Predicting Potential Drugs for Breast Cancer based on miRNA and Tissue Specificity

Abstract

Network-based computational method, with the emphasis on biomolecular interactions and biological data integration, has succeeded in drug development and created new directions, such as drug repositioning and drug combination. Drug repositioning, that is finding new uses for existing drugs to treat more patients, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Moreover, cumulative studies demonstrate that the mature miRNAs as well as their precursors can be targeted by small molecular drugs. At the same time, human diseases result from the disordered interplay of tissue- and cell lineage-specific processes. However, few computational researches predict drug-disease potential relationships based on miRNA data and tissue specificity. Therefore, based on miRNA data and the tissue specificity of diseases, we propose a new method named as miTS to predict the potential treatments for diseases. Firstly, based on miRNAs data, target genes and information of FDA (Food and Drug Administration) approved drugs, we evaluate the relationships between miRNAs and drugs in the tissue-specific PPI (protein-protein) network. Then, we construct a tripartite network: drug-miRNA-disease Finally, we obtain the potential drug-disease associations based on the tripartite network. In this paper, we take breast cancer as case study and focus on the top-30 predicted drugs. 25 of them (83.3%) are found having known connections with breast cancer in CTD (Comparative Toxicogenomics Database) benchmark and the other 5 drugs are potential drugs for breast cancer. We further evaluate the 5 newly predicted drugs from clinical records, literature mining, KEGG pathways enrichment analysis and overlapping genes between enriched pathways. For each of the 5 new drugs, strongly supported evidences can be found in three or more aspects. In particular, Regorafenib (DB08896) has 15 overlapping KEGG pathways with breast cancer and their p-values are all very small. In addition, whether in the literature curation or clinical validation, Regorafenib has a strong correlation with breast cancer. All the facts show that Regorafenib is likely to be a truly effective drug, worthy of our further study. It further follows that our method miTS is effective and practical for predicting new drug indications, which will provide potential values for treatments of complex diseases.

Keywords: drug repositioning; miRNAs; module distance; tissue specificity.

Figure 1

The framework of our method miTS. (A) Data preparation: miRNA expression data of breast cancer got from TCGA, miRNA-target gene data got from miRecords, miRTarbase and TarBase, and drug-target gene data got from Drugbank and KEGG. (B) Data preprocessing: we use Z-score to obtain the differentially expressed miRNAs for diseases and preprocess the target information of drugs. (C) In the tissue-specific PPI network, the targets of drug and miRNA are mapped to the PPI network. Orange nodes represent the target genes of miRNAs. Purple nodes represent the target genes of drugs. Green nodes represent the background genes. (D) Based on the module distance algorithm, we construct a drug-miRNA-disease tripartite network, and then based on the tripartite network, we get potential drugs for diseases. d_A,Brepresents the association score between a drug and a disease.

Figure 2

An example for calculating the distance between target set of miRNA A and target set of drug B. Orange and purple nodes represent genes related to miRNA A and drug B, respectively. Node c is a shared node, so it is marked by two colors.

Figure 3

The precision of our predictions at different top-x% drug-breast cancer pairs.

Figure 4

The common genes between enriched pathway sets of drugs and breast cancer. The purple hexagon nodes represent the enriched pathways of a drug. The light green circular nodes represent breast cancer enriched pathways. The width of edges represents the number of common genes between two pathway sets. The wider the edge, the more the number of common genes. A. The common genes between enriched pathway sets of Eribulin mesylate and breast cancer. B. The common genes between enriched pathway sets of Tenecteplase and breast cancer. C. The common genes between enriched pathway sets of Pralatrexate and breast cancer.