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
Research background

Gastric cancer (GC) is one of the most frequently diagnosed gastrointestinal cancers throughout the world. It is necessary to identify a multi-long noncoding RNA (lncRNA) prognostic model for GC.

Research motivation

Abnormal expression of lncRNAs may indirectly reflect the occurrence and development of GC. As genes do not usually act alone, it is necessary to select suitable lncRNAs and establish a multi-lncRNA prediction model.

Research objectives

To construct a multi-lncRNA combination model to predict the prognosis of gastric cancer patients.

Research methods

The RNA-seq dataset and clinical dataset of GC in The Cancer Genome Atlas were used in this study. The least absolute shrinkage and selection operator and COX models were used to identify meaningful modules and hub genes. Clinical data of 200 patients were used to evaluate the clinical significance of the multi-lncRNA combination model via survival analysis.

Research results

We found a 3-lncRNA combination prediction model: AC007991.4, AC079385.3, and AL109615.2. It could effectively predict the prognosis of GC. AC079385.3 was found to be a prognostic risk factor for GC, and it may play an important role in the development of GC. Least absolute shrinkage and selection operator improved prediction accuracy and interpretability through variable selection and regularization.

Research conclusions

The 3-lncRNA combination model (risk score = -0.92 × AC007991.4 + 1.18 × AC079385.3 + 1.17 × AL109615.2) is an independent prognostic factor for GC.

Research perspectives

Clinicians can obtain the expression levels of AC007991.4, AC079385.3, and AL109615.2 in tissue samples by real-time reverse transcription-polymerase chain reaction and calculate the corresponding risk values.