IMMUNE NETWORK

  • 제15권4호
  • /
  • Pages.177-185
  • /
  • 2015
  • /
  • 1598-2629(pISSN)
  • /
  • 2092-6685(eISSN)
대한면역학회 (The Korean Association of Immunobiologists)
권호 표지
DOI QR코드

DOI QR Code

Proteomics in Rheumatoid Arthritis Research

  • Park, Yune-Jung (Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Chung, Min Kyung (Division of Rheumatology, Department of Internal Medicine, St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Hwang, Daehee (Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology) ;
  • Kim, Wan-Uk (POSTECH-CATHOLIC Biomedical Engineering Institute)
  • 투고 : 2015.06.15
  • 심사 : 2015.07.31
  • 발행 : 2015.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Although rheumatoid arthritis (RA) is the most common chronic inflammatory autoimmune disease, diagnosis of RA is currently based on clinical manifestations, and there is no simple, practical assessment tool in the clinical field to assess disease activity and severity. Recently, there has been increasing interest in the discovery of new diagnostic RA biomarkers that can assist in evaluating disease activity, severity, and treatment response. Proteomics, the large-scale study of the proteome, has emerged as a powerful technique for protein identification and characterization. For the past 10 years, proteomic techniques have been applied to different biological samples (synovial tissue/fluid, blood, and urine) from RA patients and experimental animal models. In this review, we summarize the current state of the application of proteomics in RA and its importance in identifying biomarkers and treatment targets.

키워드

참고문헌

  1. Smolen, J. S., and D. Aletaha. 2004. Patients with rheumatoid arthritis in clinical care. Ann. Rheum. Dis. 63: 221-225. https://doi.org/10.1136/ard.2003.012575
  2. Wolfe, F., K. Michaud, O. Gefeller, and H. K. Choi. 2003. Predicting mortality in patients with rheumatoid arthritis. Arthritis Rheum. 48: 1530-1542. https://doi.org/10.1002/art.11024
  3. Pincus, T., L. F. Callahan, W. G. Sale, A. L. Brooks, L. E. Payne, and W. K. Vaughn. 1984. Severe functional declines, work disability, and increased mortality in seventy-five rheumatoid arthritis patients studied over nine years. Arthritis Rheum. 27: 864-872. https://doi.org/10.1002/art.1780270805
  4. Sokka, T., and P. Hannonen. 2000. Healing of erosions in rheumatoid arthritis. Ann. Rheum. Dis. 59: 647-649. https://doi.org/10.1136/ard.59.8.647
  5. Rau, R., S. Wassenberg, G. Herborn, W. T. Perschel, and G. Freitag. 2001. Identification of radiologic healing phenomena in patients with rheumatoid arthritis. J. Rheumatol. 28: 2608-2615.
  6. van der, H. D., and R. Landewe. 2003. Imaging: do erosions heal? Ann. Rheum. Dis. 62 Suppl 2: ii10-ii12. https://doi.org/10.1136/ard.62.1.10
  7. Ideguchi, H., S. Ohno, H. Hattori, A. Senuma, and Y. Ishigatsubo. 2006. Bone erosions in rheumatoid arthritis can be repaired through reduction in disease activity with conventional disease- modifying antirheumatic drugs. Arthritis Res. Ther. 8: R76. https://doi.org/10.1186/ar1943
  8. Arnett, F. C., S. M. Edworthy, D. A. Bloch, D. J. McShane, J. F. Fries, N. S. Cooper, L. A. Healey, S. R. Kaplan, M. H. Liang, H. S. Luthra, T. A. Medsger Jr, D. M. Mitchell, D. H. Neustadt, R. S. Pinals, J. G. Schaller, J. T. Sharp, R. L. Wilder and G. G. Hunder. 1988. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum. 31: 315-324. https://doi.org/10.1002/art.1780310302
  9. Aletaha, D., T. Neogi, A. J. Silman, J. Funovits, D. T. Felson, C. O. Bingham, III, N. S. Birnbaum, G. R. Burmester, V. P. Bykerk, M. D. Cohen, B. Combe, K. H. Costenbader, M. Dougados, P. Emery, G. Ferraccioli, J. M. Hazes, K. Hobbs, T. W. Huizinga, A. Kavanaugh, J. Kay, T. K. Kvien, T. Laing, P. Mease, H. A. Menard, L. W. Moreland, R. L. Naden, T. Pincus, J. S. Smolen, E. Stanislawska-Biernat, D. Symmons, P. P. Tak, K. S. Upchurch, J. Vencovsky, F. Wolfe, and G. Hawker. 2010. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 62: 2569-2581. https://doi.org/10.1002/art.27584
  10. Aletaha, D., T. Neogi, A. J. Silman, J. Funovits, D. T. Felson, C. O. Bingham, III, N. S. Birnbaum, G. R. Burmester, V. P. Bykerk, M. D. Cohen, B. Combe, K. H. Costenbader, M. Dougados, P. Emery, G. Ferraccioli, J. M. Hazes, K. Hobbs, T. W. Huizinga, A. Kavanaugh, J. Kay, T. K. Kvien, T. Laing, P. Mease, H. A. Menard, L. W. Moreland, R. L. Naden, T. Pincus, J. S. Smolen, E. Stanislawska-Biernat, D. Symmons, P. P. Tak, K. S. Upchurch, J. Vencovsky, F. Wolfe, and G. Hawker. 2010. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann. Rheum. Dis. 69: 1580-1588. https://doi.org/10.1136/ard.2010.138461
  11. Harrison, B. J., D. P. Symmons, E. M. Barrett, and A. J. Silman 1998. The performance of the 1987 ARA classification criteria for rheumatoid arthritis in a population based cohort of patients with early inflammatory polyarthritis. American Rheumatism Association. J. Rheumatol. 25: 2324-2330.
  12. Machold, K. P., T. A. Stamm, G. J. Eberl, V. K. Nell, A. Dunky, M. Uffmann, and J. S. Smolen. 2002. Very recent onset arthritis--clinical, laboratory, and radiological findings during the first year of disease. J. Rheumatol. 29: 2278-2287.
  13. Boers, M., A. C. Verhoeven, H. M. Markusse, M. A. van de Laar, R. Westhovens, J. C. van Denderen, Z. D. van, B. A. Dijkmans, A. J. Peeters, P. Jacobs, H. R. van den Brink, H. J. Schouten, D. M. van der Heijde, A. Boonen, and S. van der Linden. 1997. Randomised comparison of combined step-down prednisolone, methotrexate and sulphasalazine with sulphasalazine alone in early rheumatoid arthritis. Lancet 350: 309-318. https://doi.org/10.1016/S0140-6736(97)01300-7
  14. Goekoop-Ruiterman, Y. P., J. K. de Vries-Bouwstra, C. F. Allaart, Z. D. van, P. J. Kerstens, J. M. Hazes, A. H. Zwinderman, H. K. Ronday, K. H. Han, M. L. Westedt, A. H. Gerards, J. H. van Groenendael, W. F. Lems, M. V. van Krugten, F. C. Breedveld, and B. A. Dijkmans. 2005. Clinical and radiographic outcomes of four different treatment strategies in patients with early rheumatoid arthritis (the BeSt study): a randomized, controlled trial. Arthritis Rheum. 52: 3381-3390. https://doi.org/10.1002/art.21405
  15. Pincus, T., G. Ferraccioli, T. Sokka, A. Larsen, R. Rau, I. Kushner, and F. Wolfe. 2002. Evidence from clinical trials and long-term observational studies that disease-modifying anti-rheumatic drugs slow radiographic progression in rheumatoid arthritis: updating a 1983 review. Rheumatology (Oxford) 41: 1346-1356. https://doi.org/10.1093/rheumatology/41.12.1346
  16. Aletaha, D., and J. S. Smolen. 2002. The rheumatoid arthritis patient in the clinic: comparing more than 1,300 consecutive DMARD courses. Rheumatology (Oxford) 41: 1367-1374. https://doi.org/10.1093/rheumatology/41.12.1367
  17. Combe, B., R. Landewe, C. Lukas, H. D. Bolosiu, F. Breedveld, M. Dougados, P. Emery, G. Ferraccioli, J. M. Hazes, L. Klareskog, K. Machold, E. Martin-Mola, H. Nielsen, A. Silman, J. Smolen, and H. Yazici. 2007. EULAR recommendations for the management of early arthritis: report of a task force of the European Standing Committee for International Clinical Studies Including Therapeutics (ESCISIT).Ann. Rheum. Dis. 66: 34-45.
  18. Emery, P., F. C. Breedveld, M. Dougados, J. R. Kalden, M. H. Schiff, and J. S. Smolen. 2002. Early referral recommendation for newly diagnosed rheumatoid arthritis: evidence based development of a clinical guide. Ann. Rheum. Dis. 61: 290-297. https://doi.org/10.1136/ard.61.4.290
  19. Prevoo, M. L., M. A. van 't Hof, H. H. Kuper, M. A. van Leeuwen, L. B. van de Putte, and P. L. van Riel. 1995. Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum. 38: 44-48. https://doi.org/10.1002/art.1780380107
  20. van der Heijde, D. 2000. How to read radiographs according to the Sharp/van der Heijde method. J. Rheumatol. 27: 261-263.
  21. Wilkins, M. R., C. Pasquali, R. D. Appel, K. Ou, O. Golaz, J. C. Sanchez, J. X. Yan, A. A. Gooley, G. Hughes, I. Humphery- Smith, K. L. Williams, and D. F. Hochstrasser. 1996. From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology (N.Y.) 14: 61-65. https://doi.org/10.1038/nbt0196-61
  22. Anderson, N. L., and N. G. Anderson. 1998. Proteome and proteomics: new technologies, new concepts, and new words. Electrophoresis 19: 1853-1861. https://doi.org/10.1002/elps.1150191103
  23. Blackstock, W. P., and M. P. Weir. 1999. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 17: 121-127. https://doi.org/10.1016/S0167-7799(98)01245-1
  24. Mallick, P., and B. Kuster. 2010. Proteomics: a pragmatic perspective. Nat. Biotechnol. 28: 695-709. https://doi.org/10.1038/nbt.1658
  25. Ryu, S. Y. 2014. Bioinformatics tools to identify and quantify proteins using mass spectrometry data. Adv. Protein Chem. Struct. Biol. 94: 1-17. https://doi.org/10.1016/B978-0-12-800168-4.00001-9
  26. Aebersold, R., and M. Mann. 2003. Mass spectrometry-based proteomics. Nature 422: 198-207. https://doi.org/10.1038/nature01511
  27. Kim, S. J., S. Chae, H. Kim, D. G. Mun, S. Back, H. Y. Choi, K. S. Park, D. Hwang, S. H. Choi, and S. W. Lee. 2014. A protein profile of visceral adipose tissues linked to early pathogenesis of type 2 diabetes mellitus. Mol. Cell. Proteomics. 13: 811-822. https://doi.org/10.1074/mcp.M113.035501
  28. Wilhelm, M., J. Schlegl, H. Hahne, G. A. Moghaddas, M. Lieberenz, M. M. Savitski, E. Ziegler, L. Butzmann, S. Gessulat, H. Marx, T. Mathieson, S. Lemeer, K. Schnatbaum, U. Reimer, H. Wenschuh, M. Mollenhauer, J. Slotta-Huspenina, J. H. Boese, M. Bantscheff, A. Gerstmair, F. Faerber, and B. Kuster. 2014. Mass-spectrometry-based draft of the human proteome. Nature 509: 582-587. https://doi.org/10.1038/nature13319
  29. Farrah, T., E. W. Deutsch, G. S. Omenn, D. S. Campbell, Z. Sun, J. A. Bletz, P. Mallick, J. E. Katz, J. Malmstrom, R. Ossola, J. D. Watts, B. Lin, H. Zhang, R. L. Moritz, and R. Aebersold. 2011. A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas. Mol. Cell. Proteomics. 10: M110.006353 https://doi.org/10.1074/mcp.M110.006353
  30. Rigaut, G., A. Shevchenko, B. Rutz, M. Wilm, M. Mann, and B. Seraphin. 1999. A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 17: 1030-1032. https://doi.org/10.1038/13732
  31. Venne, A. S., L. Kollipara, and R. P. Zahedi. 2014. The next level of complexity: crosstalk of posttranslational modifications. Proteomics 14: 513-524. https://doi.org/10.1002/pmic.201300344
  32. Boja, E. S., and H. Rodriguez. 2012. Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins. Proteomics 12: 1093-1110. https://doi.org/10.1002/pmic.201100387
  33. Angel, T. E., U. K. Aryal, S. M. Hengel, E. S. Baker, R. T. Kelly, E. W. Robinson, and R. D. Smith. 2012. Mass spectrometry-based proteomics: existing capabilities and future directions. Chem. Soc. Rev. 41: 3912-3928. https://doi.org/10.1039/c2cs15331a
  34. Johnson, H., and C. E. Eyers. 2010. Analysis of post-translational modifications by LC-MS/MS. Methods Mol. Biol. 658: 93-108. https://doi.org/10.1007/978-1-60761-780-8_5
  35. Konermann, L., J. Pan, and Y. H. Liu. 2011. Hydrogen exchange mass spectrometry for studying protein structure and dynamics. Chem. Soc. Rev. 40: 1224-1234. https://doi.org/10.1039/C0CS00113A
  36. Zhang, Z., H. Pan, and X. Chen. 2009. Mass spectrometry for structural characterization of therapeutic antibodies. Mass Spectrom. Rev. 28: 147-176. https://doi.org/10.1002/mas.20190
  37. Tilleman, K., and D. Deforce. 2008. Proteomics in rheumatology. Expert Rev. Proteomics 5: 755-759. https://doi.org/10.1586/14789450.5.6.755
  38. Dasuri, K., M. Antonovici, K. Chen, K. Wong, K. Standing, W. Ens, H. El-Gabalawy, and J. A. Wilkins. 2004. The synovial proteome: analysis of fibroblast-like synoviocytes. Arthritis Res. Ther. 6: R161-R168. https://doi.org/10.1186/ar1153
  39. Li, X. J., M. Xu, X. Q. Zhao, J. N. Zhao, F. F. Chen, W. Yu, D. Y. Gao, and B. Luo. 2013. Proteomic analysis of synovial fibroblast-like synoviocytes from rheumatoid arthritis. Clin. Exp. Rheumatol. 31: 552-558.
  40. Yoo, S. A., S. You, H. J. Yoon, D. H. Kim, H. S. Kim, K. Lee, J. H. Ahn, D. Hwang, A. S. Lee, K. J. Kim, Y. J. Park, C. S. Cho, and W. U. Kim. 2012. A novel pathogenic role of the ER chaperone GRP78/BiP in rheumatoid arthritis. J. Exp. Med. 209: 871-886. https://doi.org/10.1084/jem.20111783
  41. Van Riel PL, W. M., van de Putte LB. 1998. Evaluation and management of active inflammatory disease. In Rheumatology, 2nd ed. D. P. Klippel JH, ed. Mosby, London. p. 5.14.11-13.
  42. Kang, M. J., Y. J. Park, S. You, S. A. Yoo, S. Choi, D. H. Kim, C. S. Cho, E. C. Yi, D. Hwang, and W. U. Kim. 2014. Urinary proteome profile predictive of disease activity in rheumatoid arthritis. J. Proteome. Res. 13: 5206-5217. https://doi.org/10.1021/pr500467d
  43. Liao, H., J. Wu, E. Kuhn, W. Chin, B. Chang, M. D. Jones, S. O'Neil, K. R. Clauser, J. Karl, F. Hasler, R. Roubenoff, W. Zolg, and B. C. Guild. 2004. Use of mass spectrometry to identify protein biomarkers of disease severity in the synovial fluid and serum of patients with rheumatoid arthritis. Arthritis Rheum. 50: 3792-3803. https://doi.org/10.1002/art.20720
  44. Haraoui, B., J. S. Smolen, D. Aletaha, F. C. Breedveld, G. Burmester, C. Codreanu, J. P. Da Silva, W. M. de, M. Dougados, P. Durez, P. Emery, J. E. Fonseca, A. Gibofsky, J. Gomez-Reino, W. Graninger, V. Hamuryudan, M. J. Jannaut Pena, J. Kalden, T. K. Kvien, I. Laurindo, E. Martin-Mola, C. Montecucco, M. P. Santos, K. Pavelka, G. Poor, M. H. Cardiel, E. Stanislawska-Biernat, T. Takeuchi, and D. van der Heijde. 2011. Treating Rheumatoid Arthritis to Target: multinational recommendations assessment questionnaire. Ann. Rheum. Dis. 70: 1999-2002. https://doi.org/10.1136/ard.2011.154179
  45. Smolen, J. S., R. Landewe, F. C. Breedveld, M. Buch, G. Burmester, M. Dougados, P. Emery, C. Gaujoux-Viala, L. Gossec, J. Nam, S. Ramiro, K. Winthrop, W. M. de, D. Aletaha, N. Betteridge, J. W. Bijlsma, M. Boers, F. Buttgereit, B. Combe, M. Cutolo, N. Damjanov, J. M. Hazes, M. Kouloumas, T. K. Kvien, X. Mariette, K. Pavelka, P. L. van Riel, A. Rubbert-Roth, M. Scholte-Voshaar, D. L. Scott, T. Sokka-Isler, J. B. Wong, and D. van der Heijde. 2014. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2013 update. Ann. Rheum. Dis. 73: 492-509.
  46. Mewar, D., and A. G. Wilson. 2011. Treatment of rheumatoid arthritis with tumour necrosis factor inhibitors. Br. J. Pharmacol. 162: 785-791. https://doi.org/10.1111/j.1476-5381.2010.01099.x
  47. Sekigawa, I., M. Yanagida, K. Iwabuchi, K. Kaneda, H. Kaneko, Y. Takasaki, G. Jung, S. Sone, Y. Tanaka, H. Ogawa, and K. Takamori. 2008. Protein biomarker analysis by mass spectrometry in patients with rheumatoid arthritis receiving anti-tumor necrosis factor-alpha antibody therapy. Clin. Exp. Rheumatol. 26: 261-267.
  48. Sellam, J., S. Marion-Thore, F. Dumont, S. Jacques, H. J. Garchon, S. Rouanet, Y. Taoufik, H. Hendel-Chavez, J. Sibilia, J. Tebib, L. Le, X, B. Combe, M. Dougados, X. Mariette, and G. Chiocchia. 2014. Use of whole-blood transcriptomic profiling to highlight several pathophysiologic pathways associated with response to rituximab in patients with rheumatoid arthritis: data from a randomized, controlled, open-label trial. Arthritis Rheumatol. 66: 2015-2025. https://doi.org/10.1002/art.38671
  49. Zheng, X., S. L. Wu, M. Hincapie, and W. S. Hancock. 2009. Study of the human plasma proteome of rheumatoid arthritis. J. Chromatogr. A 1216: 3538-3545. https://doi.org/10.1016/j.chroma.2009.01.063
  50. Jin, E. H., S. C. Shim, H. G. Kim, S. C. Chae, and H. T. Chung. 2009. Polymorphisms of COTL1 gene identified by proteomic approach and their association with autoimmune disorders. Exp. Mol. Med. 41: 354-361. https://doi.org/10.3858/emm.2009.41.5.040
  51. Kastrinaki, M. C., P. Sidiropoulos, S. Roche, J. Ringe, S. Lehmann, H. Kritikos, V. M. Vlahava, B. Delorme, G. D. Eliopoulos, C. Jorgensen, P. Charbord, T. Haupl, D. T. Boumpas, and H. A. Papadaki. 2008. Functional, molecular and proteomic characterisation of bone marrow mesenchymal stem cells in rheumatoid arthritis. Ann. Rheum. Dis. 67: 741-749. https://doi.org/10.1136/ard.2007.076174
  52. Bo, G. P., L. N. Zhou, W. F. He, G. X. Luo, X. F. Jia, C. J. Gan, G. X. Chen, Y. F. Fang, P. M. Larsen, and J. Wu. 2009. Analyses of differential proteome of human synovial fibroblasts obtained from arthritis. Clin. Rheumatol. 28: 191-199. https://doi.org/10.1007/s10067-008-1013-y
  53. Zhang, H., L. Y. Fan, M. Zong, L. S. Sun, and L. Lu. 2012. Proteins related to the functions of fibroblast-like synoviocytes identified by proteomic analysis. Clin. Exp. Rheumatol. 30: 213-221.
  54. Katano, M., K. Okamoto, M. Arito, Y. Kawakami, M. S. Kurokawa, N. Suematsu, S. Shimada, H. Nakamura, Y. Xiang, K. Masuko, K. Nishioka, K. Yudoh, and T. Kato. 2009. Implication of granulocyte-macrophage colony-stimulating factor induced neutrophil gelatinase-associated lipocalin in pathogenesis of rheumatoid arthritis revealed by proteome analysis. Arthritis Res. Ther. 11: R3. https://doi.org/10.1186/ar2587
  55. Dotzlaw, H., M. Schulz, M. Eggert, and G. Neeck. 2004. A pattern of protein expression in peripheral blood mononuclear cells distinguishes rheumatoid arthritis patients from healthy individuals. Biochim. Biophys. Acta 1696: 121-129. https://doi.org/10.1016/j.bbapap.2003.09.015
  56. Schulz, M., H. Dotzlaw, S. Mikkat, M. Eggert, and G. Neeck. 2007. Proteomic analysis of peripheral blood mononuclear cells: selective protein processing observed in patients with rheumatoid arthritis. J. Proteome Res. 6: 3752-3759. https://doi.org/10.1021/pr070285f
  57. Lu, M. C., N. S. Lai, H. C. Yu, H. B. Huang, S. C. Hsieh, and C. L. Yu. 2010. Anti-citrullinated protein antibodies bind surface-expressed citrullinated Grp78 on monocyte/macrophages and stimulate tumor necrosis factor alpha production. Arthritis Rheum. 62: 1213-1223. https://doi.org/10.1002/art.27386
  58. Tilleman, K., B. K. Van, A. Dhondt, I. Hoffman, K. F. De, E. Veys, D. Elewaut, and D. Deforce. 2005. Chronically inflamed synovium from spondyloarthropathy and rheumatoid arthritis investigated by protein expression profiling followed by tandem mass spectrometry. Proteomics 5: 2247-2257. https://doi.org/10.1002/pmic.200401109
  59. Watanabe, J., C. Charles-Schoeman, Y. Miao, D. Elashoff, Y. Y. Lee, G. Katselis, T. D. Lee, and S. T. Reddy. 2012. Proteomic profiling following immunoaffinity capture of high-density lipoprotein: association of acute-phase proteins and complement factors with proinflammatory high-density lipoprotein in rheumatoid arthritis. Arthritis Rheum. 64: 1828-1837. https://doi.org/10.1002/art.34363
  60. Giusti, L., C. Baldini, F. Ciregia, G. Giannaccini, C. Giacomelli, F. F. De, S. A. Delle, L. Riente, A. Lucacchini, L. Bazzichi, and S. Bombardieri. 2010. Is GRP78/BiP a potential salivary biomarker in patients with rheumatoid arthritis? Proteomics Clin. Appl. 4: 315-324. https://doi.org/10.1002/prca.200900082

피인용 문헌

  1. Identification of novel systemic sclerosis biomarkers employing aptamer proteomic analysis vol.57, pp.10, 2015, https://doi.org/10.1093/rheumatology/kex404
  2. New Insights for RANKL as a Proinflammatory Modulator in Modeled Inflammatory Arthritis vol.10, pp.None, 2015, https://doi.org/10.3389/fimmu.2019.00097
  3. Early supplemental α2‐macroglobulin attenuates cartilage and bone damage by inhibiting inflammation in collagen II‐induced arthritis model vol.22, pp.4, 2019, https://doi.org/10.1111/1756-185x.13457
  4. Comparative label-free proteomic analysis of equine osteochondrotic chondrocytes vol.228, pp.None, 2015, https://doi.org/10.1016/j.jprot.2020.103927
  5. Efficacy, safety and cost-effectiveness of a web-based platform delivering the results of a biomarker-based predictive model of biotherapy response for rheumatoid arthritis patients: a protocol for a vol.21, pp.1, 2015, https://doi.org/10.1186/s13063-020-04683-7
  6. Precision Medicine for Rheumatoid Arthritis: The Right Drug for the Right Patient-Companion Diagnostics vol.11, pp.8, 2015, https://doi.org/10.3390/diagnostics11081362
  7. Quantitative proteomic analysis comparing grades ICRS1 and ICRS3 in patients with osteoarthritis vol.22, pp.6, 2015, https://doi.org/10.3892/etm.2021.10905
  8. Quantitative Proteomic Analysis of Synovial Tissue Reveals That Upregulated OLFM4 Aggravates Inflammation in Rheumatoid Arthritis vol.20, pp.10, 2015, https://doi.org/10.1021/acs.jproteome.1c00399
  9. ITGA2 protein is associated with rheumatoid arthritis in Chinese and affects cellular function of T cells vol.523, pp.None, 2021, https://doi.org/10.1016/j.cca.2021.09.024