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

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Foundations of Computational Intelligence Volume 5

Part of the book series: Studies in Computational Intelligence ((SCI,volume 205))

Summary

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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References

  1. Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)

    Article  Google Scholar 

  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. The prostate cancer foundation of australia (08/08/2008 2007)

    Google Scholar 

  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. Cotta, C., Moscato, P.: The k-Feature Set problem is W[2]-complete. Journal of Computer and System Sciences 67(4), 686–690 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  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. Downey, R., Fellows, M.: Parameterized Complexity. Mongraphs in Computer Science. Springer, Heidelberg (1999)

    Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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_20

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  21. Shah, S., Kusiak, A.: Cancer gene search with data-mining and genetic algorithms. Computers in Biology and Medicine 37(2), 251–261 (2007)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  47. Wu, Q., Parry, G.: Hepsin and prostate cancer. Front Biosci. 12, 5052–5059 (2007)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Huppi, K., Chandramouli, G.V.: Molecular profiling of prostate cancer. Curr. Urol. Rep. 5(1), 45–51 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. Morin, P.J.: Claudin proteins in human cancer: promising new targets for diagnosis and therapy. Cancer Res. 65(21), 9603–9606 (2005)

    Article  Google Scholar 

  72. Hewitt, K.J., Agarwal, R., Morin, P.J.: The claudin gene family: expression in normal and neoplastic tissues. BMC Cancer 6, 186 (2006)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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), http://jasn.asnjournals.org/cgi/content/abstract/19/6/1116

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Centre for Bioinformatics, Biomarker Discovery and Information-Based, Medicine, The University of Newcastle, Australian Research Council Centre of Excellence in Bioinformatics, Callaghan, NSW, 2308, Australia

    Martín Gómez Ravetti, Regina Berretta & Pablo Moscato

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  1. Martín Gómez Ravetti
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  2. Regina Berretta
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  3. Pablo Moscato
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Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Washington, USA

    Ajith Abraham

  2. Faculty of Computers and Information, Information Technology Department, Cairo University, 5 Ahmed Zewal St., Orman, Giza

    Aboul-Ella Hassanien

  3. Dept. Computer Science, Technical University Ostrava, Tr. 17. Listopadu 15, 708 33, Ostrava, Czech Republic

    Václav Snášel

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© 2009 Springer-Verlag Berlin Heidelberg

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Ravetti, M.G., Berretta, R., Moscato, P. (2009). Novel Biomarkers for Prostate Cancer Revealed by (α,β)-k-Feature Sets. In: Abraham, A., Hassanien, AE., Snášel, V. (eds) Foundations of Computational Intelligence Volume 5. Studies in Computational Intelligence, vol 205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01536-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-01536-6_7

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