Search for Master Regulators in Walking Cancer Pathways
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In this chapter, we present an approach that allows a causal analysis of multiple “-omics” data with the help of an “upstream analysis” strategy. The goal of this approach is to identify master regulators in gene regulatory networks as potential drug targets for a pathological process. The data analysis strategy includes a state-of-the-art promoter analysis for potential transcription factor (TF)-binding sites using the TRANSFAC® database combined with an analysis of the upstream signal transduction pathways that control the activity of these TFs. When applied to genes that are associated with a switch to a pathological process, the approach identifies potential key molecules (master regulators) that may exert major control over and maintenance of transient stability of the pathological state. We demonstrate this approach on examples of analysis of multi-omics data sets that contain transcriptomics and epigenomics data in cancer. The results of this analysis helped us to better understand the molecular mechanisms of cancer development and cancer drug resistance. Such an approach promises to be very effective for rapid and accurate identification of cancer drug targets with true potential. The upstream analysis approach is implemented as an automatic workflow in the geneXplain platform (www.genexplain.com) using the open-source BioUML framework (www.biouml.org).
Key wordsUpstream analysis Promoter analysis Pathway analysis Microarray data ChIP-seq RNA-seq Pathway rewiring
This work was supported by a grant of the Federal Targeted Program “Research and development on priority directions of science and technology in Russia, 2014–2020,” grant number: 14.604.21.0101 to the Institute of Chemical Biology and Fundamental Medicine, Novosibirsk, Russia. This work was also supported by the following grants of the EU FP7 program: “SysMedIBD,” “RESOLVE,” and “MIMOMICS.” We are also very grateful to my colleague at the former Biobase GmbH, Niko Voss, for ideas on the algorithm on pathway analysis; my colleagues from Biosoft.ru: Dr. Tagir Valeev and Dr. Fedor Kolpakov for development of the BioUML framework; my colleagues at geneXplain: Dr. Holger Michael for the critical reading of the manuscript, Philip Stegmaier for development of the algorithms of TF site analysis, Dr. Jeannette Koschmann for creation workflows, Dr. Olga Kel-Margoulis and Prof. Edgar Wingender for the fruitful discussions of the work described here.
Conflicts of Interest: AK is an employee of geneXplain GmbH, which maintains and distributes the BioUML/geneXplain platform used in this study.
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