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SCIENTIA SINICA Informationis, Volume 49 , Issue 9 : 1175-1185(2019) https://doi.org/10.1360/N112018-00324

Prediction of disease–drug relationships based on tissue specificity and direct neighbor similarity

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  • ReceivedDec 11, 2018
  • AcceptedMar 4, 2019
  • PublishedSep 6, 2019

Abstract

The pathogenesis of complex diseases is a major problem in the field of human health. The development of new drugs through traditional methods requires considerable time and money, which has not met people's actual requirements. Recently, identifying new therapeutic effects of known drugs via drug repositioning has become an effective way to treat numerous diseases. At present, tissue-specific research has achieved some success; however, traditional drug repositioning methods rarely consider the tissue specificity of the disease. To explore the influence of tissue specificity on drug repositioning studies, this study explores the development of tissue specificity and its characteristics and proposes using direct neighbor similarity in drug repositioning based on tissue-specific data. A total of 11405 known drug–target relationships were extracted from the database DrugBank, and five cancers and their disease-causing gene data were obtained from the human Mendelian genetic database. Through the direct neighbor method and using the tissue-specific interaction network as the background network, five tissue-specific drug–disease bipartite networks were constructed, which provided potential drug–disease associations. The results were verified by the CTD (comparative toxicogenomics database) standard. The experimental results show that the accuracy of drug repositioning studies based on tissue specificity and direct neighbor measurement will provide a reliable candidate set for in vivo and in vitro experiments of new drugs, which also provides new ideas for studying drug repositioning.


Funded by

国家重点研发计划(2018YFC0910403)

国家自然科学基金面上项目(61672406)

中央高校基本科研业务费(JB180307)


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  • Figure 1

    (Color online) Example of calculating drug-disease similarity based on direct neighbors

  • Table 1   Drug data format
    DrugBank ID Drug name Entrez gene ID
    DB00001 Lepirudin 2147
    DB00007 Leuprolide 2798
  • Table 2   Disease data format
    OMIM ID Cancer name No. disease-causing genes
    114480 Breast cancer 31
    114500 Colon cancer 33
    114550 Liver cancer 18
    211980 Lung cancer 24
    167000 Ovarian cancer 10
  • Table 3   Tissue-specific protein interaction network data
    Cancer name Tissue name No. nodes No. edges
    Breast cancer Breast 3318 30000
    Colon cancer Colon 3909 30000
    Liver cancer Liver 3360 30000
    Lung cancer Lung 3648 30000
    Ovarian cancer Ovary 3811 30000
  • Table 4   Five drug-disease network information based on direct neighbor metrics
    Cancer name Tissue name No. drug nodes No. edges
    Breast cancer Breast 281 596
    Colon cancer Colon 298 749
    Liver cancer Liver 283 557
    Lung cancer Lung 281 596
    Ovarian cancer Ovary 305 625
  • Table 5   Five drug-disease network information based on direct neighbor metrics
    Tissue name Breast Colon Liver Lung Ovary
    DrugBank ID DB00201** DB00201** DB00997** DB00201** DB00128
    DB00563** DB00563** DB04967* DB04967* DB00563**
    DB00997** DB00642** DB08818* DB00997** DB00642**
    DB00675** DB00945** DB00201** DB00675** DB01183*
    DB08818* DB00997** DB00642** DB01108 DB08818*
    DB00242* DB00432* DB00563** DB08818* DB01708*
    DB00642** DB04967* DB01183* DB00242* DB09074**
    DB04967* DB00440 DB00218 DB00642** DB00675**
    DB00128 DB06813 DB00276** DB00563** DB00171
    DB01183* DB00218 DB00380 DB00128 DB00440
  • Table 6   Comparison of drug candidates in five tissues versus CTD database for five cancers
    Tissue name Breast cancer Colon cancer Liver cancer Lung cancer Ovarian cancer
    Breast 9 5 6 8 8
    Colon 7 8 4 8 5
    Liver 8 5 6 9 7
    Lung 8 6 7 8 6
    Ovary 7 8 4 9 6

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