Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28708
Title: Genomic sequence analysis of EGFR regulation by microRNAs in lung cancer
Authors: Chan, LWC 
Wang, FF
Cho, WCS
Keywords: EGFR
Genomic sequence analysis
Lung cancer
MicroRNA
Support vector machine
Untranslated region
Issue Date: 2012
Publisher: Bentham Science Publ Ltd
Source: Current topics in medicinal chemistry, 2012, v. 12, no. 8, p. 920-926 How to cite?
Journal: Current Topics in Medicinal Chemistry 
Abstract: Lung cancer is known as the top cancer killer in most developed countries. Epidermal growth factor receptor (EGFR) is frequently found to be activated by mutation or amplification in lung cancer. MicroRNA (miRNA) is a new class of small molecules that has emerged as important markers of lung cancer development and therapeutic target. There are queries on which miRNAs can regulate EGFR and it is important to predict the candidate miRNAs that target EGFR by bioinformatics and to investigate on the availability of these candidate miRNA regulators in lung cancer. Systematic and rigorous searches for miRNAs targeting EGFR were performed on 10 representative databases. The identified miRNAs that target EGFR were formulated into a conditional regulation matrix and then hierarchical clustering algorithm was applied for the analysis. The systematic search came up with 138 miRNAs that potentially target EGFR. Among them, 11 miRNAs including miR-7 and miR-128b were confirmed by published experimental data or literatures. There were 14 candidate miRNAs predicted by at least 3 prediction pipelines in this study which have never been previously reported to target EGFR. Further studies of these novel identified miRNAs may provide insight on the regulation of EGFR in lung cancer. To the best of our knowledge, this is the first bioinformatic study applying genomic sequence analysis for the prediction of miRNAs that target EGFR in lung cancer. This new strategy that integrates computational and published data approaches provides a fast and effective prediction of miRNAs in specific target genes involved in various diseases.
URI: http://hdl.handle.net/10397/28708
ISSN: 1568-0266
DOI: 10.2174/156802612800166747
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