Kim Dongha, Lee Young Seok, Kim Jin Ki, Kim Sung Young
Department of Biochemistry, School of Medicine, Konkuk University, Seoul, Korea.
Crit Rev Eukaryot Gene Expr. 2019;29(6):581-597. doi: 10.1615/CritRevEukaryotGeneExpr.2019026317.
Network topology-based approaches prove to be highly efficient in addressing multifactorial phenomena such as acquired drug resistance in cancer. The aim of this study was to identify differentially expressed genes across multiple microarray datasets (meta-DEGs), to prioritize meta-DEGs to find the most promising genes linked to acquired taxane resistance (ATR), and to analyze the relevant biological networks using topology analysis. A total of 771 meta-DEGs were identified by performing a cross-platform meta-analysis of ATR-related microarray datasets. A gene prioritization method was used to simultaneously identify activated or deactivated genes on a co-expression map and protein-protein interaction (PPI) network. The top 10 prioritized genes in the gene co-expression and the top 1% highly ranked genes in the PPI network were identified. The selected meta-DEGs were used to construct biological networks, and topological analysis was performed using network centrality measures. Using integrative analyses, we identified ATR candidate genes, including several previously unidentified genes that were found to be associated with ATR. From the gene co-expression network, PRSS23 was the highest-ranking gene at local average connectivity measure and ADAM9 was ranked highest in other centralities. In protein interaction network, HSPA1A, ANXA1, and PA2G4 showed highest ranks in network centrality analyses. This study provides a comprehensive overview of the gene expression patterns associated with ATR. Furthermore, it presents a new approach to identification of unveiled candidate genes to ATR, using a gene prioritization method and network analysis.
基于网络拓扑的方法在解决多因素现象（如癌症中的获得性耐药）方面被证明是非常有效的。本研究的目的是在多个微阵列数据集（元差异表达基因）中识别差异表达基因，对元差异表达基因进行优先级排序以找到与获得性紫杉烷耐药（ATR）相关的最有前景的基因，并使用拓扑分析来分析相关的生物网络。通过对与ATR相关的微阵列数据集进行跨平台元分析，共鉴定出771个元差异表达基因。使用一种基因优先级排序方法，在共表达图谱和蛋白质-蛋白质相互作用（PPI）网络上同时识别激活或失活的基因。确定了基因共表达中排名前10的基因以及PPI网络中排名前1%的高排名基因。所选的元差异表达基因用于构建生物网络，并使用网络中心性度量进行拓扑分析。通过综合分析，我们鉴定出了ATR候选基因，包括几个以前未被识别但被发现与ATR相关的基因。在基因共表达网络中，PRSS23在局部平均连通性度量中是排名最高的基因，而ADAM9在其他中心性中排名最高。在蛋白质相互作用网络中，HSPA1A、ANXA1和PA2G4在网络中心性分析中显示出最高排名。本研究全面概述了与ATR相关的基因表达模式。此外，它还提出了一种新的方法，即使用基因优先级排序方法和网络分析来识别尚未揭示的ATR候选基因。
