Integrative genome-wide gene expression profiling of clear cell renal cell carcinoma in Czech Republic and in the United States

. 2013 ; 8 (3) : e57886. [epub] 20130305

Jazyk angličtina Země Spojené státy americké Médium print-electronic

Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid23526956

Grantová podpora
U01 CA155309 NCI NIH HHS - United States
CA155309 NCI NIH HHS - United States

Gene expression microarray and next generation sequencing efforts on conventional, clear cell renal cell carcinoma (ccRCC) have been mostly performed in North American and Western European populations, while the highest incidence rates are found in Central/Eastern Europe. We conducted whole-genome expression profiling on 101 pairs of ccRCC tumours and adjacent non-tumour renal tissue from Czech patients recruited within the "K2 Study", using the Illumina HumanHT-12 v4 Expression BeadChips to explore the molecular variations underlying the biological and clinical heterogeneity of this cancer. Differential expression analysis identified 1650 significant probes (fold change ≥2 and false discovery rate <0.05) mapping to 630 up- and 720 down-regulated unique genes. We performed similar statistical analysis on the RNA sequencing data of 65 ccRCC cases from the Cancer Genome Atlas (TCGA) project and identified 60% (402) of the downregulated and 74% (469) of the upregulated genes found in the K2 series. The biological characterization of the significantly deregulated genes demonstrated involvement of downregulated genes in metabolic and catabolic processes, excretion, oxidation reduction, ion transport and response to chemical stimulus, while simultaneously upregulated genes were associated with immune and inflammatory responses, response to hypoxia, stress, wounding, vasculature development and cell activation. Furthermore, genome-wide DNA methylation analysis of 317 TCGA ccRCC/adjacent non-tumour renal tissue pairs indicated that deregulation of approximately 7% of genes could be explained by epigenetic changes. Finally, survival analysis conducted on 89 K2 and 464 TCGA cases identified 8 genes associated with differential prognostic outcomes. In conclusion, a large proportion of ccRCC molecular characteristics were common to the two populations and several may have clinical implications when validated further through large clinical cohorts.

Zobrazit více v PubMed

Eble JN TK, Pisani P (2004) Renal cell carcinoma. In: Pathology and Genetics of Tumors of the Urinary System and Male Genital Organs. Lyon, France: IARC Press.

Ferlay J, Shin HR, Bray F, Forman D, Mathers C, et al. (2010) Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 127: 2893–2917. PubMed

Chow WH, Dong LM, Devesa SS (2010) Epidemiology and risk factors for kidney cancer. Nat Rev Urol 7: 245–257. PubMed PMC

Banumathy G, Cairns P (2010) Signaling pathways in renal cell carcinoma. Cancer Biol Ther 10: 658–664. PubMed PMC

Gossage L, Eisen T (2010) Alterations in VHL as potential biomarkers in renal-cell carcinoma. Nat Rev Clin Oncol 7: 277–288. PubMed

Dalgliesh GL, Furge K, Greenman C, Chen L, Bignell G, et al. (2010) Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature 463: 360–363. PubMed PMC

Pena-Llopis S, Vega-Rubin-de-Celis S, Liao A, Leng N, Pavia-Jimenez A, et al. (2012) BAP1 loss defines a new class of renal cell carcinoma. Nat Genet 44: 751–759. PubMed PMC

Varela I, Tarpey P, Raine K, Huang D, Ong CK, et al. (2011) Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 469: 539–542. PubMed PMC

Pantuck AJ, Zeng G, Belldegrun AS, Figlin RA (2003) Pathobiology, prognosis, and targeted therapy for renal cell carcinoma: exploiting the hypoxia-induced pathway. Clin Cancer Res 9: 4641–4652. PubMed

Lin F, Zhang PL, Yang XJ, Prichard JW, Lun M, et al. (2006) Morphoproteomic and molecular concomitants of an overexpressed and activated mTOR pathway in renal cell carcinomas. Ann Clin Lab Sci 36: 283–293. PubMed

Peruzzi B, Athauda G, Bottaro DP (2006) The von Hippel-Lindau tumor suppressor gene product represses oncogenic beta-catenin signaling in renal carcinoma cells. Proc Natl Acad Sci U S A 103: 14531–14536. PubMed PMC

Dulaimi E, Ibanez de Caceres I, Uzzo RG, Al-Saleem T, Greenberg RE, et al. (2004) Promoter hypermethylation profile of kidney cancer. Clin Cancer Res 10: 3972–3979. PubMed

Wendt MK, Allington TM, Schiemann WP (2009) Mechanisms of the epithelial-mesenchymal transition by TGF-beta. Future Oncol 5: 1145–1168. PubMed PMC

Monga SP, Mars WM, Pediaditakis P, Bell A, Mule K, et al. (2002) Hepatocyte growth factor induces Wnt-independent nuclear translocation of beta-catenin after Met-beta-catenin dissociation in hepatocytes. Cancer Res 62: 2064–2071. PubMed

Tan W, Hildebrandt MA, Pu X, Huang M, Lin J, et al. (2011) Role of inflammatory related gene expression in clear cell renal cell carcinoma development and clinical outcomes. J Urol 186: 2071–2077. PubMed PMC

Brannon AR, Haake SM, Hacker KE, Pruthi RS, Wallen EM, et al. (2012) Meta-analysis of clear cell renal cell carcinoma gene expression defines a variant subgroup and identifies gender influences on tumor biology. Eur Urol 61: 258–268. PubMed PMC

Brannon AR, Reddy A, Seiler M, Arreola A, Moore DT, et al. (2010) Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns. Genes Cancer 1: 152–163. PubMed PMC

Zhao H, Ljungberg B, Grankvist K, Rasmuson T, Tibshirani R, et al. (2006) Gene expression profiling predicts survival in conventional renal cell carcinoma. PLoS Med 3: e13. PubMed PMC

Beroukhim R, Brunet JP, Di Napoli A, Mertz KD, Seeley A, et al. (2009) Patterns of gene expression and copy-number alterations in von-hippel lindau disease-associated and sporadic clear cell carcinoma of the kidney. Cancer Res 69: 4674–4681. PubMed PMC

Sanjmyatav J, Steiner T, Wunderlich H, Diegmann J, Gajda M, et al. (2011) A specific gene expression signature characterizes metastatic potential in clear cell renal cell carcinoma. J Urol 186: 289–294. PubMed

Wuttig D, Baier B, Fuessel S, Meinhardt M, Herr A, et al. (2009) Gene signatures of pulmonary metastases of renal cell carcinoma reflect the disease-free interval and the number of metastases per patient. Int J Cancer 125: 474–482. PubMed

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102: 15545–15550. PubMed PMC

Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5: 621–628. PubMed

Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11: R25. PubMed PMC

Delahunt B (2009) Advances and controversies in grading and staging of renal cell carcinoma. Mod Pathol 22 Suppl 2S24–36. PubMed

Ficarra V, Martignoni G, Maffei N, Brunelli M, Novara G, et al. (2005) Original and reviewed nuclear grading according to the Fuhrman system: a multivariate analysis of 388 patients with conventional renal cell carcinoma. Cancer 103: 68–75. PubMed

Diaz-Uriarte R (2008) SignS: a parallelized, open-source, freely available, web-based tool for gene selection and molecular signatures for survival and censored data. BMC Bioinformatics 9: 30. PubMed PMC

Motzer RJ, Mazumdar M, Bacik J, Berg W, Amsterdam A, et al. (1999) Survival and prognostic stratification of 670 patients with advanced renal cell carcinoma. J Clin Oncol 17: 2530–2540. PubMed

Zisman A, Pantuck AJ, Dorey F, Said JW, Shvarts O, et al. (2001) Improved prognostication of renal cell carcinoma using an integrated staging system. J Clin Oncol 19: 1649–1657. PubMed

Wuttig D, Zastrow S, Fussel S, Toma MI, Meinhardt M, et al. (2012) CD31, EDNRB and TSPAN7 are promising prognostic markers in clear-cell renal cell carcinoma revealed by genome-wide expression analyses of primary tumors and metastases. Int J Cancer 131: E693–704. PubMed

Boer JM, Huber WK, Sultmann H, Wilmer F, von Heydebreck A, et al. (2001) Identification and classification of differentially expressed genes in renal cell carcinoma by expression profiling on a global human 31,500-element cDNA array. Genome Res 11: 1861–1870. PubMed PMC

Gieseg MA, Cody T, Man MZ, Madore SJ, Rubin MA, et al. (2002) Expression profiling of human renal carcinomas with functional taxonomic analysis. BMC Bioinformatics 3: 26. PubMed PMC

Higgins JP, Shinghal R, Gill H, Reese JH, Terris M, et al. (2003) Gene expression patterns in renal cell carcinoma assessed by complementary DNA microarray. Am J Pathol 162: 925–932. PubMed PMC

Lane BR, Li J, Zhou M, Babineau D, Faber P, et al. (2009) Differential expression in clear cell renal cell carcinoma identified by gene expression profiling. J Urol 181: 849–860. PubMed PMC

Takahashi M, Rhodes DR, Furge KA, Kanayama H, Kagawa S, et al. (2001) Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification. Proc Natl Acad Sci U S A 98: 9754–9759. PubMed PMC

Tun HW, Marlow LA, von Roemeling CA, Cooper SJ, Kreinest P, et al. (2010) Pathway signature and cellular differentiation in clear cell renal cell carcinoma. PLoS One 5: e10696. PubMed PMC

Vasselli JR, Shih JH, Iyengar SR, Maranchie J, Riss J, et al. (2003) Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor. Proc Natl Acad Sci U S A 100: 6958–6963. PubMed PMC

Beleut M, Zimmermann P, Baudis M, Bruni N, Buhlmann P, et al. (2012) Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome. BMC Cancer 12: 310. PubMed PMC

Nickerson ML, Jaeger E, Shi Y, Durocher JA, Mahurkar S, et al. (2008) Improved identification of von Hippel-Lindau gene alterations in clear cell renal tumors. Clin Cancer Res 14: 4726–4734. PubMed PMC

Kim WY, Safran M, Buckley MR, Ebert BL, Glickman J, et al. (2006) Failure to prolyl hydroxylate hypoxia-inducible factor alpha phenocopies VHL inactivation in vivo. EMBO J 25: 4650–4662. PubMed PMC

Tello D, Balsa E, Acosta-Iborra B, Fuertes-Yebra E, Elorza A, et al. (2011) Induction of the mitochondrial NDUFA4L2 protein by HIF-1alpha decreases oxygen consumption by inhibiting Complex I activity. Cell Metab 14: 768–779. PubMed

Klatte T, Seligson DB, Riggs SB, Leppert JT, Berkman MK, et al. (2007) Hypoxia-inducible factor 1 alpha in clear cell renal cell carcinoma. Clin Cancer Res 13: 7388–7393. PubMed

Raval RR, Lau KW, Tran MG, Sowter HM, Mandriota SJ, et al. (2005) Contrasting properties of hypoxia-inducible factor 1 (HIF-1) and HIF-2 in von Hippel-Lindau-associated renal cell carcinoma. Mol Cell Biol 25: 5675–5686. PubMed PMC

Gordan JD, Lal P, Dondeti VR, Letrero R, Parekh KN, et al. (2008) HIF-alpha effects on c-Myc distinguish two subtypes of sporadic VHL-deficient clear cell renal carcinoma. Cancer Cell 14: 435–446. PubMed PMC

Shay JE, Celeste Simon M (2012) Hypoxia-inducible factors: Crosstalk between inflammation and metabolism. Semin Cell Dev Biol 23: 389–394. PubMed

van Uden P, Kenneth NS, Rocha S (2008) Regulation of hypoxia-inducible factor-1alpha by NF-kappaB. Biochem J 412: 477–484. PubMed PMC

Morikawa T, Sugiyama A, Kume H, Ota S, Kashima T, et al. (2007) Identification of Toll-like receptor 3 as a potential therapeutic target in clear cell renal cell carcinoma. Clin Cancer Res 13: 5703–5709. PubMed

Bradford JR, Hey Y, Yates T, Li Y, Pepper SD, et al. (2010) A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling. BMC Genomics 11: 282. PubMed PMC

Ibanez de Caceres I, Dulaimi E, Hoffman AM, Al-Saleem T, Uzzo RG, et al. (2006) Identification of novel target genes by an epigenetic reactivation screen of renal cancer. Cancer Res 66: 5021–5028. PubMed

Cho M, Uemura H, Kim SC, Kawada Y, Yoshida K, et al. (2001) Hypomethylation of the MN/CA9 promoter and upregulated MN/CA9 expression in human renal cell carcinoma. Br J Cancer 85: 563–567. PubMed PMC

Girgis AH, Iakovlev VV, Beheshti B, Bayani J, Squire JA, et al. (2012) Multi-level whole genome analysis reveals candidate biomarkers in clear cell renal cell carcinoma. Cancer Res. PubMed

Yoshida Y, Nakada M, Harada T, Tanaka S, Furuta T, et al. (2010) The expression level of sphingosine-1-phosphate receptor type 1 is related to MIB-1 labeling index and predicts survival of glioblastoma patients. J Neurooncol 98: 41–47. PubMed

Tang XX, Zhao H, Robinson ME, Cohen B, Cnaan A, et al. (2000) Implications of EPHB6, EFNB2, and EFNB3 expressions in human neuroblastoma. Proc Natl Acad Sci U S A 97: 10936–10941. PubMed PMC

Mura M, Swain RK, Zhuang X, Vorschmitt H, Reynolds G, et al. (2012) Identification and angiogenic role of the novel tumor endothelial marker CLEC14A. Oncogene 31: 293–305. PubMed

Davidson B, Stavnes HT, Risberg B, Nesland JM, Wohlschlaeger J, et al. (2012) Gene expression signatures differentiate adenocarcinoma of lung and breast origin in effusions. Hum Pathol 43: 684–694. PubMed

Kearsey J, Petit S, De Oliveira C, Schweighoffer F (2004) A novel four transmembrane spanning protein, CLP24. A hypoxically regulated cell junction protein. Eur J Biochem 271: 2584–2592. PubMed

Kauffmann A, Gentleman R, Huber W (2009) arrayQualityMetrics–a bioconductor package for quality assessment of microarray data. Bioinformatics 25: 415–416. PubMed PMC

Du P, Kibbe WA, Lin SM (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24: 1547–1548. PubMed

Smyth GK, Michaud J, Scott HS (2005) Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21: 2067–2075. PubMed

Huang da W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44–57. PubMed

Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80. PubMed PMC

Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I (2001) Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125: 279–284. PubMed

Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, et al. (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11: 587. PubMed PMC

Dave SS, Wright G, Tan B, Rosenwald A, Gascoyne RD, et al. (2004) Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med 351: 2159–2169. PubMed

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...