Discovery of novel candidate urinary protein biomarkers for prostate cancer in a multiethnic cohort of South African patients via label-free mass spectrometry

Proteomics - Clinical Applications

Volumn 9, Issue 5-6, 2015, Pages 597-609

  Adeola, Henry A ^a,b   Soares, Nelson C ^b   Paccez, Juliano D ^a,b   Kaestner, Lisa ^c   Blackburn, Jonathan M ^b   Zerbini, Luiz F ^a,b  

^a UNIVERSITY OF CAPE TOWN   (South Africa)

^b UNIVERSITY OF CAPE TOWN   (South Africa)

^c UNIVERSITY OF CAPE TOWN   (South Africa)

Author keywords

Biomarker discovery; Prostate cancer; South Africa; Urine

Indexed keywords

ACID PHOSPHATASE PROSTATE ISOENZYME; ALPHA 2 MACROGLOBULIN; APOLIPOPROTEIN A2; APOLIPOPROTEIN C3; ARYLDIALKYLPHOSPHATASE; BETA 2 MICROGLOBULIN; CARBONATE DEHYDRATASE I; CARBONATE DEHYDRATASE II; CD14 ANTIGEN; CD59 ANTIGEN; COMPLEMENT COMPONENT C4B BINDING PROTEIN; COMPLEMENT FACTOR H; CYSTATIN M; ENTACTIN; EPIDERMAL GROWTH FACTOR; FIBRILLIN 1; HAPTOGLOBIN; HEMOGLOBIN ALPHA CHAIN; HEMOGLOBIN BETA CHAIN; HISTONE H1; IMMUNOGLOBULIN; LEUKOCYTE ASSOCIATED IMMUNOGLOBULIN LIKE RECEPTOR 1; POLYUBIQUITIN; POLYUBIQUITIN C; PROSTAGLANDIN D SYNTHASE; PROTEIN S 100; PROTEIN SH3; REDUCED NICOTINAMIDE ADENINE DINUCLEOTIDE PHOSPHATE; SPHINGOLIPID ACTIVATOR PROTEIN; UNCLASSIFIED DRUG; PROTEOME; TUMOR MARKER;

ARTICLE; BLACK PERSON; CAUCASIAN; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE MARKER; EARLY DIAGNOSIS; HUMAN; MASS SPECTROMETER; MASS SPECTROMETRY; POLYMERASE CHAIN REACTION; PRIORITY JOURNAL; PROSTATE CANCER; PROSTATE HYPERTROPHY; PROTEIN EXPRESSION; PROTEIN URINE LEVEL; PROTEOMICS; RELIABILITY; SOUTH AFRICAN; URINALYSIS; VALIDITY; AGED; ETHNOLOGY; MALE; METABOLISM; MIDDLE AGED; PROSTATIC HYPERPLASIA; PROSTATIC NEOPLASMS; SOUTH AFRICA; TANDEM MASS SPECTROMETRY; URINE; VERY ELDERLY;

AFRICAN CONTINENTAL ANCESTRY GROUP; AGED; AGED, 80 AND OVER; BIOMARKERS, TUMOR; EARLY DETECTION OF CANCER; EUROPEAN CONTINENTAL ANCESTRY GROUP; HUMANS; MALE; MIDDLE AGED; PROSTATIC HYPERPLASIA; PROSTATIC NEOPLASMS; PROTEOME; PROTEOMICS; SOUTH AFRICA; TANDEM MASS SPECTROMETRY;

EID: 84930929201     PISSN: 18628346     EISSN: 18628354     Source Type: Journal    
DOI: 10.1002/prca.201400197     Document Type: Article

References (52)

1. Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S. et al., Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 2015, 136, E359-E386.
2. Wu, I., Modlin, C. S., Disparities in prostate cancer in African American men: what primary care physicians can do. Cleve. Clin. J. Med. 2012, 79, 313-320.
3. McIntosh, H., Why do African-American men suffer more prostate cancer? J. Natl. Cancer Inst. 1997, 89, 188-189.
4. Culig, Z., Distinguishing indolent from aggressive prostate cancer. Recent Results Cancer Res. 2014, 202, 141-147.
5. Theodorescu, D., Schiffer, E., Bauer, H. W., Douwes, F. et al., Discovery and validation of urinary biomarkers for prostate cancer. Proteomics Clin. Appl. 2008, 2, 556-570.
6. You, J., Cozzi, P., Walsh, B., Willcox, M. et al., Innovative biomarkers for prostate cancer early diagnosis and progression. Crit. Rev. Oncol./Hematol. 2010, 73, 10-22.
7. Graddis, T. J., McMahan, C. J., Tamman, J., Page, K. J., Trager, J. B., Prostatic acid phosphatase expression in human tissues. Int. J. Clin. Exp. Pathol. 2011, 4, 295-306.
8. Maattanen, L., Hakama, M., Tammela, T. L., Ruutu, M. et al., Specificity of serum prostate-specific antigen determination in the Finnish prostate cancer screening trial. Brit. J. Cancer 2007, 96, 56-60.
9. Downes, M. R., Byrne, J. C., Pennington, S. R., Dunn, M. J. et al., Urinary markers for prostate cancer. BJU Int. 2007, 99, 263-268.
10. Fredolini, C., Liotta, L. A., Petricoin, E. F., Application of proteomic technologies for prostate cancer detection, prognosis, and tailored therapy. Crit. Rev. Clin. Lab. Sci. 2010, 47, 125-138.
11. Pin, E., Fredolini, C., Petricoin, E. F., 3rd, The role of proteomics in prostate cancer research: biomarker discovery and validation. Clin. Biochem. 2013, 46, 524-538.
12. Schiffer, E., Vlahou, A., Petrolekas, A., Stravodimos, K. et al., Prediction of muscle-invasive bladder cancer using urinary proteomics. Clin. Cancer Res. 2009, 15, 4935-4943.
13. Vermassen, T., Van Praet, C., Vanderschaeghe, D., Maenhout, T. et al., Capillary electrophoresis of urinary prostate glycoproteins assists in the diagnosis of prostate cancer. Electrophoresis 2014, 35, 1017-1024.
14. Vermassen, T., Van Praet, C., Lumen, N., Decaestecker, K. et al., Urinary prostate protein glycosylation profiling as a diagnostic biomarker for prostate cancer. Prostate 2015, 75, 314-322.
15. Principe, S., Kim, Y., Fontana, S., Ignatchenko, V. et al., Identification of prostate-enriched proteins by in-depth proteomic analyses of expressed prostatic secretions in urine. J. Proteome Res. 2012, 11, 2386-2396.
16. Brown, M. J., Hypertension and ethnic group. BMJ 2006, 332, 833-836.
17. Williams, J. R., The Declaration of Helsinki and public health. Bull. World Health Organ. 2008, 86, 650-652.
18. Wisniewski, J. R., Zougman, A., Nagaraj, N., Mann, M., Universal sample preparation method for proteome analysis. Nat. Methods 2009, 6, 359-362.
19. Rappsilber, J., Mann, M., Ishihama, Y., Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protocols 2007, 2, 1896-1906.
20. Kentsis, A., Challenges and opportunities for discovery of disease biomarkers using urine proteomics. Pediatr. Int. 2011, 53, 1-6.
21. States, D. J., Omenn, G. S., Blackwell, T. W., Fermin, D. et al., Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat. Biotechnol. 2006, 24, 333-338.
22. Oberg, A. L., Vitek, O., Statistical design of quantitative mass spectrometry-based proteomic experiments. J. Proteome Res. 2009, 8, 2144-2156.
23. Cairns, D. A., Barrett, J. H., Billingham, L. J., Stanley, A. J. et al., Sample size determination in clinical proteomic profiling experiments using mass spectrometry for class comparison. Proteomics 2009, 9, 74-86.
24. Haj-Ahmad, T. A., Abdalla, M. A., Haj-Ahmad, Y., Potential urinary protein biomarker candidates for the accurate detection of prostate cancer among benign prostatic hyperplasia patients. J. Cancer 2014, 5, 103-114.
25. Old, W. M., Meyer-Arendt, K., Aveline-Wolf, L., Pierce, K. G. et al., Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol. Cell. Proteomics 2005, 4, 1487-1502.
26. Schwanhausser, B., Busse, D., Li, N., Dittmar, G. et al., Global quantification of mammalian gene expression control. Nature 2011, 473, 337-342.
27. Cox, J., Hein, M. Y., Luber, C. A., Paron, I. et al., Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 2014, 13, 2513-2526.
28. Karpievitch, Y. V., Dabney, A. R., Smith, R. D., Normalization and missing value imputation for label-free LC-MS analysis. BMC Bioinformatics 2012, 13(Suppl 16), 1-9.
29. Adachi, J., Kumar, C., Zhang, Y., Olsen, J. V., Mann, M., The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biol. 2006, 7, 1-16.
30. Young, B. L., Mlamla, Z., Gqamana, P. P., Smit, S. et al., The identification of tuberculosis biomarkers in human urine samples. Eur. Respir. J. 2014, 43, 1719-1729.
31. Nagaraj, N., Mann, M., Quantitative analysis of the intra- and inter-individual variability of the normal urinary proteome. J. Proteome Res. 2011, 10, 637-645.
32. Molina, L., Salvetat, N., Ben Ameur, R., Peres, S. et al., Analysis of the variability of human normal urine by 2D-GE reveals a "public" and a "private" proteome. J. Proteomics 2011, 75, 70-80.
33. Karpievitch, Y. V., Taverner, T., Adkins, J. N., Callister, S. J. et al., Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition. Bioinformatics 2009, 25, 2573-2580.
34. Johnson, W. E., Li, C., Rabinovic, A., Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007, 8, 118-127.
35. Griffin, N. M., Yu, J. Y., Long, F., Oh, P. et al., Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat. Biotechnol. 2010, 28, 83-89.
36. Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M. et al., Mass-spectrometry-based draft of the human proteome. Nature 2014, 509, 582-587.
37. Krey, J. F., Wilmarth, P. A., Shin, J. B., Klimek, J. et al., Accurate label-free protein quantitation with high- and low-resolution mass spectrometers. J. Proteome Res. 2014, 13, 1034-1044.
38. Levin, Y., The role of statistical power analysis in quantitative proteomics. Proteomics 2011, 11, 2565-2567.
39. Marimuthu, A., O'Meally, R. N., Chaerkady, R., Subbannayya, Y. et al., A comprehensive map of the human urinary proteome. J. Proteome Res. 2011, 10, 2734-2743.
40. Decramer, S., Gonzalez de Peredo, A., Breuil, B., Mischak, H. et al., Urine in clinical proteomics. Mol. Cell. Proteomics 2008, 7, 1850-1862.
41. Chen, Y. T., Tsao, C. Y., Li, J. M., Tsai, C. Y. et al., Large-scale protein identification of human urine proteome by multi-dimensional LC and MS/MS. Proteomics Clin. Appl. 2007, 1, 577-587.
42. Kim, M. S., Pinto, S. M., Getnet, D., Nirujogi, R. S. et al., A draft map of the human proteome. Nature 2014, 509, 575-581.
43. Rickman, D. S., Chen, Y. B., Banerjee, S., Pan, Y. et al., ERG cooperates with androgen receptor in regulating trefoil factor 3 in prostate cancer disease progression. Neoplasia 2010, 12, 1031-1040.
44. Nurmikko, P., Pettersson, K., Piironen, T., Hugosson, J., Lilja, H., Discrimination of prostate cancer from benign disease by plasma measurement of intact, free prostate-specific antigen lacking an internal cleavage site at Lys145-Lys146. Clin. Chem. 2001, 47, 1415-1423.
45. Hanahan, D., Weinberg, R. A., Hallmarks of cancer: the next generation. Cell 2011, 144, 646-674.
46. Mahadevan, N. R., Rodvold, J., Almanza, G., Perez, A. F. et al., ER stress drives Lipocalin 2 upregulation in prostate cancer cells in an NF-kappaB-dependent manner. BMC Cancer 2011, 11, 1-8.
47. Sun, W., Chen, Y., Li, F. X., Zhang, L. et al., Dynamic urinary proteomic analysis reveals stable proteins to be potential biomarkers. Proteome Clin. Appl. 2009, 3, 370-382.
48. Li, Q. R., Fan, K. X., Li, R. X., Dai, J. et al., A comprehensive and non-prefractionation on the protein level approach for the human urinary proteome: touching phosphorylation in urine. Rapid Commun. Mass Spectrom. 2010, 24, 823-832.
49. Liu, X., Shao, C., Wei, L., Duan, J. et al., An individual urinary proteome analysis in normal human beings to define the minimal sample number to represent the normal urinary proteome. Proteome Sci. 2012, 10, 1-12.
50. Knaul, F. M., Atun, R., Farmer, P., Frenk, J., Seizing the opportunity to close the cancer divide. Lancet 2013, 381, 2238-2239.
51. Fuzery, A. K., Levin, J., Chan, M. M., Chan, D. W., Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin. Proteomics 2013, 10, 1-14.
52. Frantzi, M., Bhat, A., Latosinska, A., Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development. Clin. Transl. Med. 2014, 3, 1-22.