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Critical Reviews™ in Eukaryotic Gene Expression

Publicou 6 edições por ano

ISSN Imprimir: 1045-4403

ISSN On-line: 2162-6502

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.6 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 2.2 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.3 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00058 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.33 SJR: 0.345 SNIP: 0.46 CiteScore™:: 2.5 H-Index: 67

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

Volume 29, Edição 6, 2019, pp. 581-597
DOI: 10.1615/CritRevEukaryotGeneExpr.2019026317
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RESUMO

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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