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Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study

Amin Emad, Saurabh Sinha
doi: https://doi.org/10.1101/389734
Amin Emad
1Department of Electrical and Computer Engineering, McGill University, Canada
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Saurabh Sinha
2Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, USA
3Department of Computer Science, University of Illinois at Urbana-Champaign, USA
4Cancer Center at Illinois, University of Illinois at Urbana-Champaign, USA
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ABSTRACT

Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic properties of the samples and therefore cannot identify regulatory mechanisms that are related to a phenotypic outcome of interest. In this study, we developed a new method called InPheRNo to identify ‘phenotype-relevant’ transcriptional regulatory networks. This method is based on a probabilistic graphical model whose conditional probability distributions model the simultaneous effects of multiple transcription factors (TFs) on their target genes as well as the statistical relationship between target gene expression and phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas revealed that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis revealed that the activity level of TFs with many target genes could distinguish patients with good prognosis from those with poor prognosis.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 10, 2018.
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Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study
Amin Emad, Saurabh Sinha
bioRxiv 389734; doi: https://doi.org/10.1101/389734
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Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study
Amin Emad, Saurabh Sinha
bioRxiv 389734; doi: https://doi.org/10.1101/389734

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