Novel Biomarkers for Prostate Cancer Revealed by (α,β)-k-Feature Sets

  • Martín Gómez Ravetti
  • Regina Berretta
  • Pablo Moscato
Part of the Studies in Computational Intelligence book series (SCI, volume 205)


In this chapter we present a method based on the (α,β)-k-feature set problem for identifying relevant attributes in high-dimensional datasets for classification purposes. We present a case-study of biomedical interest. Using the gene expression of thousands of genes, we show that the method can give a reduced set that can identify samples as belonging to prostate cancer tumors or not. We thus address the need of finding novel methods that can deal with classification problems that involve feature selection from several thousand features, while we only have on the order of one hundred samples. The methodology appears to be very robust in this prostate cancer case study. It has lead to the identification of a set of differentially expressed genes that are highly predictive of the cells transition to a more malignant type, thus departing from the profile which is characteristic of its originating tissue. Although the method is presented with a particular bioinformatics application in mind, it can clearly be used in other domains. A biological analysis illustrates on the relevance of the genes found, and links to the most current developments in prostate cancer biomarker studies.


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  1. 1.
    Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)CrossRefGoogle Scholar
  2. 2.
    B. D. W. Group: Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69(3), 89–95 (2001)Google Scholar
  3. 3.
    The prostate cancer foundation of australia (08/08/2008 2007)Google Scholar
  4. 4.
    Cotta, C., Sloper, C., Moscato, P.: Evolutionary search of thresholds for robust feature set selection: application to the analysis of microarray data. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 21–30. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Cotta, C., Moscato, P.: The k-Feature Set problem is W[2]-complete. Journal of Computer and System Sciences 67(4), 686–690 (2003)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Davies, S., Russell, S.: NP-completeness of searches for smallest possible feature sets. In: Proceedings of the AAAI Symposium on Relevance, pp. 41–43 (1994)Google Scholar
  7. 7.
    Downey, R., Fellows, M.: Parameterized Complexity. Mongraphs in Computer Science. Springer, Heidelberg (1999)Google Scholar
  8. 8.
    Berretta, R., Mendes, A., Moscato, P.: Selection of discriminative genes in microarray experiments using mathematical programming. Journal of Research and Practice in Information Technology 39(4), 231–243 (2007)Google Scholar
  9. 9.
    Moscato, P., Mathieson, L., Mendes, A., Berretta, R.: The electronic primaries: Predicting the u.s. presidency using feature selection with safe data reduction. In: Estivill-Castro, V. (ed.) Twenty-Eighth Australasian Computer Science Conference (ACSC 2005). CRPIT, vol. 38, pp. 371–380. ACS, Newcastle (2005)Google Scholar
  10. 10.
    Berretta, R., Mendes, A., Moscato, P.: Integer programming models and algorithms for molecular classification of cancer from microarray data. In: Estivill-Castro, V. (ed.) Twenty-Eighth Australasian Computer Science Conference (ACSC 2005). CRPIT, vol. 38, pp. 361–370. ACS, Newcastle (2005)Google Scholar
  11. 11.
    Moscato, P., Berretta, R., Hourani, M., Mendes, A., Cotta, C.: Genes related with Alzheimer’s disease: A comparison of evolutionary search, statistical and integer programming approaches. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 84–94. Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI, pp. 1022–1029 (1993)Google Scholar
  13. 13.
    Berretta, R., Costa, W., Moscato, P.: Combinatorial optimization models for finding genetic signatures from gene expression datasets. In: Keith, J.M. (ed.) Bioinformatics, Volume II: Structure, Function and Applications, Methods in Molecura Biology, ch. 19, pp. 363–378. Humana Press (2008)Google Scholar
  14. 14.
    Hourani, M., Mendes, A., Berretta, R., Moscato, P.: Genetic biomarkers for brain hemisphere differentiation in parkinson’s disease. In: AIP Conference Proceedings, vol. 952(1), pp. 207–216 (2007)Google Scholar
  15. 15.
    Hourani, M., Berretta, R., Mendes, A., Moscato, P.: Genetic signatures for a rodent model of parkinson’s disease using combinatorial optimization methods. In: Keith, J.M. (ed.) Bioinformatics, Volume II: Structure, Function and Applications. Structure, Function and Applications, Methods in Molecura Biology, vol. II, pp. 379–392. Humana Press (2008), doi:10.1007/978-1-60327-429-6_20Google Scholar
  16. 16.
    Ravetti, M.G., Moscato, P.: Identification of a 5-protein biomarker molecular signature for predicting alzheimer’s disease, PLOS One (accepted)Google Scholar
  17. 17.
    Ross, D., Scherf, U., Eisen, M., et al.: Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics 24(3), 227–235 (2000)CrossRefGoogle Scholar
  18. 18.
    Brown, V., Ossadtchi, A., Khan, A., Cherry, S., Leahy, R., Smith, D.: High-throughput imaging of brain gene expression. Genome Research 12(2), 244–254 (2002)CrossRefGoogle Scholar
  19. 19.
    Singh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., D’Amico, A.V., Richie, J.P., Lander, E.S., Loda, M., Kantoff, P.W., Golub, T.R., Sellers, W.R.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1(2), 203–209 (2002)CrossRefGoogle Scholar
  20. 20.
    Orsenigo, C.: Gene selection and cancer microarray data classification via mixed-integer optimization. In: Marchiori, E., Moore, J.H. (eds.) EvoBIO 2008. LNCS, vol. 4973, pp. 141–152. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Shah, S., Kusiak, A.: Cancer gene search with data-mining and genetic algorithms. Computers in Biology and Medicine 37(2), 251–261 (2007)CrossRefGoogle Scholar
  22. 22.
    Wang, H.-Q., Wong, H.-S., Huang, D.-S., Shu, J.: Extracting gene regulation information for cancer classification. Pattern Recognition 40(12), 3379–3392 (2007)MATHCrossRefGoogle Scholar
  23. 23.
    Chandran, U.R., Ma, C., Dhir, R., Bisceglia, M., Lyons-Weiler, M., Liangand, W., Michalopoulos, G., Becich, M., Monzon, F.A.: Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process. BMC Cancer 7(64)Google Scholar
  24. 24.
    Yu, Y.P., Landsittel, D., Jing, L., Nelson, J., Ren, B., Liu, L., McDonald, C., Thomas, R., Dhir, R., Finkelstein, S., Michalopoulos, G., Becich, M., Luo, J.-H.: Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy. J. Clin. Oncol. 22(14), 2790–2799 (2004)CrossRefGoogle Scholar
  25. 25.
    Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America 99(10), 6567–6572 (2002)CrossRefGoogle Scholar
  26. 26.
    Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America 98(9), 5116–5121 (2001)MATHCrossRefGoogle Scholar
  27. 27.
    Gomez Ravetti, M., Moscato, P.: Identification of a 5-protein biomarker molecular signature for predicting alzheimer’s disease. PLOS One 3(9), e3111 (2008)CrossRefGoogle Scholar
  28. 28.
    Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Class prediction by nearest shrunken centroids, with applications to dna microarrays. Statistical Science 18(1), 104–117 (2003)MATHCrossRefMathSciNetGoogle Scholar
  29. 29.
    Banerjee, A.G., Bhattacharyya, I., Vishwanatha, J.K.: Identification of genes and molecular pathways involved in the progression of premalignant oral epithelia. Mol. Cancer Ther. 4(6), 865–875 (2005)CrossRefGoogle Scholar
  30. 30.
    Eichele, K., Ramer, R., Hinz, B.: Decisive role of cyclooxygenase-2 and lipocalin-type prostaglandin d synthase in chemotherapeutics-induced apoptosis of human cervical carcinoma cells. Oncogene 27(21), 3032–3044 (2008)CrossRefGoogle Scholar
  31. 31.
    Su, B., Guan, M., Xia, J., Lu, Y.: Stimulation of lipocalin-type prostaglandin d synthase by retinoic acid coincides with inhibition of cell proliferation in human 3ao ovarian cancer cells. Cell Biol. Int. 27(7), 587–592 (2003)CrossRefGoogle Scholar
  32. 32.
    Kim, J., Yang, P., Suraokar, M., Sabichi, A., Llansa, N., Mendoza, G., Subbarayan, V., Logothetis, C., Newman, R., Lippman, S., Menter, D.: Suppression of prostate tumor cell growth by stromal cell prostaglandin d synthase-derived products. Cancer Res. 65(14), 6189–6198 (2005)CrossRefGoogle Scholar
  33. 33.
    Park, J.M., Kanaoka, Y., Eguchi, N., Aritake, K., Grujic, S., Materi, A.M., Buslon, V.S., Tippin, B.L., Kwong, A.M., Salido, E., French, S.W., Urade, Y., Lin, H.J.: Hematopoietic prostaglandin d synthase suppresses intestinal adenomas in apcmin/+ mice. Cancer Res. 67(3), 881–889 (2007)CrossRefGoogle Scholar
  34. 34.
    Richard, C.L., Lowthers, E.L., Blay, J.: 15-deoxy-delta(12,14)-prostaglandin J(2) down-regulates CXCR4 on carcinoma cells through PPARgamma- and NFkappaB-mediated pathways. Exp. Cell Res. 313(16), 3446–3458 (2007)CrossRefGoogle Scholar
  35. 35.
    Chen, Y., Perussia, B., Campbell, K.: Prostaglandin d2 suppresses human nk cell function via signaling through d prostanoid receptor. J. Immunol. 179(5), 2766–2773 (2007)Google Scholar
  36. 36.
    Cao, H., Xiao, L., Park, G., Wang, X., Azim, A.C., Christman, J.W., van Breemen, R.B.: An improved lc-ms/ms method for the quantification of prostaglandins e(2) and d(2) production in biological fluids. Anal. Biochem. 372(1), 41–51 (2008)CrossRefGoogle Scholar
  37. 37.
    Torres, D., Paget, C., Fontaine, J., Mallevaey, T., Matsuoka, T., Narumiya, T.M.S., Capron, M., Gosset, P., Faveeuw, C., Trottein, F.: Prostaglandin d2 inhibits the production of ifn-gamma by invariant nk t cells: consequences in the control of b16 melanoma. J. Immunol. 180(2), 783–792 (2008)Google Scholar
  38. 38.
    Watson, M., Lind, M., Smith, L., Drew, P., Cawkwell, L.: Expression microarray analysis reveals genes associated with in vitro resistance to cisplatin in a cell line model. Acta Oncol. 46(5), 651–658 (2007)CrossRefGoogle Scholar
  39. 39.
    Guy, C.A., Hoogendoorn, B., Smith, S.K., Coleman, S., O’Donovan, M.C., Buckland, P.R.: Promoter polymorphisms in glutathione-s-transferase genes affect transcription. Pharmacogenetics 14(1), 45–51 (2004)CrossRefGoogle Scholar
  40. 40.
    Denson, J., Xi, Z., Wu, Y., Yang, W., Neale, G., Zhang, J.: Screening for inter-individual splicing differences in human gstm4 and the discovery of a single nucleotide substitution related to the tandem skipping of two exons. Gene. 379, 14855 (2006)CrossRefGoogle Scholar
  41. 41.
    Efferth, T., Volm, M.: Glutathione-related enzymes contribute to resistance of tumor cells and low toxicity in normal organs to artesunate. Vivo 19(1), 225–232 (2005)Google Scholar
  42. 42.
    Knight, T., Choudhuri, S., Klaassen, C.: Constitutive mrna expression of various glutathione s-transferase isoforms in different tissues of mice. Toxicol Sci. 100(2), 513–524 (2007)CrossRefGoogle Scholar
  43. 43.
    Liloglou, T., Walters, M., Maloney, P., Youngson, J., Field, J.K.: A t2517c polymorphism in the gstm4 gene is associated with risk of developing lung cancer. Lung Cancer 7(2), 143–146 (2002)CrossRefGoogle Scholar
  44. 44.
    DiLella, A.G., Toner, T.J., Austin, C.P., Connolly, B.M.: Identification of genes differentially expressed in benign prostatic hyperplasia. J. Histochem Cytochem. 49(5), 669–670 (2001)Google Scholar
  45. 45.
    Luo, J., Dunn, T.A., Ewing, C.M., Walsh, P.C., Isaacs, W.B.: Decreased gene expression of steroid 5 alpha-reductase 2 in human prostate cancer: implications for finasteride therapy of prostate carcinoma. Prostate 57(2), 134–139 (2003)CrossRefGoogle Scholar
  46. 46.
    Grigo, K., Wirsing, A., Lucas, B., Klein-Hitpass, L., Ryffel, G.U.: Hnf4 alpha orchestrates a set of 14 genes to down-regulate cell proliferation in kidney cells. Biol. Chem. 389(2), 179–187 (2008)CrossRefGoogle Scholar
  47. 47.
    Wu, Q., Parry, G.: Hepsin and prostate cancer. Front Biosci. 12, 5052–5059 (2007)CrossRefGoogle Scholar
  48. 48.
    Matsuo, T., Nakamura, K., Takamoto, N., Kodama, J., Hongo, A., Abrzua, F., Nasu, Y., Kumon, H., Hiramatsu, Y.: Expression of the serine protease hepsin and clinical outcome of human endometrial cancer. Anticancer Res. 28(1A), 159–164 (2008)Google Scholar
  49. 49.
    Kelly, K.A., Setlur, S.R., Ross, R., Anbazhagan, R., Waterman, P., Rubin, M.A., Weissleder, R.: Detection of early prostate cancer using a hepsin-targeted imaging agent. Cancer Res. 68(7), 2286–2291 (2008)CrossRefGoogle Scholar
  50. 50.
    Magee, J.A., Araki, T., Patil, S., Ehrig, T., True, L., Humphrey, P.A., Catalona, W.J., Watson, M.A., Milbrandt, J.: Expression profiling reveals hepsin overexpression in prostate cancer. Cancer Res. 61(15), 5692–5696 (2001)Google Scholar
  51. 51.
    Huppi, K., Chandramouli, G.V.: Molecular profiling of prostate cancer. Curr. Urol. Rep. 5(1), 45–51 (2004)CrossRefGoogle Scholar
  52. 52.
    Xu, L., Tan, A.C., Naiman, D.Q., Geman, D., Winslow, R.L.: Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data. Bioinformatics 21(20), 3905–3911 (2005)CrossRefGoogle Scholar
  53. 53.
    Riddick, A.C., Barker, C., Sheriffs, I., Bass, R., Ellis, V., Sethia, K.K., Edwards, D.R., Ball, R.Y.: Banking of fresh-frozen prostate tissue: methods, validation and use. BJU Int. 91(4), 315–324 (2003)CrossRefGoogle Scholar
  54. 54.
    Stephan, C., Yousef, G.M., Scorilas, A., Jung, K., Jung, M., Kristiansen, G., Hauptmann, S., Kishi, T., Nakamura, T., Loening, S.A., Diamandis, E.P.: Hepsin is highly over expressed in and a new candidate for a prognostic indicator in prostate cancer. J. Urol. 171(1), 187–191 (2004)CrossRefGoogle Scholar
  55. 55.
    Fromont, G., Chene, L., Vidaud, M., Vallancien, G., Mangin, P., Fournier, G., Validire, P., Latil, A., Cussenot, O.: Differential expression of 37 selected genes in hormone-refractory prostate cancer using quantitative taqman real-time rt-pcr. Int. J. Cancer. 114(2), 174–181 (2005)CrossRefGoogle Scholar
  56. 56.
    Pal, P., Xi, H., Kaushal, R., Sun, G., Jin, C.H., Jin, L., Suarez, B.K., Catalona, W.J., Deka, R.: Variants in the HEPSIN gene are associated with prostate cancer in men of european origin. Hum. Genet. 120(2), 187–192 (2006)CrossRefGoogle Scholar
  57. 57.
    Burmester, J.K., Suarez, B.K., Lin, J.H., Jin, C.H., Miller, R.D., Zhang, K.Q., Salzman, S.A., Reding, D.J., Catalona, W.J.: Analysis of candidate genes for prostate cancer. Hum Hered. 57(4), 172–178 (2004)CrossRefGoogle Scholar
  58. 58.
    Heinrich, R., Ben-Izhak, E.L.O., Aronheim, A.: The c-Jun dimerization protein 2 inhibits cell transformation and acts as a tumor suppressor gene. J. Biol. Chem. 279(7), 5708–5715 (2004)CrossRefGoogle Scholar
  59. 59.
    Mehraein-Ghomi, F., Lee, E., Church, D.R., Thompson, T.A., Basu, H.S., Wilding, G.: Jund mediates androgen-induced oxidative stress in androgen dependent lncap human prostate cancer cells. Prostate 68(9), 924–934 (2008)CrossRefGoogle Scholar
  60. 60.
    Polytarchou, C., Hatziapostolou, M., Papadimitriou, E.: Hydrogen peroxide stimulates proliferation and migration of human prostate cancer cells through activation of activator protein-1 and up-regulation of the heparin affin regulatory peptide gene. J. Biol. Chem. 280(49), 40428–40435 (2005)CrossRefGoogle Scholar
  61. 61.
    Zhang, J.S., Gong, A., Cheville, J.C., Smith, D.I., Young, C.Y.: Agr2, an androgen-inducible secretory protein overexpressed in prostate cancer. Genes Chromosomes Cancer. 43(3), 249–259 (2005)CrossRefGoogle Scholar
  62. 62.
    Zhang, Y., Forootan, S.S., Liu, D., Barraclough, R., Foster, C.S., Rudland, P.S., Ke, Y.: Increased expression of anterior gradient-2 is significantly associated with poor survival of prostate cancer patients. Prostate Cancer Prostatic Dis. 10(3), 293–300 (2007)CrossRefGoogle Scholar
  63. 63.
    LI, L.I.K., Shishkin, S.S., Khasigov, P.Z., Dzeranov, N.K., Kazachenko, A.V., Toropygin, I., Mamykina, S.V.: Identification of agr2 protein, a novel potential cancer marker, using proteomics technologies, [article in russian]. Prikl Biokhim Mikrobiol. 42(4), 480–484 (2006)Google Scholar
  64. 64.
    Wang, Z., Hao, Y., Lowe, A.W.: The adenocarcinoma-associated antigen, agr2, promotes tumor growth, cell migration, and cellular transformation. Cancer Res. 68(2), 492–497 (2008)CrossRefGoogle Scholar
  65. 65.
    Kristiansen, G., Pilarsky, C., Wissmann, C., Kaiser, S., Bruemmendorf, T., Roepcke, S., Dahl, E., Hinzmann, B., Specht, T., Pervan, J., Stephan, C., Loening, S., Dietel, M., Rosenthal, A.: Expression profiling of microdissected matched prostate cancer samples reveals CD166/MEMD and CD24 as new prognostic markers for patient survival. J. Pathol. 205(3), 359–376 (2005)CrossRefGoogle Scholar
  66. 66.
    Landers, K.A., Samaratunga, H., Teng, L., Buck, M., Burger, M.J., Scells, B., Lavin, M.F., Gardiner, R.A.: Identification of claudin-4 as a marker highly overexpressed in both primary and metastatic prostate cancer. Br. J. Cancer 99(3), 491–501 (2008)CrossRefGoogle Scholar
  67. 67.
    Kim, S.O., Lee, I.J., Choi, Y.H.: Genistein reduced the invasive activity of human breast carcinoma cells as a result of decreased tight junction permeability and modulation of tight junction proteins. Cancer Lett. (Epub ahead of print)Google Scholar
  68. 68.
    Hough, C.D., Sherman-Baust, C.A., Pizer, E.S., Montz, F.J., Im, D.D., Rosenshein, N.B., Cho, K.R., Riggins, G.J., Morin, P.J.: Large-scale serial analysis of gene expression reveals genes differentially expressed in ovarian cancer. Cancer Res. 60(22), 6281–6287 (2000)Google Scholar
  69. 69.
    Kleinberg, L., Holth, A., Trope, C.G., Reich, R., Davidson, B.: Claudin upregulation in ovarian carcinoma effusions is associated with poor survival. Hum Pathol. 39(5), 747–757 (2008)CrossRefGoogle Scholar
  70. 70.
    Long, H., Crean, C.D., Lee, W.H., Cummings, O.W., Gabig, T.G.: Expression of clostridium perfringens enterotoxin receptors claudin-3 and claudin-4 in prostate cancer epithelium. Cancer Res. 61(21), 7878–7881 (2001)Google Scholar
  71. 71.
    Morin, P.J.: Claudin proteins in human cancer: promising new targets for diagnosis and therapy. Cancer Res. 65(21), 9603–9606 (2005)CrossRefGoogle Scholar
  72. 72.
    Hewitt, K.J., Agarwal, R., Morin, P.J.: The claudin gene family: expression in normal and neoplastic tissues. BMC Cancer 6, 186 (2006)CrossRefGoogle Scholar
  73. 73.
    Nichols, L.S., Ashfaq, R., Iacobuzio-Donahue, C.A.: Claudin 4 protein expression in primary and metastatic pancreatic cancer: support for use as a therapeutic target. Am J. Clin. Pathol. 121(2), 226–230 (2004)CrossRefGoogle Scholar
  74. 74.
    Foss, C.A., Fox, J.J., Feldmann, G., Maitra, A., Iacobuzio-Donohue, C., Kern, S.E., Hruban, R., Pomper, M.G.: Radiolabeled anti-claudin 4 and anti-prostate stem cell antigen: initial imaging in experimental models of pancreatic cancer. Mol. Imaging. 6(2), 131–139 (2007)Google Scholar
  75. 75.
    Hanada, S., Maeshima, A., Matsuno, Y., Ohta, T., Ohki, M., Yoshida, T., Hayashi, Y., Yoshizawa, Y., Hirohashi, S., Sakamoto, M.: Expression profile of early lung adenocarcinoma: identification of mrp3 as a molecular marker for early progression. J. Pathol. 216(1), 75–82 (2008)CrossRefGoogle Scholar
  76. 76.
    Nishino, R., Honda, M., Yamashita, T., Takatori, H., Minato, H., Zen, Y., Sasaki, M., Takamura, H., Horimoto, K., Ohta, T., Nakanuma, Y., Kaneko, S.: Identification of novel candidate tumour marker genes for intrahepatic cholangiocarcinoma. J. Hepatol. 49(2), 207–216 (2008)CrossRefGoogle Scholar
  77. 77.
    Bello, I.O., Vilen, S.T., Niinimaa, A., Kantola, S., Soini, Y., Salo, T.: Expression of claudins 1, 4, 5, and 7 and occludin, and relationship with prognosis in squamous cell carcinoma of the tongue. Hum. Pathol. 39(8), 1212–1220 (2008)CrossRefGoogle Scholar
  78. 78.
    Ashton-Chess, J., Giral, M., Mengel, M., Renaudin, K., Foucher, Y., Gwinner, W., Braud, C., Dugast, E., Quillard, T., Thebault, P., Chiffoleau, E., Braudeau, C., Charreau, B., Soulillou, J., Brouard, S.: Tribbles-1 as a novel biomarker of chronic antibody-mediated rejection. J. Am. Soc. Nephrol. 19(6), 1116–1127 (2008), CrossRefGoogle Scholar
  79. 79.
    Röthlisberger, B., Heizmann, M., Bargetzi, M.J., Huber, A.R.: Trib1 overexpression in acute myeloid leukemia. Cancer Genet Cytogenet. 176(1), 58–60 (2007)CrossRefGoogle Scholar
  80. 80.
    Rücker, F.G., Bullinger, L., Schwaenen, C., Lipka, D.B., Wessendorf, S., Fröhling, S., Bentz, M., Miller, S., Scholl, C., Schlenk, R.F., Radlwimmer, B., Kestler, H.A., Pollack, J.R., Lichter, P., Döhner, K., Döhner, H.: Disclosure of candidate genes in acute myeloid leukemia with complex karyotypes using microarray-based molecular characterization. J. Clin. Oncol. 25(9), 1151–1152 (2007)CrossRefGoogle Scholar
  81. 81.
    Keeshan, K., Shestova, O., Ussin, L., Pear, W.S.: Tribbles homolog 2 (trib2) and hoxa9 cooperate to accelerate acute myelogenous leukemia. Blood Cells Mol. Dis. 40(1), 119–121 (2008)CrossRefGoogle Scholar
  82. 82.
    Puskas, L.G., Juhasz, F., Zarva, A., Hackler Jr., L., Farid, N.R.: Gene profiling identifies genes specific for well-differentiated epithelial thyroid tumors. Cell Mol. Biol. 51(2), 177–186 (2005)Google Scholar
  83. 83.
    Puiffe, M.L., Page, C.L., Filali-Mouhim, A., Zietarska, M., Ouellet, V., Tonin, P.N., Chevrette, M., Provencher, D.M., Mes-Masson, A.M.: Characterization of ovarian cancer ascites on cell invasion, proliferation, spheroid formation, and gene expression in an in vitro model of epithelial ovarian cancer. Neoplasia 9(10), 820–829 (2007)CrossRefGoogle Scholar
  84. 84.
    Mangs, A.H., Morris, B.J.: ZRANB2: Structural and functional insights into a novel splicing protein. Int. J. Biochem. Cell Biol. 40(11), 2353–2357 (2008)CrossRefGoogle Scholar
  85. 85.
    Leiblich, A., Cross, S.S., Catto, J.W., Phillips, J.T., Leung, H.Y., Hamdy, F.C., Rehman, I.: Lactate dehydrogenase-b is silenced by promoter hypermethylation in human prostate cancer. Oncogene 25(20), 2953–2960 (2006)CrossRefGoogle Scholar
  86. 86.
    Glen, A., Gan, C.S., Hamdy, F.C., Eaton, C.L., Cross, S.S., Catto, J.W., Wright, P.C., Rehman, I.: Itraq-facilitated proteomic analysis of human prostate cancer cells identifies proteins associated with progression. J. Proteome Res. 7(3), 897–907 (2008)CrossRefGoogle Scholar
  87. 87.
    Wingender, E.: The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation. Brief Bioinform. 9(4), 326–332 (2008)CrossRefGoogle Scholar
  88. 88.
    Kataoka, K., Noda, M., Nishizawa, M.: Maf nuclear oncoprotein recognizes sequences related to an ap-1 site and forms heterodimers with both fos and jun. Mol. Cell. Biol. 14(1), 700–712 (1994)Google Scholar
  89. 89.
    Hofer, M., Fecko, A., Shen, R., Setlur, S., Pienta, K., Tomlins, S., Chinnaiyan, A., Rubin, M.: Expression of the platelet-derived growth factor receptor in prostate cancer and treatment implications with tyrosine kinase inhibitors. Neoplasia 6(5), 503–512 (2004)CrossRefGoogle Scholar
  90. 90.
    Toffolatti, L., Gastaldo, L.R., Patarnello, T., Romualdi, C., Merlanti, R., Montesissa, C., Poppi, L., Castagnaro, M., Bargelloni, L.: Expression analysis of androgen-responsive genes in the prostate of veal calves treated with anabolic hormones. Domest. Anim. Endocrinol. 30(1), 38–55 (2006)CrossRefGoogle Scholar
  91. 91.
    So, A., Gleave, M., Hurtado-Col, A., Nelson, C.: Mechanisms of the development of androgen independence in prostate cancer. World J. Urol. 23(1), 1–9 (2005)CrossRefGoogle Scholar
  92. 92.
    Lapointe, J., Li, C., Higgins, J., van de Rijn, M., Bair, E., Montgomery, K., Ferrari, M., Egevad, L., Rayford, W., Bergerheim, U., Ekman, P., DeMarzo, A., Tibshirani, R., Botstein, D., Brown, P., Brooks, J., Pollack, J.: Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc. Natl. Acad. Sci. USA 101(3), 811–816 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Martín Gómez Ravetti
    • 1
  • Regina Berretta
    • 1
  • Pablo Moscato
    • 1
  1. 1.Centre for Bioinformatics, Biomarker Discovery and Information-BasedMedicine, The University of Newcastle, Australian Research Council Centre of Excellence in BioinformaticsCallaghanAustralia

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