-institutional cohort of patients treated for renal cell carcinoma: Are further improvements needed? Eur Urol. 58:588–595. 2010. View Article : Google Scholar : PubMed/NCBI
4	Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, et al: NCBI GEO: Archive for functional genomics data sets-10 years on. Nucleic Acids Res. 39(Database Issue): D1005–D1010. 2011. View Article : Google Scholar : PubMed/NCBI
5	Parkinson H, Kapushesky M, Shojatalab M, Abeygunawardena N, Coulson R, Farne A, Holloway E, Kolesnykov N, Lilja P, Lukk M, et al: ArrayExpress-a public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 35(Database issue): D747–D750. 2007. View Article : Google Scholar : PubMed/NCBI
6	Ni D, Ma X, Li HZ, Gao Y, Li XT, Zhang Y, Ai Q, Zhang P, Song EL, Huang QB, et al: Downregulation of FOXO3a promotes tumor metastasis and is associated with metastasis-free survival of patients with clear cell renal cell carcinoma. Clin Cancer Res. 20:1779–1790. 2014. View Article : Google Scholar : PubMed/NCBI
7	Mestas J, Burdick MD, Reckamp K, Pantuck A, Figlin RA and Strieter RM: The role of CXCR2/CXCR2 ligand biological axis in renal cell carcinoma. J Immunol. 175:5351–5357. 2005. View Article : Google Scholar : PubMed/NCBI
8	Tölle A, Jung M, Lein M, Johannsen M, Miller K, Moch H, Jung K and Kristiansen G: Brain-type and liver-type fatty acid-binding proteins: New tumor markers for renal cancer? BMC Cancer. 9:2482009. View Article : Google Scholar : PubMed/NCBI
9	Zhang ML and Zhou ZH: A k-nearest neighbor based algorithm for multi-label classification. Journal. 2:718–721. 2005.
10	Gautier L, Cope L, Bolstad BM and Irizarry RA: affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI
11	Rao Y, Lee Y, Jarjoura D, Ruppert AS, Liu CG, Hsu JC and Hagan JP: A comparison of normalization techniques for microRNA microarray data. Stat Appl Genet Mol Biol. 7:: 2008.PubMed/NCBI
12	Gentleman R, Carey VJ, Huber W, Irizarry RA and Dudoit S: Bioinformatics and computational biology solutions using R and Bioconductor. Springer; 2005, View Article : Google Scholar
13	Genovese C and Wasserman L: Operating characteristics and extensions of the false discovery rate procedure. J Royal Statistical Soci Series B (Statistical Methodology). 64:499–517. 2002. View Article : Google Scholar
14	Ester M, Kriegel HP, Sander J and Xu X: A density-based algorithm for discovering clusters in large spatial databases with noise. Journal. 96:226–231. 1996.
15	Kolde R: Pheatmap: Pretty Heatmaps. R Package Version 0.6.1. Journal. 2012.
16	Szekely GJ and Rizzo ML: Hierarchical clustering via joint between-within distances: Extending Ward's minimum variance method. J Classification. 22:151–183. 2005. View Article : Google Scholar
17	Deza MM and Deza E: Encyclopedia of Distances. Springer; 2009, View Article : Google Scholar
18	Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC and Conklin BR: GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet. 31:19–20. 2002. View Article : Google Scholar : PubMed/NCBI
19	Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC and Conklin BR: MAPPFinder: Using gene ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol. 4:R72003. View Article : Google Scholar : PubMed/NCBI
20	Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, et al: The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39(Database issue): D561–D568. 2011. View Article : Google Scholar : PubMed/NCBI
21	Smoot ME, Ono K, Ruscheinski J, Wang PL and Ideker T: Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar : PubMed/NCBI
22	Lamb J: The Connectivity Map: A new tool for biomedical research. Nat Rev Cancer. 7:54–60. 2007. View Article : Google Scholar : PubMed/NCBI
23	Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, et al: The connectivity map: Using gene-expression signatures to connect small molecules, genes and disease. Science. 313:1929–1935. 2006. View Article : Google Scholar : PubMed/NCBI
24	Reddy KB, Nabha SM and Atanaskova N: Role of MAP kinase in tumor progression and invasion. Cancer Metastasis Rev. 22:395–403. 2003. View Article : Google Scholar : PubMed/NCBI
25	Chaturvedi MM, Sung B, Yadav VR, Kannappan R and Aggarwal BB: NF-κB addiction and its role in cancer: ‘One size does not fit all’. Oncogene. 30:1615–1630. 2010. View Article : Google Scholar : PubMed/NCBI
26	Zhang Y, Pu X, Shi M, Chen L, Song Y, Qian L, Yuan G, Zhang H, Yu M, Hu M, et al: Critical role of c-Jun overexpression in liver metastasis of human breast cancer xenograft model. BMC Cancer. 7:1452007. View Article : Google Scholar : PubMed/NCBI
27	Davis FM, Stewart TA, Thompson EW and Monteith GR: Targeting EMT in cancer: Opportunities for pharmacological intervention. Trends Pharmacol Sci. 35:479–488. 2014. View Article : Google Scholar : PubMed/NCBI
28	Tun HW, Marlow LA, Von Roemeling CA, Cooper SJ, Kreinest P, Wu K, Luxon BA, Sinha M, Anastasiadis PZ and Copland JA: Pathway signature and cellular differentiation in clear cell renal cell carcinoma. PLoS One. 5:e106962010. View Article : Google Scholar : PubMed/NCBI
29	Mikami S, Mizuno R, Kosaka T, Saya H, Oya M and Okada Y: Expression of TNF-α and CD44 is implicated in poor prognosis, cancer cell invasion, metastasis and resistance to the sunitinib treatment in clear cell renal cell carcinomas. Int J Cancer. 136:1504–1514. 2015. View Article : Google Scholar : PubMed/NCBI
30	Wheeler AP and Ridley AJ: RhoB affects macrophage adhesion, integrin expression and migration. Exp Cell Res. 313:3505–3516. 2007. View Article : Google Scholar : PubMed/NCBI
31	Hutchison N, Hendry BM and Sharpe CC: Rho isoforms have distinct and specific functions in the process of epithelial to mesenchymal transition in renal proximal tubular cells. Cell Signal. 21:1522–1531. 2009. View Article : Google Scholar : PubMed/NCBI
32	Huang D, Ding Y, Luo WM, Bender S, Qian CN, Kort E, Zhang ZF, VandenBeldt K, Duesbery NS, Resau JH and Teh BT: Inhibition of MAPK kinase signaling pathways suppressed renal cell carcinoma growth and angiogenesis in vivo. Cancer Res. 68:81–88. 2008. View Article : Google Scholar : PubMed/NCBI
33	Pei L, Castrillo A and Tontonoz P: Regulation of macrophage inflammatory gene expression by the orphan nuclear receptor Nur77. Mol Endocrinol. 20:786–794. 2006. View Article : Google Scholar : PubMed/NCBI
34	Zhou F, Drabsch Y, Dekker TJ, de Vinuesa AG, Li Y, Hawinkels LJ, Sheppard KA, Goumans MJ, Luwor RB, de Vries CJ, et al: Nuclear receptor NR4A1 promotes breast cancer invasion and metastasis by activating TGF-β signalling. Nat Commun. 5:33882014. View Article : Google Scholar : PubMed/NCBI
35	Westbrook L, Johnson AC, Regner KR, Williams JM, Mattson DL, Kyle PB, Henegar JR and Garrett MR: Genetic susceptibility and loss of Nr4a1 enhances macrophage-mediated renal injury in CKD. J Am Soc Nephrol. 25:2499–2510. 2014. View Article : Google Scholar : PubMed/NCBI
36	Janda E, Lehmann K, Killisch I, Jechlinger M, Herzig M, Downward J, Beug H and Grünert S: Ras and TGF[beta] cooperatively regulate epithelial cell plasticity and metastasis Dissection of Ras signaling pathways. J Cell Biol. 156:299–313. 2002. View Article : Google Scholar : PubMed/NCBI
37	Heldin C-H, Vanlandewijck M and Moustakas A: Regulation of EMT by TGFβ in cancer. FEBS Lett. 586:1959–1970. 2012. View Article : Google Scholar : PubMed/NCBI
38	Hohenester E and Yurchenco PD: Laminins in basement membrane assembly. Cell Adh Migr. 7:56–63. 2013. View Article : Google Scholar : PubMed/NCBI
39	Patarroyo M, Tryggvason K and Virtanen I: Laminin isoforms in tumor invasion, angiogenesis and metastasis. Semin Cancer Biol. 12:197–207. 2002. View Article : Google Scholar : PubMed/NCBI
40	Vainionpää N, Lehto VP, Tryggvason K and Virtanen I: Alpha4 chain laminins are widely expressed in renal cell carcinomas and have a de-adhesive function. Lab Invest. 87:780–791. 2007. View Article : Google Scholar : PubMed/NCBI
41	Rogers TB, Inesi G, Wade R and Lederer W: Use of thapsigargin to study Ca2+ homeostasis in cardiac cells. Biosci Rep. 15:341–349. 1995. View Article : Google Scholar : PubMed/NCBI
42	Denmeade SR, Jakobsen CM, Janssen S, Khan SR, Garrett ES, Lilja H, Christensen SB and Isaacs JT: Prostate-specific antigen-activated thapsigargin prodrug as targeted therapy for prostate cancer. J Natl Cancer Inst. 95:990–1000. 2003. View Article : Google Scholar : PubMed/NCBI
43	Doan NT, Paulsen ES, Sehgal P, Møller JV, Nissen P, Denmeade SR, Isaacs JT, Dionne CA and Christensen SB: Targeting thapsigargin towards tumors. Steroids. 2014.PubMed/NCBI