Exploring Drug Treatment Patterns Based on the Action of Drug and Multilayer Network Model
| Autor: | Shuhang Wang, Yayong Shi, Liang Yu, Liping Zheng, Lin Gao, Quan Zou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Drug
Male Computer science media_common.quotation_subject Breast Neoplasms Drug action Computational biology tissue specificity Hydroxamic Acids Models Biological Catalysis Article drug-target module Inorganic Chemistry lcsh:Chemistry Drug treatment Humans Gene Regulatory Networks drug action Protein Interaction Maps Physical and Theoretical Chemistry Molecular Biology lcsh:QH301-705.5 Spectroscopy media_common Network model multilayer network Protein Synthesis Inhibitors Leukemia Gene ontology fungi Organic Chemistry Drug Repositioning food and beverages Prostatic Neoplasms General Medicine Computer Science Applications Tissue specificity Drug repositioning Drug activity Gene Ontology lcsh:Biology (General) lcsh:QD1-999 drug treatment pattern Female Transcriptome |
| Zdroj: | International Journal of Molecular Sciences International Journal of Molecular Sciences, Vol 21, Iss 5014, p 5014 (2020) Volume 21 Issue 14 |
| ISSN: | 1422-0067 |
| Popis: | Some drugs can be used to treat multiple diseases, suggesting potential patterns in drug treatment. Determination of drug treatment patterns can improve our understanding of the mechanisms of drug action, enabling drug repurposing. A drug can be associated with a multilayer tissue-specific protein&ndash protein interaction (TSPPI) network for the diseases it is used to treat. Proteins usually interact with other proteins to achieve functions that cause diseases. Hence, studying drug treatment patterns is similar to studying common module structures in multilayer TSPPI networks. Therefore, we propose a network-based model to study the treatment patterns of drugs. The method was designated SDTP (studying drug treatment pattern) and was based on drug effects and a multilayer network model. To demonstrate the application of the SDTP method, we focused on analysis of trichostatin A (TSA) in leukemia, breast cancer, and prostate cancer. We constructed a TSPPI multilayer network and obtained candidate drug-target modules from the network. Gene ontology analysis provided insights into the significance of the drug-target modules and co-expression networks. Finally, two modules were obtained as potential treatment patterns for TSA. Through analysis of the significance, composition, and functions of the selected drug-target modules, we validated the feasibility and rationality of our proposed SDTP method for identifying drug treatment patterns. In summary, our novel approach used a multilayer network model to overcome the shortcomings of single-layer networks and combined the network with information on drug activity. Based on the discovered drug treatment patterns, we can predict the potential diseases that the drug can treat. That is, if a disease-related protein module has a similar structure, then the drug is likely to be a potential drug for the treatment of the disease. |
| Databáze: | OpenAIRE |
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