  Drug repositioning, that is finding new uses for existing drugs to treat more
patients. 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 approved drugs, we evaluate the relationships between miRNAs
and drugs in the tissue-specific PPI 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 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 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.
