Abstract
We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics data from 521 prostate tumor samples indicated a stronger regulatory impact of structural variants, as they affect more transcription factor hubs in the tissue-specific network. Moreover, crosstalk between transcription factor hub expression modulated by structural variants and methylation levels likely leads to the differential expression of target genes. We report known prostate tumor regulatory drivers and nominate novel transcription factors (ERF, CREB3L1, and POU2F2), which are supported by functional validation.
Highlights
Cancer is a disease of the genome, characterized by uncontrolled growth and survival of damaged cells [1]
We propose that the dysregulated expression of the remaining three transcription factors (TFs) hubs identified in our study (ERF, CREB3L1, and POU2F2) can lead to large-scale changes in the prostate regulatory network, which in turn can play an important role in the transformation of normal cells to a tumorigenic state
ERG and TP53 are known Prostate cancer (PCa) genes and we propose that POU2F2, CREB3L1, and ERF can play an important role in prostate tumorigenesis
Summary
Cancer is a disease of the genome, characterized by uncontrolled growth and survival of damaged cells [1]. Prostate cancer (PCa) is the second most common cancer in men worldwide [2]. Whole-exome sequencing and wholegenome sequencing (WGS) of tumors has revealed recurrent genomic alterations in PCa [3,4,5,6,7,8]. Genomic alterations range from single nucleotide variants (SNVs) to large structural variants (SVs) [5, 9, 10]. SVs include deletions, insertions, duplications, inversions, translocations, and other complex rearrangements. The most common genomic alteration identified in prostate tumors is Dhingra et al Genome Biology (2017) 18:141
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