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Identification of miRNA-transcription factor network motifs to search for drug repurposing candidates in the treatment of colorectal liver metastasis






This project is funded by the German-Russian Funding Competition in the Area of Industry-Oriented Applied Research and Cooperation of Innovative SMEs 2016

Colorectal cancer (CRC) is the third most common cancer in the world with a frequently lethal complication – the development of colorectal liver metastases (CLM). Despite improved methods for detection and treatment of CLM, the chemotherapy methods used today remain inadequate and no molecularly targeted therapy is available. Therefore, this project aims at the combined use of cancer genomics with computational drug repositioning to predict treatment responses of existing drugs based on disease associated gene regulatory networks. The objectives of the project are to combine most innovative knowledge-based bioinformatic techniques built on databases and methods of artificial intelligence and modern experimental methods to analyze chromatin structure and to do massive gene expression analyses. From these data, the intracellular gene regulatory and signaling networks specific for heterogeneous forms of colorectal liver metastasis (CLM) will be reconstructed. They will be used to reveal the most important components of these network and the feedforward loops (FFLs) consisting of miRNA and TFs, to identify most sensitive nodes in these circuits for potential therapeutic intervention, and to predict the best drug combinations and treatment regimens for multi-targeted therapies of CLMs. Exploitation of the results of the project will be in patenting the drug combinations and treatment regimens for multi-targeted therapies of CLMs as well as in offering to the drug industry the developed bioinformatics network-motif-based drug target identification and drug repurposing pipeline for potential application in various cancers and other disease areas.

GeneXplain’s contribution to the project is in

  1. scanning available relevant data to identify miRNAs;
  2. determining promoter and enhancer regions for genes related to CRC / CLM;
  3. developing advanced algorithms to find common TF regulatory sites in miRNA and CRC / CLM genes;
  4. integrating these results with miRNA target predictions using bioinformatic resources;
  5. reconstructing combined TF and miRNA regulatory networks of CRC / CLM genes;
  6. establishing a reference protein-protein interaction database for CRC / CLM genes;
  7. finding TFs in feed-forward loops (FFL), which are also highly represented in the CRC / CLM PPI database;
  8. detecting possible drug attack points by modeling the FFLs;
  9. selecting drug candidates.