Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival.

ABSTRACT: The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. To investigate global interactions in MM, we combined a known protein interaction network with a large clinically annotated MM dataset. We hypothesized that an unbiased network analysis method based on large-scale similarities in gene expression, copy number aberration, and protein interactions may provide novel biological insights. Applying a novel measure of network robustness, Ollivier-Ricci Curvature, we examined patterns in the RNA-Seq gene expression and CNA data and how they relate to clinical outcomes. Hierarchical clustering using ORC differentiated high-risk subtypes with low progression free survival. Differential gene expression analysis de

SUBMITTER: Simhal AK 

PROVIDER: S-EPMC10687027 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature