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Linked References

1. K. Kubo, S. Matsuyama, K. Katayama et al., “Frequent expression of the c-kit proto-oncogene in canine malignant mammary tumor,” Journal of Veterinary Medical Science, vol. 60, no. 12, pp. 1335–1340, 1998. View at Scopus
2. B. Vogelstein and K. W. Kinzler, “The multistep nature of cancer,” Trends in Genetics, vol. 9, no. 4, pp. 138–141, 1993. View at Publisher · View at Google Scholar · View at Scopus
3. V. E. Velculescu, L. Zhang, W. Zhou et al., “Characterization of the yeast transcriptome,” Cell, vol. 88, no. 2, pp. 243–251, 1997. View at Publisher · View at Google Scholar · View at Scopus
4. M. R. Wilkins, C. Pasquali, R. D. Appel, et al., “From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis,” Biotechnology, vol. 14, no. 1, pp. 61–65, 1996.
5. S. G. Oliver, M. K. Winson, D. B. Kell, and F. Baganz, “Systematic functional analysis of the yeast genome,” Trends in Biotechnology, vol. 16, no. 9, pp. 373–378, 1998. View at Publisher · View at Google Scholar · View at Scopus
6. T. Feizi, “Progress in deciphering the information content of the “glycome”—a crescendo in the closing years of the millennium,” Glycoconjugate Journal, vol. 17, no. 7-9, pp. 553–565, 2000. View at Publisher · View at Google Scholar · View at Scopus
7. R. C. Lee, R. L. Feinbaum, and V. Ambros, “The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14,” Cell, vol. 75, no. 5, pp. 843–854, 1993. View at Publisher · View at Google Scholar · View at Scopus
8. U. Maskos and E. M. Southern, “Oligonucleotide hybridisations on glass supports: a novel linker for oligonucleotide synthesis and hybridisation properties of oligonucleotides synthesised in situ,” Nucleic Acids Research, vol. 20, no. 7, pp. 1679–1684, 1992. View at Scopus
9. H. J. Lee, T. T. Goodrich, and R. M. Corn, “SPR imaging measurements of 1-D and 2-D DNA microarrays created from microfluidic channels on gold thin films,” Analytical Chemistry, vol. 73, no. 22, pp. 5525–5531, 2001. View at Publisher · View at Google Scholar · View at Scopus
10. J. G. Hacia, J. B. Fan, O. Ryder et al., “Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays,” Nature Genetics, vol. 22, no. 2, pp. 164–167, 1999. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
11. D. Shalon, S. J. Smith, and P. O. Brown, “A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization,” Genome Research, vol. 6, no. 7, pp. 639–645, 1996. View at Scopus
12. Y. Zhang, J. Szustakowski, and M. Schinke, “Bioinformatics analysis of microarray data,” Methods in Molecular Biology, vol. 573, pp. 259–284, 2009. View at Scopus
13. Y. F. Leung and D. Cavalieri, “Fundamentals of cDNA microarray data analysis,” Trends in Genetics, vol. 19, no. 11, pp. 649–659, 2003. View at Publisher · View at Google Scholar · View at Scopus
14. I. Priness, O. Maimon, and I. Ben-Gal, “Evaluation of gene-expression clustering via mutual information distance measure,” BMC Bioinformatics, vol. 8, article 111, 2007. View at Publisher · View at Google Scholar · View at PubMed
15. L. Li, “Dimension reduction for high-dimensional data,” Methods in Molecular Biology, vol. 620, pp. 417–434, 2010. View at Publisher · View at Google Scholar · View at Scopus
16. J. H. Do and D. K. Choi, “Clustering approaches to identifying gene expression patterns from DNA microarray data,” Molecules and Cells, vol. 25, no. 2, pp. 279–288, 2008. View at Scopus
17. A. Brazma, P. Hingamp, J. Quackenbush et al., “Minimum information about a microarray experiment (MIAME)-toward standards for microarray data,” Nature Genetics, vol. 29, no. 4, pp. 365–371, 2001. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
18. L. Shi, L. H. Reid, W. D. Jones, et al., “The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements,” Nature Biotechnology, vol. 24, no. 9, pp. 1151–1161, 2006. View at Publisher · View at Google Scholar · View at PubMed
19. R. Kothapalli, S. J. Yoder, S. Mane, and T. P. Loughran, “Microarray result: how accurate are they?” BMC Bioinformatics, vol. 3, article 22, 2002. View at Publisher · View at Google Scholar
20. Z. Wang, M. Gerstein, and M. Snyder, “RNA-Seq: a revolutionary tool for transcriptomics,” Nature Reviews Genetics, vol. 10, no. 1, pp. 57–63, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
21. M. Meyerson, S. Gabriel, and G. Getz, “Advances in understanding cancer genomes through second-generation sequencing,” Nature Reviews Genetics, vol. 11, no. 10, pp. 685–696, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
22. D. A. Wheeler, M. Srinivasan, M. Egholm et al., “The complete genome of an individual by massively parallel DNA sequencing,” Nature, vol. 452, no. 7189, pp. 872–876, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
23. T. J. Ley, E. R. Mardis, L. Ding et al., “DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome,” Nature, vol. 456, no. 7218, pp. 66–72, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
24. R. D. Morin, M. Bainbridge, A. Fejes et al., “Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing,” BioTechniques, vol. 45, no. 1, pp. 81–94, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
25. C. A. Maher, C. Kumar-Sinha, X. Cao et al., “Transcriptome sequencing to detect gene fusions in cancer,” Nature, vol. 458, no. 7234, pp. 97–101, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
26. N. Cloonan, A. R. R. Forrest, G. Kolle et al., “Stem cell transcriptome profiling via massive-scale mRNA sequencing,” Nature Methods, vol. 5, no. 7, pp. 613–619, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
27. S. J. Emrich, W. B. Barbazuk, L. Li, and P. S. Schnable, “Gene discovery and annotation using LCM-454 transcriptome sequencing,” Genome Research, vol. 17, no. 1, pp. 69–73, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
28. P. Milos, “Helicos BioSciences,” Pharmacogenomics, vol. 9, no. 4, pp. 477–480, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
29. M. Margulies, M. Egholm, W. E. Altman et al., “Genome sequencing in microfabricated high-density picolitre reactors,” Nature, vol. 437, no. 7057, pp. 376–380, 2006. View at Publisher · View at Google Scholar · View at Scopus
30. E. R. Mardis, “Next-generation DNA sequencing methods,” Annual Review of Genomics and Human Genetics, vol. 9, pp. 387–402, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
31. S. C. Schuster, “Next-generation sequencing transforms today's biology,” Nature Methods, vol. 5, no. 1, pp. 16–18, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
32. A. Valouev, J. Ichikawa, T. Tonthat et al., “A high-resolution, nucleosome position map of C. elegans reveals a lack of universal sequence-dictated positioning,” Genome Research, vol. 18, no. 7, pp. 1051–1063, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
33. J. C. Vera, C. W. Wheat, H. W. Fescemyer et al., “Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing,” Molecular Ecology, vol. 17, no. 7, pp. 1636–1647, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
34. J. Shendure, “The beginning of the end for microarrays?” Nature Methods, vol. 5, no. 7, pp. 585–587, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
35. A. Mortazavi, B. A. Williams, K. McCue, L. Schaeffer, and B. Wold, “Mapping and quantifying mammalian transcriptomes by RNA-Seq,” Nature Methods, vol. 5, no. 7, pp. 621–628, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
36. B. T. Wilhelm, S. Marguerat, S. Watt et al., “Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution,” Nature, vol. 453, no. 7199, pp. 1239–1243, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
37. Y. Chen, J. A. L. Gelfond, L. M. McManus, and P. K. Shireman, “Reproducibility of quantitative RT-PCR array in miRNA expression profiling and comparison with microarray analysis,” BMC Genomics, vol. 10, article 1471, p. 407, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
38. S. Griffiths-Jones, H. K. Saini, S. Van Dongen, and A. J. Enright, “miRBase: tools for microRNA genomics,” Nucleic Acids Research, vol. 36, no. 1, pp. D154–D158, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
39. S. Griffiths-Jones, R. J. Grocock, S. van Dongen, A. Bateman, and A. J. Enright, “miRBase: microRNA sequences, targets and gene nomenclature,” Nucleic Acids Research, vol. 34, pp. D140–D144, 2006. View at Scopus
40. S. Griffiths-Jones, “The microRNA registry,” Nucleic Acids Research, vol. 32, pp. D109–D111, 2004. View at Scopus
41. M. Dufva, J. Petersen, and L. Poulsen, “Increasing the specificity and function of DNA microarrays by processing arrays at different stringencies,” Analytical and Bioanalytical Chemistry, vol. 395, no. 3, pp. 669–677, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
42. S. Sharbati-Tehrani, B. Kutz-Lohroff, R. Bergbauer, J. Scholven, and R. Einspanier, “MiR-Q: a novel quantitative RT-PCR approach for the expression profiling of small RNA molecules such as miRNAs in a complex sample,” BMC Molecular Biology, vol. 9, article 34, 2008. View at Publisher · View at Google Scholar · View at PubMed
43. C. Chen, D. A. Ridzon, A. J. Broomer et al., “Real-time quantification of microRNAs by stem-loop RT-PCR,” Nucleic Acids Research, vol. 33, no. 20, p. e179, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
44. R. Klopfleisch and A. D. Gruber, “Derlin-1 and Stanniocalcin-1 are Differentially Regulated in Metastasizing Canine Mammary Adenocarcinomas,” Journal of Comparative Pathology, vol. 141, no. 2-3, pp. 113–120, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
45. R. Klopfleisch and A. D. Gruber, “Differential expression of cell cycle regulators p21, p27 and p53 in metastasizing canine mammary adenocarcinomas versus normal mammary glands,” Research in Veterinary Science, vol. 87, no. 1, pp. 91–96, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
46. R. Klopfleisch and A. D. Gruber, “Increased expression of BRCA2 and RAD51 in lymph node metastases of canine mammary adenocarcinomas,” Veterinary Pathology, vol. 46, no. 3, pp. 416–422, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
47. D. Greenbaum, C. Colangelo, K. Williams, and M. Gerstein, “Comparing protein abundance and mRNA expression levels on a genomic scale,” Genome Biology, vol. 4, no. 9, article 117, 2003. View at Publisher · View at Google Scholar · View at PubMed
48. J. M. Gilmore and M. P. Washburn, “Advances in shotgun proteomics and the analysis of membrane proteomes,” Journal of Proteomics, vol. 73, no. 11, pp. 2078–2091, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
49. T. Kislinger and A. O. Gramolini, “Proteome analysis of mouse model systems: a tool to model human disease and for the investigation of tissue-specific biology,” Journal of Proteomics, vol. 73, no. 11, pp. 2205–2218, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
50. P. H. O'Farrell, “High resolution two dimensional electrophoresis of proteins,” Journal of Biological Chemistry, vol. 250, no. 10, pp. 4007–4021, 1975. View at Scopus
51. J. Klose, “Protein mapping by combined isoelectric focusing and electrophoresis of mouse tissues. A novel approach to testing for induced point mutations in mammals,” HUMANGENETIK, vol. 26, no. 3, pp. 231–243, 1975. View at Scopus
52. B. Bjellqvist, K. Ek, P. Giorgio Righetti et al., “Isoelectric focusing in immobilized pH gradients: principle, methodology and some applications,” Journal of Biochemical and Biophysical Methods, vol. 6, no. 4, pp. 317–339, 1982. View at Scopus
53. U. K. Laemmli, “Cleavage of structural proteins during the assembly of the head of bacteriophage T4,” Nature, vol. 227, no. 5259, pp. 680–685, 1970. View at Publisher · View at Google Scholar · View at Scopus
54. T. S. Meyer and B. L. Lamberts, “Use of coomassie brilliant blue R250 for the electrophoresis of microgram quantities of parotid saliva proteins on acrylamide-gel strips,” Biochimica et Biophysica Acta, vol. 107, no. 1, pp. 144–145, 1965. View at Scopus
55. T. Rabilloud, G. Carpentier, and P. Tarroux, “Improvement and simplification of low-background silver staining of proteins by using sodium dithionite,” Electrophoresis, vol. 9, no. 6, pp. 288–291, 1988. View at Scopus
56. K. Berggren, T. H. Steinberg, W. M. Lauber et al., “A luminescent ruthenium complex for ultrasensitive detection of proteins immobilized on membrane supports,” Analytical Biochemistry, vol. 276, no. 2, pp. 129–143, 1999. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
57. M. Berth, F. M. Moser, M. Kolbe, and J. Bernhardt, “The state of the art in the analysis of two-dimensional gel electrophoresis images,” Applied Microbiology and Biotechnology, vol. 76, no. 6, pp. 1223–1243, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
58. M. Ünlü, M. E. Morgan, and J. S. Minden, “Difference gel electrophoresis: a single gel method for detecting changes in protein extracts,” Electrophoresis, vol. 18, no. 11, pp. 2071–2077, 1997. View at Scopus
59. M. Stessl, C. R. Noe, and B. Lachmann, “Influence of image-analysis software on quantitation of two-dimensional gel electrophoresis data,” Electrophoresis, vol. 30, no. 2, pp. 325–328, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
60. D. B. Friedman, S. Hoving, and R. Westermeier, “Chapter 30 isoelectric focusing and two-dimensional gel electrophoresis,” Methods in Enzymology, vol. 463, no. C, pp. 515–540, 2009. View at Publisher · View at Google Scholar · View at Scopus
61. M. Mann, P. Hojrup, and P. Roepstorff, “Use of mass spectrometric molecular weight information to identify proteins in sequence databases,” Biological Mass Spectrometry, vol. 22, no. 6, pp. 338–345, 1993. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
62. F. Hillenkamp, M. Karas, R. C. Beavis, and B. T. Chait, “Matrix-assisted laser desorption/ionization mass spectrometry of biopolymers,” Analytical Chemistry, vol. 63, no. 24, pp. 1193A–1203A, 1991. View at Scopus
63. W. J. Henzel, T. M. Billeci, J. T. Stults, S. C. Wong, C. Grimley, and C. Watanabe, “Identifying proteins from two-dimensional gels by molecular mass searching of peptide fragments in protein sequence databases,” Proceedings of the National Academy of Sciences of the United States of America, vol. 90, no. 11, pp. 5011–5015, 1993. View at Scopus
64. R. Klopfleisch, P. Klose, C. Weise et al., “Proteome of metastatic canine mammary carcinomas: similarities to and differences from human breast cancer,” Journal of Proteome Research, vol. 9, no. 12, pp. 6380–6391, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
65. S. P. Gygi, G. L. Corthals, Y. Zhang, Y. Rochon, and R. Aebersold, “Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 17, pp. 9390–9395, 2000. View at Scopus
66. M. Fountoulakis, M. F. Takács, P. Berndt, H. Langen, and B. Takács, “Enrichment of low abundance proteins of Escherichia coli by hydroxyapatite chromatography,” Electrophoresis, vol. 20, no. 11, pp. 2181–2195, 1999. View at Publisher · View at Google Scholar · View at Scopus
67. C. L. Corthals, V. C. Wasinger, D. F. Hochstrasser, and J. C. Sanchez, “The dynamic range of protein expression: a challenge for proteomic research,” Electrophoresis, vol. 21, no. 6, pp. 1104–1115, 2000. View at Publisher · View at Google Scholar · View at Scopus
68. M. Oh-Ishi, M. Satoh, and T. Maeda, “Preparative two-dimensional gel electrophoresis with agarose gels in the first dimension for high molecular mass proteins,” Electrophoresis, vol. 21, no. 9, pp. 1653–1669, 2000. View at Publisher · View at Google Scholar · View at Scopus
69. V. Santoni, M. Molloy, and T. Rabilloud, “Membrane proteins and proteomics: un amour impossible?” Electrophoresis, vol. 21, no. 6, pp. 1054–1070, 2000. View at Publisher · View at Google Scholar · View at Scopus
70. M. P. Washburn, D. Wolters, and J. R. Yates, “Large-scale analysis of the yeast proteome by multidimensional protein identification technology,” Nature Biotechnology, vol. 19, no. 3, pp. 242–247, 2001. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
71. P. Mauri and M. Scigelova, “Multidimensional protein identification technology for clinical proteomic analysis,” Clinical Chemistry and Laboratory Medicine, vol. 47, no. 6, pp. 636–645, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
72. M. L. Fournier, J. M. Gilmore, S. A. Martin-Brown, and M. P. Washburn, “Multidimensional separations-based shotgun proteomics,” Chemical Reviews, vol. 107, no. 8, pp. 3654–3686, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
73. V. Sanz-Nebot, E. Balaguer, F. Benavente, C. Neusüß, and J. Barbosa, “Characterization of transferrin glycoforms in human serum by CE-UV and CE-ESI-MS,” Electrophoresis, vol. 28, no. 12, pp. 1949–1957, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
74. K. G. Kline, G. L. Finney, and C. C. Wu, “Quantitative strategies to fuel the merger of discovery and hypothesis-driven shotgun proteomics,” Briefings in Functional Genomics and Proteomics, vol. 8, no. 2, pp. 114–125, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
75. J. Z. Bereszczak and F. L. Brancia, “Offline and online liquid chromatography mass spectrometry in quantitative proteomics,” Combinatorial Chemistry and High Throughput Screening, vol. 12, no. 2, pp. 185–193, 2009. View at Publisher · View at Google Scholar · View at Scopus
76. B. Zybailov, M. K. Coleman, L. Florens, and M. P. Washburn, “Correlation of relative abundance ratios derived from peptide ion chromatograms and spectrum counting for quantitative proteomic analysis using stable isotope labeling,” Analytical Chemistry, vol. 77, no. 19, pp. 6218–6224, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
77. H. Liu, R. G. Sadygov, and J. R. Yates, “A model for random sampling and estimation of relative protein abundance in shotgun proteomics,” Analytical Chemistry, vol. 76, no. 14, pp. 4193–4201, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
78. K. G. Kline, B. Frewen, M. R. Bristow, M. J. MacCoss, and C. C. Wu, “High quality catalog of proteotypic peptides from human heart,” Journal of Proteome Research, vol. 7, no. 11, pp. 5055–5061, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
79. Y. Oda, K. Huang, F. R. Cross, D. Cowburn, and B. T. Chait, “Accurate quantitation of protein expression and site-specific phosphorylation,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 12, pp. 6591–6596, 1999. View at Publisher · View at Google Scholar · View at Scopus
80. C. C. Wu, M. J. MacCoss, K. E. Howell, D. E. Matthews, and J. R. Yates, “Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis,” Analytical Chemistry, vol. 76, no. 17, pp. 4951–4959, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
81. K. Michael, B. G. Klupp, T. C. Mettenleiter, and A. Karger, “Composition of pseudorabies virus particles lacking tegument protein US3, UL47, or UL49 or envelope glycoprotein E,” Journal of Virology, vol. 80, no. 3, pp. 1332–1339, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
82. S. P. Gygi, B. Rist, S. A. Gerber, F. Turecek, M. H. Gelb, and R. Aebersold, “Quantitative analysis of complex protein mixtures using isotope-coded affinity tags,” Nature Biotechnology, vol. 17, no. 10, pp. 994–999, 1999. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
83. P. L. Ross, Y. N. Huang, J. N. Marchese et al., “Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents,” Molecular and Cellular Proteomics, vol. 3, no. 12, pp. 1154–1169, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
84. V. Kulasingam and E. P. Diamandis, “Strategies for discovering novel cancer biomarkers through utilization of emerging technologies,” Nature Clinical Practice Oncology, vol. 5, no. 10, pp. 588–599, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
85. G. I. Abelev, S. D. Perova, N. I. Khramkova, Z. A. Postnikova, and I. S. Irlin, “Embryonal serum alpha- globulin and its synthesis by transplantable mouse hepatomas,” Biokhimiia, vol. 28, pp. 625–634, 1963.
86. P. Gold and S. O. Freedman, “Demonstration of tumor-specific antigens in human colonic carcinomata by immunological tolerance and absorption techniques,” The Journal of Experimental Medicine, vol. 121, pp. 439–462, 1965. View at Scopus
87. C. J. Henry, “Biomarkers in veterinary cancer screening: applications, limitations and expectations,” Veterinary Journal, vol. 185, no. 1, pp. 10–14, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
88. K. Lindblad-Toh, C. M. Wade, T. S. Mikkelsen et al., “Genome sequence, comparative analysis and haplotype structure of the domestic dog,” Nature, vol. 438, no. 7069, pp. 803–819, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
89. D. W. Burt, “The chicken genome,” Genome Dynamics, vol. 2, pp. 123–137, 2006.
90. R. L. Tellam, D. G. Lemay, C. P. Van Tassell, H. A. Lewin, K. C. Worley, and C. G. Elsik, “Unlocking the bovine genome,” BMC Genomics, vol. 10, p. 193, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
91. W. J. Murphy, “The feline genome,” Genome Dynamics, vol. 2, pp. 60–68, 2006.
92. B. P. Chowdhary and T. Raudsepp, “The horse genome,” Genome Dynamics, vol. 2, pp. 97–110, 2006.
93. B. Matharoo-Ball, A. K. Miles, C. S. Creaser, G. Ball, and R. Rees, “Serum biomarker profiling in cancer studies: a question of standardisation?” Veterinary and Comparative Oncology, vol. 6, no. 4, pp. 224–247, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
94. T. Reynolds, “Validating biomarkers: early detection research network launches first phase III study,” Journal of the National Cancer Institute, vol. 95, no. 6, pp. 422–423, 2003. View at Scopus
95. L. A. Liotta, M. Ferrari, and E. Petricoin, “Clinical proteomics: written in blood,” Nature, vol. 425, no. 6961, p. 905, 2003. View at Scopus
96. P. Wang, J. R. Whiteaker, and A. G. Paulovich, “The evolving role of mass spectrometry in cancer biomarker discovery,” Cancer Biology and Therapy, vol. 8, no. 12, pp. 1083–1094, 2009. View at Scopus
97. S. A. Carr and L. Anderson, “Protein quantitation through targeted mass spectrometry: the way out of biomarker purgatory?” Clinical Chemistry, vol. 54, no. 11, pp. 1749–1752, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
98. N. L. Anderson, N. G. Anderson, T. W. Pearson et al., “A human proteome detection and quantitation project,” Molecular and Cellular Proteomics, vol. 8, no. 5, pp. 883–886, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
99. D. S. A. Nuyten and M. J. van de Vijver, “Using microarray analysis as a prognostic and predictive tool in Oncology: focus oVn breast cancer and normal tissue toxicity,” Seminars in Radiation Oncology, vol. 18, no. 2, pp. 105–114, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
100. L. J. van't Veer, H. Dai, M. J. Van de Vijver et al., “Gene expression profiling predicts clinical outcome of breast cancer,” Nature, vol. 415, no. 6871, pp. 530–536, 2002. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
101. R. Klopfleisch, P. Klose, and A. D. Gruber, “The combined expression pattern of BMP2, LTBP4, and DERL1 discriminates malignant from benign canine mammary tumors,” Veterinary Pathology, vol. 47, no. 3, pp. 446–454, 2010. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
102. R. Klopfleisch, D. Lenze, M. Hummel, and A. D. Gruber, “Metastatic canine mammary carcinomas can be identified by a gene expression profile that partly overlaps with human breast cancer profiles,” BMC Cancer, vol. 10, article 618, 2010. View at Publisher · View at Google Scholar · View at PubMed
103. S. Michiels, S. Koscielny, and C. Hill, “Prediction of cancer outcome with microarrays: a multiple random validation strategy,” Lancet, vol. 365, no. 9458, pp. 488–492, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
104. R. Klopfleisch, D. Lenze, M. Hummel, and A. D. Gruber, “The metastatic cascade is reflected in the transcriptome of metastatic canine mammary carcinomas,” Veterinary Journal. In press. View at Publisher · View at Google Scholar · View at PubMed
105. P. Uva, L. Aurisicchio, J. Watters et al., “Comparative expression pathway analysis of human and canine mammary tumors,” BMC Genomics, vol. 10, article 135, 2009. View at Publisher · View at Google Scholar · View at PubMed
106. M. Krol, K. M. Pawlowski, J. Skierski et al., “Transcriptomic profile of two canine mammary cancer cell lines with different proliferative and anti-apoptotic potential,” Journal of Physiology and Pharmacology, vol. 60, supplement 1, pp. 95–106, 2009.
107. N. A.S. Rao, M. E. Van Wolferen, A. Gracanin et al., “Gene expression profiles of progestin-induced canine mammary hyperplasia and spontaneous mammary tumors,” Journal of Physiology and Pharmacology, vol. 60, supplement 1, pp. 73–84, 2009.
108. N. A. S. Rao, M. E. Van Wolferen, R. Van Den Ham et al., “cDNA microarray profiles of canine mammary tumour cell lines reveal deregulated pathways pertaining to their phenotype,” Animal Genetics, vol. 39, no. 4, pp. 333–345, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
109. D. L. McCaw, A. S. Chan, A. L. Stegner et al., “Proteomics of canine lymphoma identifies potential cancer-specific protein markers,” Clinical Cancer Research, vol. 13, no. 8, pp. 2496–2503, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
110. P. Klose, C. Weise, A. Bondzio et al., “Is there a malignant progression associated with a linear change in protein expression levels from normal canine mammary gland to metastatic mammary tumors?” Journal of Proteome Research, vol. 10, no. 10, pp. 4405–4415, 2011. View at Publisher · View at Google Scholar · View at PubMed
111. C. M. Perou, T. Sørile, M. B. Eisen et al., “Molecular portraits of human breast tumours,” Nature, vol. 406, no. 6797, pp. 747–752, 2000. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
112. T. Sørlie, C. M. Perou, R. Tibshirani et al., “Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 19, pp. 10869–10874, 2001. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
113. H. Y. Chang, J. B. Sneddon, A. A. Alizadeh et al., “Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds,” PLoS Biology, vol. 2, no. 2, p. E7, 2004. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
114. H. F. Dvorak, “Tumors: wounds that do not heal: similarities between tumor stroma generation and wound healing,” New England Journal of Medicine, vol. 315, no. 26, pp. 1650–1659, 1986. View at Scopus
115. H. Y. Chang, D. S. A. Nuyten, J. B. Sneddon et al., “Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 10, pp. 3738–3743, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus