Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/36342
Title: Exploring microRNA-mediated alteration of EGFR signaling pathway in non-small cell lung cancer using an mRNA:miRNA regression model supported by target prediction databases
Authors: Wang, F
Chan, LWC 
Law, HKW 
Cho, WCS
Tang, P
Yu, J
Shyu, CR
Wong, SCC 
Yip, SP 
Yung, BYM 
Keywords: miRNA
EGFR signaling pathway
Regression model
Microarray analysis
Non-small cell lung cancer
Issue Date: 2014
Publisher: Academic Press
Source: Genomics, 2014, v. 104, no. 6, p. 504-511 How to cite?
Journal: Genomics 
Abstract: EGFR signaling pathway and microRNAs (miRNAs) are two important factors for development and treatment in non-small cell lung cancer (NSCLC). Microarray analysis enables the genome-wide expression profiling. However, the information from microarray data may not be fully deciphered through the existing approaches. In this study we present an mRNA:miRNA stepwise regression model supported by miRNA target prediction databases. This model is applied to explore the roles of miRNAs in the EGFR signaling pathway. The results show that miR-145 is positively associated with epidermal growth factor (EGF) in the pre-surgery NSCLC group and miR-199a-5p is positively associated with EGF in the post-surgery NSCLC group. Surprisingly, miR-495 is positively associated with protein tyrosine kinase 2 (PTK2) in both groups. The coefficient of determination (R-2) and leave-one-out cross-validation (LOOCV) demonstrate good performance of our regression model, indicating that it can identify the miRNA roles as oncomirs and tumor suppressor mirs in NSCLC.
URI: http://hdl.handle.net/10397/36342
ISSN: 0888-7543
DOI: 10.1016/j.ygeno.2014.09.004
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