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Analysis of cis-Regulatory Elements in Gene Co-expression Networks in Cancer

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Biological Networks and Pathway Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1613))

Abstract

Analysis of gene co-expression networks is a powerful “data-driven” tool, invaluable for understanding cancer biology and mechanisms of tumor development. Yet, despite of completion of thousands of studies on cancer gene expression, there were few attempts to normalize and integrate co-expression data from scattered sources in a concise “meta-analysis” framework. Here we describe an integrated approach to cancer expression meta-analysis, which combines generation of “data-driven” co-expression networks with detailed statistical detection of promoter sequence motifs within the co-expression clusters. First, we applied Weighted Gene Co-Expression Network Analysis (WGCNA) workflow and Pearson’s correlation to generate a comprehensive set of over 3000 co-expression clusters in 82 normalized microarray datasets from nine cancers of different origin. Next, we designed a genome-wide statistical approach to the detection of specific DNA sequence motifs based on similarities between the promoters of similarly expressed genes. The approach, realized as cisExpress software module, was specifically designed for analysis of very large data sets such as those generated by publicly accessible whole genome and transcriptome projects. cisExpress uses a task farming algorithm to exploit all available computational cores within a shared memory node.

We discovered that although co-expression modules are populated with different sets of genes, they share distinct stable patterns of co-regulation based on promoter sequence analysis. The number of motifs per co-expression cluster varies widely in accordance with cancer tissue of origin, with the largest number in colon (68 motifs) and the lowest in ovary (18 motifs). The top scored motifs are typically shared between several tissues; they define sets of target genes responsible for certain functionality of cancerogenesis. Both the co-expression modules and a database of precalculated motifs are publically available and accessible for further studies.

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Author information

Authors and Affiliations

  1. Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA

    Martin Triska & Tatiana V. Tatarinova

  2. Thomson Reuters, Boston, MA, USA

    Alexander Ivliev

  3. Prosapia Genetics, Solana Beach, CA, USA

    Yuri Nikolsky

  4. School of Systems Biology, George Mason University, Fairfax, VA, USA

    Yuri Nikolsky

  5. Center for Personalized Medicine, Children’s Hospital Los Angeles, 4640 Hollywood Blvd, Los Angeles, CA, 90027, USA

    Tatiana V. Tatarinova

  6. A.A. Kharkevich Institute for Information Transmission Problems RAS, Moscow, Russia

    Tatiana V. Tatarinova

Authors
  1. Martin Triska
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  2. Alexander Ivliev
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  3. Yuri Nikolsky
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  4. Tatiana V. Tatarinova
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Corresponding author

Correspondence to Tatiana V. Tatarinova .

Editor information

Editors and Affiliations

  1. Keck School of Medicine, University of Southern California, Los Angeles, California, USA

    Tatiana V. Tatarinova

  2. Prosapia Genetics, Solana Beach, California, USA

    Yuri Nikolsky

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Triska, M., Ivliev, A., Nikolsky, Y., Tatarinova, T.V. (2017). Analysis of cis-Regulatory Elements in Gene Co-expression Networks in Cancer. In: Tatarinova, T., Nikolsky, Y. (eds) Biological Networks and Pathway Analysis. Methods in Molecular Biology, vol 1613. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7027-8_11

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  • DOI: https://doi.org/10.1007/978-1-4939-7027-8_11

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7025-4

  • Online ISBN: 978-1-4939-7027-8

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