Skip to main content

Advertisement

Log in

Contributions of advanced proteomics technologies to cancer diagnosis

  • Educational Series
  • Red Series
  • Published:
Clinical and Translational Oncology Aims and scope Submit manuscript

Abstract

The ability of Medicine to effectively treat and cure cancer is directly dependent on their capability to detect cancers at their earliest stages. The advent of proteomics has brought with it the hope of discovering novel biomarkers in the early phases of tumorigenesis that can be used to diagnose diseases, predict susceptibility, and monitor progression. This discipline incorporates technologies that can be applied to complex biosystems such as serum and tissue in order to characterize the content of, and changes in, the proteome induced by physiological changes, benign or pathologic. These tools include 2-DE, 2D-DIGE, ICAT, protein arrays, MudPIT and mass spectrometries including SELDI-TOF. The application of these tools has assisted to uncover molecular mechanisms associated with cancer at the global level and may lead to new diagnostic tests and improvements in therapeutics. In this review these approaches are evaluated in the context of their contribution to cancer biomarker discovery. Particular attention is paid to the promising contribution of the ProteinChip/SELDI-TOF platform as a revolutionary approach in proteomic patterns analysis that can be applied at the bedside for discovering protein profiles that distinguish disease and disease-free states with high sensitivity and specificity. Understanding the basic concepts and tools used will illustrate how best to apply these technologies for patient benefit for the early cancer detection and improved patient care.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. López, JA, Bernat A, Albar JP. Protein Expression Profiling Analysis in Hematopoietic Sttem Cells: Phenotipic of Mesenchymal Sttem Cells. In: Sánchez J-C, Corthals GL., Hochstrasser DF, eds. Biomedical Applications of Proteomic. Wiley-VCH: 2004, p. 155–70.

  2. Hanash, S., Integrated global profiling of cancer. Nat Rev Cancer. 2004;8:638–44.

    Google Scholar 

  3. Kolch W., Mischak H., Chalmers MJ, et al. Clinical proteomics: a question of technology. Rapid Commun. Mass Spectrom. 2004;18:2365–6.

    CAS  Google Scholar 

  4. Kolch W, Mischak H, Pitt AR. The molecular make-up of a tumour: proteomics in cancer research. Clin. Sci. (Lond.), 2005; 108:569–85.

    Google Scholar 

  5. Gygi SP, Corthals GL, Zhang Y, et al. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci. 2000;97:9390–5.

    PubMed  CAS  Google Scholar 

  6. Wilkins MR, Gasteiger E, Bairoch A, et al. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol. 1999; 112:551–52.

    Google Scholar 

  7. Yates JR. Mass spectrometry: from genomics to proteomics. Trends Genet. 2000;16:5–8.

    PubMed  CAS  Google Scholar 

  8. Gatlin CL, Eng JK, Cross ST, et al. Automated identification of amino acid sequence variations in proteins by HPLC/microscpray tandem mass spectrometry. Anal Chem. 2000;72:757–65.

    PubMed  CAS  Google Scholar 

  9. Alaiya A, Al-Mohanna M, Linder S. Clinical cancer proteomics: promises and pitfalls. Proteome Res. 2005;4:1215–22.

    Google Scholar 

  10. Anderson L, Seilhamer J. A comparison of selected mRNA and protein abundances in human liver. Electrophoresis. 1997;18:533–7.

    PubMed  CAS  Google Scholar 

  11. Chen G, Gharib TG, Wang H, et al. Protein profiles associated with survival in lung adenocarcinoma. Proc Natl Acad Sci USA, 2003;100:13537–42.

    PubMed  CAS  Google Scholar 

  12. Ginestier C, Charafe-Jauffret E, Bertucci F, et al. Distinct and complementary information provided by use of tissue and DNA microarrays in the study of breast tumor markers. Am J Pathol. 2002;161:1223–33.

    PubMed  CAS  Google Scholar 

  13. Alaiya A, Roblick U, Egevad L, et al. Polypeptide expression in prostate hyper-plasia and prostate adenocarcinoma. Anal Cell Pathol. 2000;21:1–9.

    PubMed  CAS  Google Scholar 

  14. Nishizuka S, Charboneau L, Young L, et al. Proteomic profiling of the NCI-60 cancer cell lines using new highdensity reverse-phase lysate microarrays. Proc Natl Acad Sci USA, 2005;100:14229–54.

    Google Scholar 

  15. Izzotti A, Bagnasco M, Cartiglia C, et al. Proteomic analysis as related to transcriptome data in the lung of chromium(VI)-treated rats. Int J Oncol. 2004;24:1515–22.

    Google Scholar 

  16. Zerkowski HR, Grussenmeyer T, Matt P, et al. Proteomics strategies in cardiovascular research. J Proteome Res. 2004;3:200–8.

    PubMed  CAS  Google Scholar 

  17. Donners MM, Verluyten MJ, Bouwman FG. Proteomic analysis of differential protein expression in human atherosclerotic plaque progression. J Pathol. 2005;206:39–45.

    PubMed  CAS  Google Scholar 

  18. Pierson J, Norris JL, Aerni HR, et al. Molecular profiling of experimental Parkinson's disease. Direct analysis of peptides and proteins on brain tissue sections by MALDI mass spectrometry. J Proteome Res. 2004;3:289–95.

    PubMed  CAS  Google Scholar 

  19. Castano EM, Roher AE, Esh CL, et al. Comparative proteomics of cerebrospinal fluid in neuropathologically-confirmed Alzheimer's disease and non-demented elderly subjects. Neurol Res. 2006;2:155–63.

    Google Scholar 

  20. Borozdenkova S, Westbrook JA, Patel V, et al. Use of proteomics to discover novel markers of cardiac allograft rejection. J Proteome Res. 2004;3:282–8.

    PubMed  CAS  Google Scholar 

  21. Bohring C, Krause W, Immune infertility: towards a better understanding of sperm (auto-) immunity. The value of proteomic analysis. Human Reprod. 2003;18:915–24.

    CAS  Google Scholar 

  22. Clarke W, Zhang Z, Chan DW. The application of clinical proteomics to cancer and other diseases. Clin Chem Lab Med. 2003;41:1562–70

    PubMed  CAS  Google Scholar 

  23. Wulfkuhle JD, Liotta LA, Petricoin EF. Proteomic applications for the early detection of cancer. Nat Rev Cancer. 2003;4:267–75.

    Google Scholar 

  24. Muddiman DC, Cliby W, Bergen R. Cancer biomarkers. Clin Lab News. 2003;7:12.

    Google Scholar 

  25. Wulfkuhle JD, Sgroi DC, Krutzsch H, et al. Proteomics of human breast ductal carcinomain situ. Cancer Res. 2002;62:6740–9.

    PubMed  CAS  Google Scholar 

  26. Anderson NL, Polanski M, Pieper R, et al. The human plasma proteome: a nonredundant list developed by combination of four separate sources. Mol Cell Proteomics. 2004;3(4):311–26.

    PubMed  CAS  Google Scholar 

  27. O'Farrell P. High resolution two dimensional electrophoresis of proteins. J Biol Chem. 1975;250:4007–21.

    PubMed  Google Scholar 

  28. Mann M, Hojrup P, Roepstorff P. Use of mass spectrometric molecular weight information to identify proteins in sequence databases. Biol Mass Spectrom. 1995;22: 338–45.

    Google Scholar 

  29. Unlu M, Morgan ME, Minden JS. Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis. 1997;18:2071–7.

    PubMed  CAS  Google Scholar 

  30. Monteoliva L, Albar JP. Differential proteomics: an overview of gel and non-gel based approaches. Brief Funct Genomic Proteomic. 2004;3:220–59.

    PubMed  CAS  Google Scholar 

  31. Zhou G, Li H, De Camp D, et al. 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers. Mol Cell Proteomics 2002;1:117–24.

    PubMed  CAS  Google Scholar 

  32. Somiari RI, Somiari S, Russell S, et al. Proteomics of breast carcinoma. J Chromatogr B 2005;815:215–25.

    CAS  Google Scholar 

  33. Huang HL, Stasyk T, Morandell S, et al. Biomarker discovery in breast cancer serum using 2-D differential gel electrophoresis/MALDI-TOF/TOF and data validation by routine clinical assays. Electrophoresis. 2006;27(8):1641–50.

    PubMed  CAS  Google Scholar 

  34. Alexander H, Stegner AL, Wagner-Mann C, et al. Proteomic Analysis to Identify Breast Cancer Biomarkers in Nipple Aspirate Fluid. Clin Cancer Res. 2004;10:7500–10.

    PubMed  CAS  Google Scholar 

  35. Bi X, Lin Q, Foo TW, et al. Proteomic analysis of colorectal cancer reveals alterations in metabolic pathways: mechanism of tumorigenesis. Mol Cell Proteomics 2006;5(6):119–30, 56

    Google Scholar 

  36. Gygi SP, Rist B, Gerber SA, et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 1999;17:994–9.

    PubMed  CAS  Google Scholar 

  37. Pawlik T, Hawke D, Liu Y, et al. Proteomic analysis of nipple aspirate fluid from women with early-stage breast cancer using isotope-coded affinity tags and tandem mass spectrometry reveals differential expression of vitamin D binding protein. BMC Cancer. 2006;6:68:1471–2407.

    Google Scholar 

  38. Li C, Hong Y, Tan YX, et al. Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry. Mol Cell Proteomics. 2004;3(4):599–509.

    Google Scholar 

  39. Stewart JJ, White JT, Yan X, et al. Proteins associated with Cisplatin resistance in ovarian cancer cells identified by quantitative proteomic technology and integrated with mRNA expression levels. Mol Cell Proteomics 2006;5(5):433–43.

    PubMed  CAS  Google Scholar 

  40. DeSouza L, Diehl MJ, Rodriguez J, et al. Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cICAT with multidimensional liquid chromatography and tandem mass spectrometry. J Proteome Res. 2005;4:377–86.

    PubMed  CAS  Google Scholar 

  41. Keshamouni VG, Michailidis G, Grasso CS, et al. Differential protein expression profiling by iTRAQ-2DLC-MS/MS of lung cancer cells undergoing epithelial-mesenchymal transition reveals a migratory/invasive phenotype. J Proteome Res. 2006;5(5):1145–54.

    Google Scholar 

  42. Ong SE, Blagoev B, Kratchmarova I, et al. Stable isotope labelling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics, 2002;1(5):376–86.

    PubMed  CAS  Google Scholar 

  43. Gronborg M, Kristiansen TZ, Iwahori A, et al. Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Mol Cell Proteomics. 2006;1:157–71.

    Google Scholar 

  44. Mauri P, Scarpa A, Nascimbeni AC, et al. Identification of proteins released by pancreatic cancer cells by multidimensional protein identification technology: a strategy for identification of novel cancer markers. FASEB J. 2005;19(9):1125–7.

    PubMed  CAS  Google Scholar 

  45. Jessani N, Niessen S, Wei BQ, et al. A streamlined platform for high-content functional proteomics of primary human specimens. Nat Methods, 2005;2:691–7

    PubMed  CAS  Google Scholar 

  46. Haab BB. Antibody Arrays in Cancer Research. Mol Cell Proteomics. 2005;4:377–83.

    PubMed  CAS  Google Scholar 

  47. Kusnezow W, Jacob A, Walijew A, et al. Antibody microarrays: an evaluation of production parameters. Proteomics. 2003; 5:254–64.

    Google Scholar 

  48. Sreekumar A, Nyati MK, Varambally S, et al. Profiling of cancer cells using protein microarrays: discovery of novel radiation-regulated proteins. Cancer Res. 2001;61:7585–93.

    PubMed  CAS  Google Scholar 

  49. Ghobrial IM, McCormick DJ, kaufmann SH, et al. Proteomic analysis of mantle-cell lymphoma by protein microarray. Blood. 2005;105:3722–30.

    PubMed  CAS  Google Scholar 

  50. Zhang Z, Bast RC, Yu Y, et al. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Res. 2004;64:5882–90.

    PubMed  CAS  Google Scholar 

  51. Plebani M: Proteomics: the next revolution in laboratory medicine? Clin Chim Acta. 2005;357(2):113–22.

    PubMed  CAS  Google Scholar 

  52. Diamandis EP, Point proteomic patterns in biological fluids: do they represent the future of cancer diagnostics. Clin Chem. 2003;49:1272–8.

    PubMed  CAS  Google Scholar 

  53. Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid. Commun. Mass Spectrom. 1995;7:576–80.

    Google Scholar 

  54. Issaq HJ, Veenstra TD, Conrads TP, et al. The SELDI-TOF-MS approach to proteomics: protein profiling and biomarker identification. Biochem Biophys Res Commun. 2002;292:587–92.

    PubMed  CAS  Google Scholar 

  55. Clarke CH, Buckley JA and Fung ET. SELDI-TOF-MS proteomics of breast cancer. Clin Chem Lab Med. 2005;43:1314–20.

    PubMed  CAS  Google Scholar 

  56. Diamandis EP. Mass spectrometry as a diagnostic and a cancer biomarker discovery tool. Mol Cell Proteomics. 2004;3:567–78.

    Google Scholar 

  57. Diamandis EP. Analysis of serum proteomic patterns for early cancer diagnosis: drawing attention to potential problems. J. Natl Cancer Inst. 2004;96:353–6.

    Article  PubMed  Google Scholar 

  58. Kozak KR, Amneus MW, Pusey SM, et al. Identification of biomarkers for ovarian cancer using strong anion-exchange Protein Chips: Potential use in diagnosis and prognosis. Proc Natl Acad Sci USA. 2003; 100:12343–8.

    PubMed  CAS  Google Scholar 

  59. Petricoin III EF, Omstein DK, Paweletz CP, et al. Serum proteomic patterns for detection of prostate cancer. J. Natl Cancer Inst. 2002;94:1576–8.

    PubMed  CAS  Google Scholar 

  60. Won Y, Song HJ, Kang TW, et al. Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons. Proteomics. 2003;3:2510–6.

    Google Scholar 

  61. Koopmann J, Zhang Z, White N, et al. Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry. Clin Cancer Res. 2004;10:860–8.

    PubMed  CAS  Google Scholar 

  62. Adam BL, Qu Y, Davies JW, et al. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 2002;62:3609–14.

    PubMed  CAS  Google Scholar 

  63. Semmes OJ, Feng Z, Adam BL, et al. Evaluation of serum protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for the detection of prostate cancer: I Assessment of platform reproducibility. Clin Chem 2005;51(1):102–12.

    PubMed  CAS  Google Scholar 

  64. Petricoin III EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359:572–77.

    PubMed  CAS  Google Scholar 

  65. Kozak KR, Su F, Whitelegge JP, et al. Characterization of serum biomarkers for detection of early stage ovarian cancer. Proteomics. 2005;5:4589–96.

    PubMed  CAS  Google Scholar 

  66. Rai AJ, Zhang Z, Rosenzweig J, et al. Proteomic approaches to tumor marker discovery. Arch Pathol Lab Med. 2002;126:(12):1518–26

    PubMed  CAS  Google Scholar 

  67. Ye B, Cramer DW, Skates SJ, et al. Haptoglobin-alpha subunit as potential serum biomarker in ovarian cancer: identification and characterization using proteomic profiling and mass spectrometry. Clin Cancer Res. 2003;9(8):2904–11.

    PubMed  CAS  Google Scholar 

  68. Woong-Shick A, Sung-Pil P, Su-Mi B, et al. Identification of hemoglobin-α and-β subunits as potential serum biomarkers for the diagnosis and prognosis of ovarian cancer. Cancer Sci. 2005;96:197–201.

    PubMed  Google Scholar 

  69. Moshkovskii SA, Serebryakova MV, Kuteykin-Teplyakov BK, et al. Ovarian cancer marker of 11.7 kDa detected by proteomics is a serum amyloid A1. Proteomics. 2005;5:3790–7.

    PubMed  CAS  Google Scholar 

  70. Wright, Jr, Cazares, LH, Leung SM, et al. ProteinChip surface enhanced laser desertion/ionization (SELDI) mass spectrometry: a novel protein biochip technology for detection of prostate cancer biomarkers in complex protein mixtures. Prostate Cancer Prostatic Dis. 1999;2:264–76.

    CAS  Google Scholar 

  71. Xiao Z, Jiang X, Beckett ML, et al. Generation of a Baculovirus Recombinant Prostate-Specific Membrane Antigen and Its Use in the Development of a Novel Protein Biochip Quantitative Immunoassay. Protein Expression and Purification. 2000;19:12–21.

    PubMed  CAS  Google Scholar 

  72. Xiao Z, Adam BL, Cazares LH, et al. Quantitation of Serum Prostate-specific Membrane Antigen by a Novel Protein Biochip Immunoassay Discriminates Benign from Malignant Prostate Disease. Cancer Research 2001;61:6029–33.

    PubMed  CAS  Google Scholar 

  73. Maliki G, Ward MD, Gupta SK, et al. Serum Levels of an Isoform of Apolipoprotein A-II as a Potential Marker for Prostate Cancer. Clinical Cancer Research. 2005;11:1073–85.

    Google Scholar 

  74. Le L, Chi K, Tyldesley S, et al. Identification of Serum Amyloid A as a Biomarker to Distinguish Prostate Cancer Patients with Bone Lesions. Clinical Chemistry. 2005;51(4):695–707.

    PubMed  CAS  Google Scholar 

  75. Shiwa M, Nishimura Y, Wakatabe R, et al. Rapid discovery and identification of a tissu especific tumor biomarker from 39 human cancer cell lines using the SELDI ProteinChip platform. Biochem Biophys Res. Commun. 2003;509:18–25.

    Google Scholar 

  76. Chen YD, Zheng S, Yu JK, et al. Artificial neural networks analysis of surface enhancedlaser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population. Clin Cancer Res. 2004;10:8380–5.

    PubMed  CAS  Google Scholar 

  77. Yu JK, Chen YD, Zheng S. An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics. World J Gastroenterol. 2004;10:5127–51.

    Google Scholar 

  78. Engwegen JY, Helgason HH, Cats A, et al. Identification of serum proteins discriminating colorectal cancer patients and healthy controls using surface enhanced laser desorption ionisation-time of flight mass spectrometry (SELDI-TOF MS). World J Gastroenterol 2006;12:1536–44.

    PubMed  CAS  Google Scholar 

  79. Ward DG, Suggett N, Cheng Y, et al. Identification of serum biomarkers for colon cancer by proteomic analysis. Br J Cancer. 2006;94(12):1898–905.

    PubMed  CAS  Google Scholar 

  80. Melle C, Ernst G, Schimmel B, et al. Discovery and identification of α-defensins as low abundant tumor-derived serum markers in colorectal cancer. Gastroenterology, 2005;129:66–75.

    PubMed  CAS  Google Scholar 

  81. Albrethsen J, Bogebo R, Gammeltoft S, et al. Upregulated expression of human neutrophil peptides 1,2 and 3 (HNP 1–3) in colon cancer serum and tumours: a biomarker study. BMC Cancer. 2005;5(8) 1–10.

    Google Scholar 

  82. Melle C, Ernst G, Schimmel B, et al. Different expression of calgizzarin (S100A11) in normal colonic epithelium, adenoma and colorectal carcinoma: Int J Oncol. 2006;28(1):195–200.

    PubMed  CAS  Google Scholar 

  83. Melle C, Bogumil R, Ernst G, et al. Detection and identification of heat shock protein 10 as a biomarker in colorectal cancer by protein profiling. Proteomics. 2006;6(8):2600–8.

    PubMed  CAS  Google Scholar 

  84. Tolson JP, Flad T, Gnau V, et al. Differential detection of S100A 8 in transitional cell carcinoma of the bladder by pair wise tissue proteomic and immunohistochemical analysis. Proteomics. 2006;6(2):697–708.

    PubMed  CAS  Google Scholar 

  85. Li J, Zhang Z, Rosenzweig J, et al. Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin Chem. 2002;48:1296–304.

    PubMed  CAS  Google Scholar 

  86. Pusztai L, Gregory BW, Baggerly KA, et al. Pharmacoproteomic analysis of prechemotherapy and postchemotherapy plasma samples from patients receiving neoadjuvant chemotherapy for breast carcinoma. Cancer. 2004;100:1814–22.

    PubMed  CAS  Google Scholar 

  87. Caputo E, Lombardi ML, Luongo V, et al. Peptide profiling in epithelial tumor plasma by the emerging proteomic techniques. J. Chromatogr. B Analyt Technol Biomed Life Sci. 2005;819:59–66.

    PubMed  CAS  Google Scholar 

  88. Traub F, Feist H, Kreipe HH, Pich A. SEL-DI-MS-based expression profiling of ductal invasive and lobular invasive of ductal invasive and lobular invasive human breast carcinomas. Pathol Res Pract. 2005;201:765–70.

    Google Scholar 

  89. Li J, et al. Identification of biomarkers for breast cancer in nipple aspiration and ductal lavage fluid. Clin Cancer Res. 2005; 11:8312–20.

    PubMed  CAS  Google Scholar 

  90. Sauter ER, Shan S, Hewett JE, et al. Proteomic analysis of nipple aspirate fluid using SELDI-TOF-MS. Int J Cancer. 2005; 114:791–6.

    PubMed  CAS  Google Scholar 

  91. Li J, Orlandi R, White CN, et al. Independent validation of candidate breast cancer serum biomarkers identified by mass spectrometry. Clin Chem. 2005;51:2229–35.

    PubMed  CAS  Google Scholar 

  92. Heike Y, Hosokawa M, Osumi S, et al. Identification of serum proteins related to adverse effects induced by docetaxel infusion from protein expression profiles of serum using SELDI ProteinChip system. Anticancer Res. 2005:25:1197–203.

    PubMed  CAS  Google Scholar 

  93. Xiao X, Liu, D, Tang, Y., et al. Development of proteomic patterns for detecting lung cancer. Dis Markers. 2005;19:33–9.

    Google Scholar 

  94. Paradis V, Degos F, Dargere D, et al. Identification of a new marker of hepatocellular carcinoma by serum protein profiling of patients with chronic liver diseases. Hepatology. 2005;41:40–7.

    PubMed  CAS  Google Scholar 

  95. Tolson J, Bogumil R, Brunst E, et al. Serum protein profiling by SELDI mass spectrometry: detection of multiple variants of serum amyloid a in renal cancer patients. Lab Invest. 2004;84:845–56.

    PubMed  CAS  Google Scholar 

  96. Rosty C, Ueki T, Argani P, et al. Identification of hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein I as a biomarker for pancreatic ductal adeno-carcinoma by protein biochip technology. Cancer Res. 2002:62:8–1875.

    Google Scholar 

  97. Wadsworth JT, Somers KD, Cazares LH, et al. Identification of patients with head and neck cancer using serum protein profiles. Arch. Otolaryngol. Head Neck Surg. 2004:150:98–104.

    Google Scholar 

  98. Melle C, Ernst G, Schimmel B, et al. Biomarker discovery and identification in laser microdissected head and neck squamous cell carcinoma with ProteinChipw technology, two-dimensional gel electrophoresis, tandem mass spectrometry, and immunohistochemistry. Mol. Cell. Proteomics. 2003;2:443–52.

    PubMed  CAS  Google Scholar 

  99. Roesch-Ely M, Nees M, Karsai S, et al. Proteomic analysis reveals successive aberrations in protein expression from healthy mucosa to invasive head and neck cancer. Oncogene. 2006; Jul 3.

  100. Cho WC, Yip TT, Yip C, et al. Identification of serum amyloid A protein as a potentially useful biomarker to monitor relapse of nasopharyngeal cancer by serum proteomic profiling. Clin Cancer Res. 2004;10:43–52.

    PubMed  CAS  Google Scholar 

  101. Yang EC, Guo J, Diehl G, et al. Protein expression profiling of endometrial malignancies reveals a new tumormarker: chaperonin 10. J Proteome Res. 2004;3:636–43.

    PubMed  CAS  Google Scholar 

  102. Guo J, Yang EC, Desouza L, et al. A strategy for high-resolution protein identification in surface-enhanced laser desorption/ionization mass spectrometry: calgranulin A and chaperonin 10 as protein markers for endometrial carcinoma. Proteomics. 2005;5:1953–66.

    PubMed  CAS  Google Scholar 

  103. Yoshizaki T, Enomoto T, Nakashima R et al. A hered protein expression in endometrial carcinogenesis. Cancer Lett. 2005; 226(2):101–6.

    PubMed  CAS  Google Scholar 

  104. Vlahou, A, Schellhammer, PF, Mendrinos, S. et al. Development of a novel proteomic approach for the detection of transitional cell carcinoma of the bladder in urine. Am J Pathol. 2001:158:1491–502.

    PubMed  CAS  Google Scholar 

  105. Langbein S, Lehmann J, Harder A, et al. Protein profiling of bladder cancer using the 2D-PAGE and SELDI-TOF-MS technique. Technol Cancer Res Treat. 2006;5(1):67–72.

    PubMed  CAS  Google Scholar 

  106. Krieg RC, Gaisa NT, Paweletz CP, et al. Proteomic analysis of human bladder tissue using SELDI approach following microdissection techniques. Methods Mol Biol. 2005;293:255–67.

    PubMed  CAS  Google Scholar 

  107. Lin Z, Jenson SD, Lim MS, et al. Application of SELDI-TOF mass spectrometry for the identification of differentially expressed proteins in transformed follicular lymphoma. Mod Pathol. 2004;17:670–8.

    PubMed  CAS  Google Scholar 

  108. Diamond DL, Zhang Y, Gaiger A et al. Carter D. Use of Protein Chipe array surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDITOF-MS) to identify thymosin b-4, a differentially secreted protein from lymphoblastoid cell lines. J Am Soc Mass Spectrom. 2003;14:760–5.

    PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

Supported by an unrestricted educational grant from AstraZeneca.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ciordia, S., Ríos, V.d.l. & Albar, JP. Contributions of advanced proteomics technologies to cancer diagnosis. Clin Transl Oncol 8, 566–580 (2006). https://doi.org/10.1007/s12094-006-0062-4

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12094-006-0062-4

Keyword

Navigation