Title :
In-silico analysis of EGFR-associated microRNA signature in cancer
Author :
Fengfeng Wang ; Chan, Louiza ; Law, Helen K. W. ; Wong, Charence ; Yip, S.P. ; Yung, Benjamin Y. M. ; Cho, William C. S.
Author_Institution :
Dept. of Health Technol. & Inf., Hong Kong Polytech. Univ., Hong Kong, China
Abstract :
Epidermal growth factor receptor (EGFR) is often overexpressed or mutated in human carcinomas. The EGFR signaling pathway plays a vital role in regulation of cell proliferation and apoptosis. At the moment the existing anti-cancer drugs for EGFR are limited or not effective. It is important to develop novel approaches to inhibiting the expression of EGFR and its signaling pathway. Recently, researchers have considered investigating EGFR down-regulation by microRNAs (miRNAs), single-stranded noncoding RNA molecules with about 22 nucleotides long. In this study, in-silico strategies were used to identify an eight-EGFR-associated-miRNA signature. We also studied the association of this miRNA signature and the directly interacting partners of EGFR. We investigated the functional role of this miRNA signature using the multiple linear regression analysis based on the expression profiles between mRNAs and their corresponding miRNAs, to verify if the regression coefficients would implicate the functional miRNA-mRNA relationships. The results showed that four potential miRNAs (miR-27a, miR-155, miR-27b and miR-7) positively or negatively associated with the expression of EGFR and its interacting partner PIK3CA were found in the cancer group, but not in the normal group. Our findings will contribute to the exploration of cancer mechanisms, as well as identification of cancer treatment targets and diagnostic markers.
Keywords :
RNA; association; biochemistry; cancer; cellular biophysics; drugs; molecular biophysics; regression analysis; EGFR down-regulation; EGFR signaling pathway; EGFR-associated microRNA signature; EGFR-associated-miRNA signature; anticancer drugs; cancer treatment targets; cell apoptosis; cell proliferation; diagnostic markers; epidermal growth factor receptor; human carcinomas; in-silico analysis; interacting partner PIK3CA; multiple linear regression analysis; nucleotides; single-stranded noncoding RNA molecules; Cancer; Cancer drugs; Decision support systems; Informatics; Linear regression; Oncology; RNA; EGFR; EGFR signaling pathway; miRNA; multiple linear regression;
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on
Conference_Location :
Shanghai
DOI :
10.1109/BIBM.2013.6732612