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ISSN 在线: 2162-6502


DOI: 10.1615/CritRevEukaryotGeneExpr.2019026317
pages 581-597

Gene Prioritization and Network Topology Analysis of Targeted Genes for Acquired Taxane Resistance by Meta-Analysis

Dongha Kim
Department of Biochemistry, School of Medicine, Konkuk University, Seoul, Korea
Young Seok Lee
Department of Biochemistry, School of Medicine, Konkuk University, Seoul, Korea
Jin Ki Kim
Department of Biochemistry, School of Medicine, Konkuk University, Seoul, Korea
Sung Young Kim
Department of Biochemistry, School of Medicine, Konkuk University, Seoul, Korea


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.


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