Basic Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 28, 2020; 26(44): 6929-6944
Published online Nov 28, 2020. doi: 10.3748/wjg.v26.i44.6929
Development and validation of a three-long noncoding RNA signature for predicting prognosis of patients with gastric cancer
Jun Zhang, Hai-Yan Piao, Yue Wang, Mei-Yue Lou, Shuai Guo, Yan Zhao
Jun Zhang, Yue Wang, Shuai Guo, Yan Zhao, Department of Gastric Cancer, Liaoning Province Cancer Hospital and Institute (Cancer Hospital of China Medical University), Shenyang 110042, Liaoning Province, China
Hai-Yan Piao, Medical Oncology Department of Gastrointestinal Cancer, Liaoning Province Cancer Hospital and Institute (Cancer Hospital of China Medical University), Shenyang 110042, Liaoning Province, China
Mei-Yue Lou, Department of Gastroenterological Surgery, Kumamoto University, Graduate School of Medical Sciences, Kumamoto 860-8556, Kumamoto, Japan
Author contributions: Zhang J and Piao HY performed the majority of experiments, analyzed the data, and drafted the manuscript; Zhao Y designed the research; Guo S and Lou MY conducted the molecular biology assays and assisted in writing the manuscript; Wang Y collected and analyzed the data.
Supported by Liaoning S&T Project, No. 20180550971 and No. 20180550999; and Shenyang Young and Middle-Aged Scientific & Technological Innovation Talents Support Plan, No. 2018416017.
Institutional review board statement: The study was reviewed and approved by the Faculty of Science Ethics Committee at Liaoning Cancer Hospital & Institute (Cancer Hospital of China Medical University (No. 20181226).
Conflict-of-interest statement: No potential conflicts of interest are disclosed.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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:
Corresponding author: Yan Zhao, PhD, Professor, Surgical Oncologist, Department of Gastric Cancer, Liaoning Province Cancer Hospital and Institute (Cancer Hospital of China Medical University), No. 44 Xiaoheyan Road, Dadong District, Shenyang 110042, Liaoning Province, China.
Received: April 18, 2020
Peer-review started: April 18, 2020
First decision: July 29, 2020
Revised: August 6, 2020
Accepted: September 17, 2020
Article in press: September 17, 2020
Published online: November 28, 2020

Gastric cancer (GC) is one of the most frequently diagnosed gastrointestinal cancers throughout the world. Novel prognostic biomarkers are required to predict the prognosis of GC.


To identify a multi-long noncoding RNA (lncRNA) prognostic model for GC.


Transcriptome data and clinical data were downloaded from The Cancer Genome Atlas. COX and least absolute shrinkage and selection operator regression analyses were performed to screen for prognosis associated lncRNAs. Receiver operating characteristic curve and Kaplan-Meier survival analyses were applied to evaluate the effectiveness of the model.


The prediction model was established based on the expression of AC007991.4, AC079385.3, and AL109615.2 Based on the model, GC patients were divided into “high risk” and “low risk” groups to compare the differences in survival. The model was re-evaluated with the clinical data of our center.


The 3-lncRNA combination model is an independent prognostic factor for GC.

Keywords: Gastric cancer, Prognosis, Least absolute shrinkage and selection operator, Survival analysis, Long noncoding RNA

Core Tip: A model to predict survival of patients with gastric cancer was developed and validated by RNA sequencing and real-time reverse transcription-polymerase chain reaction assays. The model had an excellent performance: The areas under the curves for 3-year and 5-year survival were 0.78 and 0.75, respectively. The C-index was 0.72 (se = 0.022, 95% confidence interval: 0.67-0.76). The model contributed as a poor independent prognostic factor both in disease free survival and overall survival.