RT Journal
A1 Xu, Peng
A1 Zhang, Bin
T1 Multiscale network modeling reveals the gene regulatory landscape driving cancer prognosis in 32 cancer types
JF Genome Research 
JO Genome Research 
YR 2023 
FD October 01 
VO 33 
IS 10 
SP 1806 
OP 1817 
DO 10.1101/gr.278063.123 
UL http://genome.cshlp.org/content/33/10/1806.abstract 
AB Cancer is a complex disease with diverse molecular mechanisms that affect patient prognosis. Network-based approaches are effective in revealing a holistic picture of cancer prognosis and gene interactions. However, a comprehensive landscape of coexpression networks and prognostic gene modules across multiple cancer types remains elusive. In this study, we performed a systematic analysis of coexpression networks in 32 cancer types. Our analysis identified 4749 prognostic modules that play a vital role in regulating cancer progression. Integrative epigenomic analyses revealed that these modules were regulated by interactions between gene expression and methylation. Coregulated genes of network modules were enriched in chromosome cytobands and preferentially localized in open chromatin regions. The preserved network modules formed 330 module clusters that resided in chromosome hot spots. The cancer-type-specific prognostic modules participated in unique essential biological processes in different cancer types. Overall, our study provides rich resources of prevalent gene networks and underlying multiscale regulatory mechanisms driving cancer prognosis, which lay a foundation for biomarker discovery and therapeutic target development.