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Text mining of rheumatoid arthritis and diabetes mellitus to understand the mechanisms of Chinese medicine in different diseases with same treatment

  • Ning Zhao
  • Guang Zheng
  • Jian Li
  • Hong-yan Zhao
  • Cheng Lu
  • Miao Jiang
  • Chi Zhang
  • Hong-tao Guo
  • Ai-ping Lu
Thinking and Methodology
  • 47 Downloads

Abstract

Objective

To identify the commonalities between rheumatoid arthritis (RA) and diabetes mellitus (DM) to understand the mechanisms of Chinese medicine (CM) in different diseases with the same treatment.

Methods

A text mining approach was adopted to analyze the commonalities between RA and DM according to CM and biological elements. The major commonalities were subsequently verifified in RA and DM rat models, in which herbal formula for the treatment of both RA and DM identifified via text mining was used as the intervention.

Results

Similarities were identifified between RA and DM regarding the CM approach used for diagnosis and treatment, as well as the networks of biological activities affected by each disease, including the involvement of adhesion molecules, oxidative stress, cytokines, T-lymphocytes, apoptosis, and inflfl ammation. The Ramulus Cinnamomi-Radix Paeoniae Alba-Rhizoma Anemarrhenae is an herbal combination used to treat RA and DM. This formula demonstrated similar effects on oxidative stress and inflfl ammation in rats with collagen-induced arthritis, which supports the text mining results regarding the commonalities between RA and DM.

Conclusion

Commonalities between the biological activities involved in RA and DM were identifified through text mining, and both RA and DM might be responsive to the same intervention at a specifific stage.

Keywords

text mining rheumatoid arthritis diabetes mellitus different diseases with same treatment Chinese medicine 

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References

  1. 1.
    Eisenberg DM, Kaptchuk TJ, Laine C, Davidoff F. Complementary and alternative medicine—an annals series. Ann Intern Med 2001;135:208.CrossRefPubMedGoogle Scholar
  2. 2.
    Lu AP, Jia HW, Xiao C, Lu QP. Theory of traditional Chinese medicine and therapeutic method of diseases. World J Gastroenterol 2004;10:1854–1856.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Lu AP, Li S. Principle and application of Chinese medicine syndrome differentiation and disease classification. Chin J Integr Tradit West Med (Chin) 2010;30:84–86.Google Scholar
  4. 4.
    Jiang M, Xiao C, Chen G, Lu C, Zha Q, Yan X, et al. Correlation between cold and hot pattern in traditional Chinese medicine and gene expression profiles in rheumatoid arthritis. Front Med 2011;5:219–228.CrossRefPubMedGoogle Scholar
  5. 5.
    Lu C, Zha Q, Chang A, He Y, Lu A. Pattern differentiation in traditional Chinese medicine can help define specific indications for biomedical therapy in the treatment of rheumatoid arthritis. J Altern Complement Med 2009;15:1021–1025.CrossRefPubMedGoogle Scholar
  6. 6.
    Zheng G, Jiang M, He X, Zhao J, Guo H, Chen G, et al. Discrete derivative: a data slicing algorithm for exploration of sharing biological networks between rheumatoid arthritis and coronary heart disease. BioData Min 2011;4:18.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Harmston N, Filsell W, Stumpf MP. What the papers say: text mining for genomics and systems biology. Human Genomics 2010;5:17–29.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Wasko MC, Kay J, Hsia EC, Rahman MU. Diabetes mellitus and insulin resistance in patients with rheumatoid arthritis: risk reduction in a chronic inflammatory disease. Arthritis Care Res 2011;63:512–521.CrossRefGoogle Scholar
  9. 9.
    Liu YS. New usage of Zhibai Dihuang Pill. Gansu J Chin Med (Chin) 2010;23:11–12.Google Scholar
  10. 10.
    Berry M, Castellanos M, eds. Survey of text mining?. London: Springer Verlag;2008:21–22.Google Scholar
  11. 11.
    Agarwal P, Searls DB. Literature mining in support of drug discovery. Brief Bioinf 2008;9:479–492.CrossRefGoogle Scholar
  12. 12.
    Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008;4:682–690.CrossRefPubMedGoogle Scholar
  13. 13.
    Li S, Zhang B, Zhang N. Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Syst Biol 2011;5(Suppl 1):S10.CrossRefGoogle Scholar
  14. 14.
    Xiao C, Li J, Dong X, He X, Niu X, Liu C, et al. Antioxidative and TNF-alpha suppressive activities of puerarin derivative (4AC) in RAW264.7 cells and collagen-induced arthritic rats. Eur J Pharm 2011;666:242–250.CrossRefGoogle Scholar
  15. 15.
    Reed MJ, Meszaros K, Entes LJ, Claypool MD, Pinkett JG, Gadbois TM, et al. A new rat model of type 2 diabetes: the fat-fed, streptozotocin-treated rat. Metabolism 2000;49:1390–1394.CrossRefPubMedGoogle Scholar
  16. 16.
    Lu AP, Chen KJ. Pondering on syndrome differentiation of diseases. Chin J Integr Tradit West Med (Chin) 2005;25:843–845.Google Scholar
  17. 17.
    Gan XJ. Aplication of different diseases with same treatment in blood disease. J Emerg Tradit Chin Med (Chin) 2010;19:795.Google Scholar
  18. 18.
    Wang D, Shang XL. Prevention and control on the geriatrics with Liuwei Dihuang Pill. Chin J Gerontol (Chin) 2011;31:524–527.Google Scholar
  19. 19.
    Graf S, Schumm-Draeger PM. Diabetes and rheumatism: is diabetes mellitus also an inflammatory disease? Z Rheumatol 2011;70:747–751.CrossRefPubMedGoogle Scholar
  20. 20.
    Li S, Zhang B, Jiang D, Wei YY, Zhang NB. Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae. BMC Bioinf 2010;11(Suppl 11):S6.CrossRefGoogle Scholar
  21. 21.
    Zaremba S, Ramos-Santacruz M, Hampton T, Shetty P, Fedorko J, Whitmore J, et al. Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens. BMC Bioinf 2009;10:177.CrossRefGoogle Scholar
  22. 22.
    Cohen AM, Hersh WR. A survey of current work in biomedical text mining. Brief Bioinf 2005;6:57–71.CrossRefGoogle Scholar
  23. 23.
    Angelotti F, Parma A, Cafaro G, Capecchi R, Alunno A, Puxeddu I. One year in review 2017: pathogenesis of rheumatoid arthritis. Clin Exp Rheumatol 2017;35:368–378.PubMedGoogle Scholar
  24. 24.
    Xia C, Rao X, Zhong J. Role of T lymphocytes in type 2 diabetes and diabetes-associated infl ammation. J Diabetes Res 2017;2017:6494795.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Arend WP. Physiology of cytokine pathways in rheumatoid arthritis. Arthrit Rheum 2001;45:101–106.CrossRefGoogle Scholar
  26. 26.
    Hata H, Sakaguchi N, Yoshitomi H, Iwakura Y, Sekikawa K, Azuma Y, et al. Distinct contribution of IL-6, TNF-alpha, IL-1, and IL-10 to T cell-mediated spontaneous autoimmune arthritis in mice. J Clin Invest 2004;114:582–588.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    King GL. The role of infl ammatory cytokines in diabetes and its complications. J Periodontol 2008;79:1527–1534.CrossRefPubMedGoogle Scholar
  28. 28.
    Cardellini M, Perego L, D'Adamo M, Marini MA, Procopio C, Hribal ML, et al. C-174G polymorphism in the promoter of the interleukin-6 gene is associated with insulin resistance. Diabetes Care 2005;28:2007–2012.CrossRefPubMedGoogle Scholar
  29. 29.
    Arroul-Lammali A, Rahal F, Chetouane R, Djeraba Z, Medjeber O, Ladjouze-Rezig A, et al. Ex vivo all-trans retinoic acid modulates NO production and regulates IL-6 effect during rheumatoid arthritis: a study in Algerian patients. Immunopharmacol Immunotoxicol 2017;39:87–96.CrossRefPubMedGoogle Scholar
  30. 30.
    Kumar S, Singh RK, Bhardwaj TR. Therapeutic role of nitric oxide as emerging molecule. Biomed Pharmacother 2017;85:182–201.CrossRefPubMedGoogle Scholar
  31. 31.
    Baier A, Meineckel I, Gay S, Pap T. Apoptosis in rheumatoid arthritis. Curr Opin Rheumatol 2003;15:274–279.CrossRefPubMedGoogle Scholar
  32. 32.
    Pap T, Muller-Ladner U, Gay RE, Gay S. Fibroblast biology. Role of synovial fibroblasts in the pathogenesis of rheumatoid arthritis. Arthritis Res 2000;2:361–367.PubMedGoogle Scholar
  33. 33.
    Davidson SM, Duchen MR. Endothelial mitochondria: contributing to vascular function and disease. Circ Res 2007;100:1128–1141.CrossRefPubMedGoogle Scholar
  34. 34.
    Visser H. Early diagnosis of rheumatoid arthritis. Best Pract Res Clin Rheumatol 2005;19:55–72.CrossRefPubMedGoogle Scholar
  35. 35.
    Lee YH, Bae SC. Intercellular adhesion molecule-1 polymorphisms, K469E and G261R and susceptibility to vasculitis and rheumatoid arthritis: a meta-analysis. Cell Mol Biol 2016;62:84–90.PubMedGoogle Scholar
  36. 36.
    McMurray RW. Adhesion molecules in autoimmune disease. Semin Arthrit Rheum 1996;25:215–233.CrossRefGoogle Scholar

Copyright information

© Chinese Association of the Integration of Traditional and Western Medicine 2017

Authors and Affiliations

  • Ning Zhao
    • 1
  • Guang Zheng
    • 2
  • Jian Li
    • 3
  • Hong-yan Zhao
    • 4
  • Cheng Lu
    • 1
  • Miao Jiang
    • 1
  • Chi Zhang
    • 1
  • Hong-tao Guo
    • 1
  • Ai-ping Lu
    • 5
  1. 1.Institute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijingChina
  2. 2.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  3. 3.School of Basic Medical SciencesBeijing University of Chinese MedicineBeijingChina
  4. 4.Institute of Basic Theory for Chinese MedicineChina Academy of Chinese Medical SciencesBeijingChina
  5. 5.School of Chinese MedicineHong Kong Baptist UniversityHong Kong SARChina

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