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 defined 118 genes with significantly aberrant expression. These genes, while not previously associated with MM, were associated with DNA repair, apoptosis, and the immune system. Univariate analysis identified 8/118 to be prognostic genes; all associated with the immune system. A network topology analysis identified both hub and bridge genes which connect known genes of biological significance of MM. Taken together, gene interaction network analysis in MM uses a novel method of global assessment to demonstrate complex immune dysregulation associated with shorter survival.
浆细胞癌多发性骨髓瘤在基因组特征、治疗反应及长期预后方面存在显著差异。为探究多发性骨髓瘤中的全局相互作用，我们将已知蛋白质相互作用网络与大规模临床注释数据集相结合。我们提出假设：基于基因表达、拷贝数变异和蛋白质相互作用的大规模相似性进行无偏倚网络分析方法，可能提供新的生物学见解。运用新型网络稳健性度量方法——奥利维-里奇曲率，我们分析了RNA测序基因表达与拷贝数变异数据模式及其与临床结局的关联。通过奥利维-里奇曲率进行的层次聚类分析，成功区分出具有较低无进展生存期的高危亚型。差异基因表达分析确定了118个表达显著异常的基因，这些基因虽既往未与多发性骨髓瘤建立关联，却与DNA修复、细胞凋亡及免疫系统功能密切相关。单变量分析发现其中8个基因（占118个的6.8%）具有预后价值，且均与免疫系统相关。网络拓扑分析识别出枢纽基因和桥梁基因，这些基因连接了已知具有多发性骨髓瘤生物学意义的关键基因。综上，多发性骨髓瘤基因相互作用网络分析通过创新的全局评估方法，揭示了与较短生存期相关的复杂免疫失调机制。
肿瘤(癌症)患者之家