Basic Study
Copyright ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Nov 15, 2019; 11(11): 983-997
Published online Nov 15, 2019. doi: 10.4251/wjgo.v11.i11.983
Eight key long non-coding RNAs predict hepatitis virus positive hepatocellular carcinoma as prognostic targets
Zi-Lin Huang, Wang Li, Qi-Feng Chen, Pei-Hong Wu, Lu-Jun Shen
Zi-Lin Huang, Wang Li, Qi-Feng Chen, Pei-Hong Wu, Lu-Jun Shen, Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, Guangdong Province, China
Zi-Lin Huang, Wang Li, Qi-Feng Chen, Pei-Hong Wu, Lu-Jun Shen, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong Province, China
Author contributions: Huang ZL, Li W, and Chen QF contributed equally to this work; Huang ZL, Li W, and Chen QF contributed to study conceptualization; Chen QF contributed to the methodology; Wu PH contributed to software; Chen QF, Huang ZL, and Li W contributed to data validation; Chen QF, Wu PH, Huang ZL, and Shen LJ analyzed the data and contributed to manuscript writing and editing; Chen QF and Li W contributed to manuscript drafting; Chen QF contributed to visualization and supervised the final paper.
Institutional review board statement: Not applicable, because the data were publicly available.
Institutional animal care and use committee statement: Not applicable, because no animal was used in the present study.
Conflict-of-interest statement: The authors deny any conflict of interest.
Data sharing statement: The data used in this manuscript are accessible through: https://portal.gdc.cancer.gov/
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/
Corresponding author: Qi-Feng Chen, MD, Doctor, Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, 651 East Dongfeng Road, Yuexiu District, Guangzhou 510060, Guangdong Province, China. ch_peaks@126.com
Telephone: +86-15626062848 Fax: +86-20-87343392
Received: March 27, 2019
Peer-review started: March 28, 2019
First decision: April 15, 2019
Revised: July 26, 2019
Accepted: September 12, 2019
Article in press: September 12, 2019
Published online: November 15, 2019
Processing time: 233 Days and 22.6 Hours
ARTICLE HIGHLIGHTS
Research background

Hepatocellular carcinoma (HCC), one of the most frequent liver cancer subtype, has posed a serious health issue in the world. Hepatitis virus is recognized to be a major factor leading to HCC. Recently, a verity of genetic markers as well as prediction models are proposed to improve HCC treatment. At the same time, numerous statistical techniques are also utilized to mine data in numerous large-scale public databases. Thanks to the well-developed clinical approaches, prognosis models with a higher accuracy and robustness can be established for HCC.

Research motivation

This study aimed to establish a prognosis model on the basis of HCC molecular biomarkers. Typically, long non-coding RNAs (lncRNAs) have been recognized as new predictive factors. Great efforts have been made to establish lncRNA-based HCC models, however, the lncRNA features for hepatitis virus positive HCC (VHCC) are not available at present.

Research objectives

This study aimed to develop a prognostic lncRNA feature for VHCC based on candidate lncRNAs through analyzing data collected from The Cancer Genome Atlas (TCGA) database.

Research methods

Specifically, the least absolute shrinkage and selection operator (LASSO), the most advanced statistic algorithm, was used to establish the prediction model. This approach was carried out on the basis of typical lncRNAs selected according to the expression profiles of lncRNAs collected from the TCGA database. The as-established lncRNA feature was validated, and its clinical applicability was also examined.

Research results

Using LASSO, a risk score system was established to predict the overall survival (OS) for VHCC, which incorporated eight lncRNAs (including AC005722.2, AC107959.3, AL353803.1, AL589182.1, AP000844.2, AP002478.1, FLJ36000, and NPSR1-AS1). Notably, the as-constructed lncRNA feature was helpful in stratifying the risk for VHCC. To extend the model application range, a nomogram was also constructed, which involved both the lncRNA feature and other clinical features. Our results also suggested that the lncRNAs were markedly enriched in the Wnt signaling pathway, angiogenesis, the p53 pathway, and the PI3 kinase pathway.

Research conclusions

The as-constructed signature based on eight lncRNAs displayed a favorable capacity in predicting the prognosis for VHCC patients, which can also contribute to risk stratification and may provide more useful clinical advice for individual patients.

Research perspectives

Our results further support that lncRNAs can serve as possible functional regulating factors for the progression of VHCC. It is the future direction to search for the effective molecular biomarkers and predictive factors for the prognosis of HCC patients.