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Inference of Phenotype-Relevant Transcriptional Regulatory Networks Elucidates Cancer Type-Specific Regulatory Mechanisms in a Pan-Cancer Study

47 Pages Posted: 16 Aug 2018 Last revised: 17 Aug 2018 Sneak Peek Status: Under Review

See all articles by Amin Emad

Amin Emad

McGill University - Department of Electrical and Computer Engineering

Saurabh Sinha

University of Illinois at Urbana-Champaign - Department of Computer Science

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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.

Suggested Citation

Emad, Amin and Sinha, Saurabh, Inference of Phenotype-Relevant Transcriptional Regulatory Networks Elucidates Cancer Type-Specific Regulatory Mechanisms in a Pan-Cancer Study. Available at SSRN: https://ssrn.com/abstract=3231851 or http://dx.doi.org/10.2139/ssrn.3231851
This is a paper under consideration at Cell Press and has not been peer-reviewed.

Amin Emad (Contact Author)

McGill University - Department of Electrical and Computer Engineering ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

Saurabh Sinha

University of Illinois at Urbana-Champaign - Department of Computer Science

601 E John St
Champaign, IL 61820
United States

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