Distinct expression and prognostic value of members of the epidermal growth factor receptor family in ovarian cancer
Received 13 August 2018
Accepted for publication 16 November 2018
Published 13 December 2018 Volume 2018:10 Pages 6937—6948
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Rituraj Purohit
Quan Zhou,* Chao-Nan Hou,* Huai-Jie Yang,* Ze He, Man-Zhen Zuo
Department of Gynecology and Obstetrics, The People’s Hospital of China Three Gorges University/The First People’s Hospital of Yichang, Yichang 443000, China
*These authors contributed equally to this work
Background: Increased aberrant expression or activation of the epidermal growth factor receptor (EGFR) family members has been reported in a wide range of cancers, and the EGFR family of tyrosine kinases has emerged as an important therapeutic target in malignancies. However, the expression patterns and exact roles of each distinct EGFR family member, which contribute to tumorigenesis and progression of ovarian cancer (OC), are yet to be elucidated.
Materials and methods: In the current study, we report the distinct expression and prognostic value of EGFR family members in patients with OC by analyzing a series of databases including ONCOMINE, Gene Expression Profiling Interactive Analysis , Kaplan–Meier plotter, cBioPortal, and Database for Annotation, Visualization and Integrated Discovery.
Results: It was found that in patients with OC, mRNA expression levels of ERBB2/3/4 were significantly upregulated, whereas the transcription levels of EGFR were downregulated. Aberrant EGFR expression and ERBB2/3/4 mRNA levels were associated with OC prognosis.
Conclusion: These results suggest that EGFR and ERBB3/4 are distinct prognostic biomarkers and may be potential targets for OC. These results may be beneficial to better understand the molecular underpinning of OC and may be useful to develop tools for more accurate OC prognosis and for promoting the development of EGFR-targeted inhibitors for OC treatment.
Keywords: EGFR, ovarian cancer, database mining, prognostic value, bioinformatics analysis
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