Cross-platform meta-analysis of multiple gene expression profiles identifies novel expression signatures in acquired anthracycline-resistant breast cancer

Lee,Young Seok; Ryu,Seung Won; Bae,Se Jong; Park,Tae Hwan; Kwon,Kang; Noh,Yun Hee; Kim,Sung Young

doi:10.3892/or.2015.3810

Cross-platform meta-analysis of multiple gene expression profiles identifies novel expression signatures in acquired anthracycline-resistant breast cancer

Authors:
- Young Seok Lee
- Seung Won Ryu
- Se Jong Bae
- Tae Hwan Park
- Kang Kwon
- Yun Hee Noh
- Sung Young Kim
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Affiliations: Department of Biochemistry, Konkuk University School of Medicine, Seoul 143‑701, Republic of Korea, Institute for Human Tissue Restoration, Department of Plastic and Reconstructive Surgery, College of Medicine, Yonsei University, Seoul 120-749, Republic of Korea, Pusan National University School of Korean Medicine, Pusan 609-735, Republic of Korea
Published online on: February 17, 2015 https://doi.org/10.3892/or.2015.3810
Pages: 1985-1993

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Abstract

Anthracyclines are among the most effective and commonly used chemotherapeutic agents. However, the development of acquired anthracycline resistance is a major limitation to their clinical application. The aim of the present study was to identify differentially expressed genes (DEGs) and biological processes associated with the acquisition of anthracycline resistance in human breast cancer cells. We performed a meta-analysis of publically available microarray datasets containing data on stepwise-selected, anthracycline‑resistant breast cancer cell lines using the RankProd package in R. Additionally, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to analyze GO term enrichment and pathways, respectively. A protein-protein interaction (PPI) network was also generated using Cytoscape software. The meta-analysis yielded 413 DEGs related to anthracycline resistance in human breast cancer cells, and 374 of these were not involved in individual DEGs. GO analyses showed the 413 genes were enriched with terms such as ‘response to steroid metabolic process’, ‘chemical stimulus’, ‘external stimulus’, ‘hormone stimulus’, ‘multicellular organismal process’, and ‘system development’. Pathway analysis revealed significant pathways including steroid hormone biosynthesis, cytokine-cytokine receptor interaction, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, and arachidonic acid metabolism. The PPI network indicated that proteins encoded by TRIM29, VTN, CCNA1, and karyopherin α 5 (KPNA5)participated in a significant number of interactions. In conclusion, our meta-analysis provides a comprehensive view of gene expression patterns associated with acquired resistance to anthracycline in breast cancer cells, and constitutes the basis for additional functional studies.

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