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BMC Cancer

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Open Access 01-12-2016 | Research article

Increased long noncoding RNA SNHG20 predicts poor prognosis in colorectal cancer

Authors: Cong Li, Li Zhou, Jun He, Xue-Qing Fang, Shao-Wen Zhu, Mao-Ming Xiong

Published in: BMC Cancer | Issue 1/2016

Abstract

Background

Long noncoding RNAs (lncRNAs) have been suggested to be involved in the development and progression of malignancies. However, the investigation of small nucleolar RNA host gene 20 (SNHG20) on cancer progression remains unknown. The present study aims to explore the clinical significance of SNHG20 and its potential molecular mechanism in colorectal cancer (CRC).

Methods

Quantitative real-time PCR (qRT-PCR) was used to measure the SNHG20 expression in a total of 107 CRC tissues and CRC cell lines. Loss of function approach was employed to explore the biological roles of SNHG20 in vitro. Its potential molecular mechanism was further verified by western blotting and qRT-PCR.

Results

The results suggested that SNHG20 expression was significantly upregulated in CRC tissues compared to corresponding normal tissues from 107 CRC patients. High expression of SNHG20 was remarkably associated with advanced TNM stage in patients with CRC. Multivariate analyses unraveled that SNHG20 expression was an independent prognostic factor for overall survival in CRC patients. Further functional assays revealed that knockdown of SNHG20 suppressed cell proliferation, invasion and migration, and cell cycle progression in CRC cells. Moreover, SNHG20 regulated cell growth through modulation of a series of cell cycle-associated genes.

Conclusions

Our findings suggest that dysregulation of SNHG20 participates in CRC progression and may serve as a potential therapeutic target in CRC patients.

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Title: Increased long noncoding RNA SNHG20 predicts poor prognosis in colorectal cancer
Authors: Cong Li
Li Zhou
Jun He
Xue-Qing Fang
Shao-Wen Zhu
Mao-Ming Xiong
Publication date: 01-12-2016
Publisher: BioMed Central
Published in: BMC Cancer / Issue 1/2016
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-016-2719-x

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Abstract

Background

Methods

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