Studying the drug treatment pattern based on the action of drug and multi-layer network model

Abstract

Objectives:A drug can treat multiple diseases, indicating that the treatment of the drug has certain patterns. In this paper, we studied the treatment pattern of drugs from a new perspective based on the action of drug and multi-layer network model (STAM).Diseases affect the gene expression in related tissues and each disease corresponds to a tissue-specific protein-protein interaction (TSPPI) network. Therefore, a drug is associated with a multi-layer TSPPI network associated with diseases it treats. Single tissue-specific PPI network cannot consider all disease-related information, leading to find the potential treatment pattern of drugs difficultly. Research on multi-layer networks can effectively solve this disadvantage. Furthermore, proteins usually interact with other proteins in PPI to achieve specific functions, such as causing disease. Hence, studying the drug treatment patterns is equivalent to study common module structures in the multi-layer TSPPI network corresponding to drug-related diseases. Knowing the treatment patterns of the drug can help to understand the action mechanisms of the drug and to identify new indications of the drug. Methods:In this paper, we were based on the action of drug and multi-layer network model to study the treatment patterns of drugs. We named our method as STAM. As a case of our proposed method STAM, we focused on a study to trichostatin A (TSA) and three diseases it treats: leukemia, breast cancer, and prostate cancer. Based on the therapeutic effects of TSA on various diseases, we constructed a tissue-specific protein-protein interaction (TSPPI) network and applied a multi-layer network module mining algorithm to obtain candidate drug-target modules. Then, using the genes affected by TSA and related to the three diseases, we employed Gene Ontology (GO), the modules' significance, co-expression network and literatures to filter and analyze the identified drug-target modules. Finally, two modules (named as M17 and M18) were preserved as the potential treatment patterns of TSA. Results:The processed results based on the above framework STAM demonstrated that M17 and M18 had strong potential to be the treatment patterns of TSA. Through the analysis of the significance, composition and functions of the selected drug-target modules, we validated the feasibility and rationality of our proposed method STAM for identifying the drug treatment pattern.Conclusion: This paper studied the drug treatment pattern from a new perspective.The new method STAM used a multi-layer network model, which overcame the shortcomings of the single-layer network, and combined the action of drug. Research on drug treatment model provides new research ideas for disease treatment.