Basic Study
Copyright ©The Author(s) 2018. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Aug 14, 2018; 24(30): 3426-3439
Published online Aug 14, 2018. doi: 10.3748/wjg.v24.i30.3426
Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma
Qiu-Jie Zhao, Jiao Zhang, Lin Xu, Fang-Feng Liu
Qiu-Jie Zhao, Jiao Zhang, Lin Xu, Department of Gastroenterology, Shandong Provincial Hospital affiliated to Shandong University, Jinan 250021, Shandong Province, China
Fang-Feng Liu, Department of Hepatobiliary Surgery, Shandong Provincial Hospital affiliated to Shandong University, Jinan 250021, Shandong Province, China
Author contributions: Zhao QJ conceived the study, drafted and revised the manuscript; Zhang J and Xu L helped with the statistical analysis; Liu FF helped participated in data mining; all authors read and approved the final manuscript.
Supported by the National Nature Science Foundation of China, No. 81702816 (to Zhao QJ); and Shandong Provincial Natural Science Foundation, No. ZR2017PH030 (to Zhao QJ).
Institutional review board statement: This studied mined the TCGA database and doesn’t involve any experiments with animals or human beings. Because the TCGA data are a community resource project, additional ethical approval was not acquired.
Conflict-of-interest statement: The authors declare that there is no conflict of interest related to this study.
Data sharing statement: The datasets supporting the conclusions of this article are included within the article.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Qiu-Jie Zhao, PhD, Chief Doctor, Department of Gastroenterology, Shandong Provincial Hospital affiliated to Shandong University, No. 324, Jingwuwei 7th Road, Jinan 250021, Shandong Province, China. zhaoqiujiesdu@126.com
Telephone: +86-531-68772952
Received: March 27, 2018
Peer-review started: March 28, 2018
First decision: May 9, 2018
Revised: May 24, 2018
Accepted: June 22, 2018
Article in press: June 22, 2018
Published online: August 14, 2018
Processing time: 139 Days and 16 Hours
ARTICLE HIGHLIGHTS
Research background

Hepatocellular carcinoma (HCC) is the sixth most commonly diagnosed cancer in the world. Although treatment for HCC, including surgical resection, has improved over the past decades, its overall survival rate remains devastatingly high due to its high rate of recurrence. Because HCC is a heterogeneous disease with substantially variable clinical outcomes, the search for effective biomarkers to predict recurrence and prognosis is crucial.

Research motivation

Recent studies have demonstrated the importance of long non-coding RNAs (lncRNAs) in physiological and pathological cellular processes. Increasing evidence suggests that lncRNA dysregulation is associated with various human diseases, particularly the initiation and progression of various human cancers. For patients with HCC, most of the existing prognostic signatures have focused on mRNAs or microRNAs, and only a few lncRNA signatures have been developed. In the present study, we aimed to construct a lncRNA signature for the prediction of HCC prognosis with high efficiency.

Research objectives

To construct a lncRNA signature for the prediction of HCC prognosis with high efficiency.

Research methods

Differentially expressed lncRNAs (DELs) between HCC specimens and peritumor liver specimens were acquired from the The Cancer Genome Atlas (TCGA) LIHC dataset using the edgeR package. Univariate Cox proportional hazards regression was performed to identify the DELs that were significantly associated with overall survival for the training set. The stepwise multivariate Cox regression model was applied. Those lncRNAs fitted in the multivariate Cox regression model and independently associated with overall survival were chosen to build a prognostic risk formula. The prognostic value of this formula was validated in the test group and the full cohort and further compared with two previously developed prognostic signatures for HCC.

Research results

We identified a five-lncRNA prognostic signature from the TCGA dataset and determined that its prognostic value was independent from clinicopathological factors. The signature was reproducible and robust in another independent large-scale HCC cohort, supporting its utility and effectiveness.

Research conclusions

This study constructed a 5-lncRNA signature that improves survival prediction, and can be used as a prognostic biomarker for HCC patients.