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, anthracyclineresistant 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.
Subject(s)
Anthracyclines/pharmacology , Antibiotics, Antineoplastic/pharmacology , Breast Neoplasms/genetics , Drug Resistance, Neoplasm/genetics , Gene Expression Regulation, Neoplastic , Transcriptome , Breast Neoplasms/drug therapy , Databases, Genetic , Datasets as Topic/statistics & numerical data , Female , Gene Ontology , Humans , Metabolic Networks and Pathways/genetics , Neoplasm Proteins/biosynthesis , Neoplasm Proteins/genetics , Neoplasm Proteins/physiology , Protein Interaction Maps , Tissue Array AnalysisABSTRACT
A method of line-defect calibration for line-scanning projection display is developed to accomplish acceptable display uniformity. The line scanning display uses a line modulating imaging and scanning device to construct a two-dimensional image. The inherent line-defects in an imaging device and optical lenses are the most fatal performance-degrading factor that should be overcome to reach the basic display uniformity level. Since the human eye recognizes line defects very easily, a method that perfectly removes line defects is required. Most line imaging devices are diffractive optical devices that require a coherent light source. This particular requirement makes the calibration method of sequential single pixel measurement and correction insufficient to take out the line defects distributed on screen due to optical crosstalk. In this report, we present a calibration method using a recursively converging algorithm that successfully transforms the unacceptable line-defected images into a uniform display image.
