Basic Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Sep 15, 2020; 12(9): 975-991
Published online Sep 15, 2020. doi: 10.4251/wjgo.v12.i9.975
Identification of a nine-gene prognostic signature for gastric carcinoma using integrated bioinformatics analyses
Kun-Zhe Wu, Xiao-Hua Xu, Cui-Ping Zhan, Jing Li, Jin-Lan Jiang
Kun-Zhe Wu, Jin-Lan Jiang, Scientific Research Center, China-Japan Union Hospital of Jilin University, Changchun 130000, Jilin Province, China
Xiao-Hua Xu, Jing Li, Department of Nephrology, China-Japan Union Hospital of Jilin University, Changchun 130000, Jilin Province, China
Cui-Ping Zhan, Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun 130000, Jilin Province, China
Author contributions: Jiang JL designed this study; Li J and Zhan CP conducted the data analysis; Xu XH reviewed the article; Wu KZ wrote the article.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of China-Japan Union Hospital of Jilin University.
Conflict-of-interest statement: All the authors declare no conflict of interest.
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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Jin-Lan Jiang, PhD, Professor, Research Scientist, Scientific Research Center, China-Japan Union Hospital of Jilin University, No. 126 Xiantai Street, Changchun 130000, Jilin Province, China. jiangjinlan@jlu.edu.cn
Received: May 21, 2020
Peer-review started: April 5, 2020
First decision: May 15, 2020
Revised: May 15, 2020
Accepted: August 1, 2020
Article in press: August 1, 2020
Published online: September 15, 2020
Processing time: 155 Days and 1.6 Hours
Abstract
BACKGROUND

Gastric carcinoma (GC) is one of the most aggressive primary digestive cancers. It has unsatisfactory therapeutic outcomes and is difficult to diagnose early.

AIM

To identify prognostic biomarkers for GC patients using comprehensive bioinformatics analyses.

METHODS

Differentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas and Gene Expression Omnibus databases for GC. Overlapping DEGs were analyzed using univariate and multivariate Cox regression analyses. A risk score model was then constructed and its prognostic value was validated utilizing an independent Gene Expression Omnibus dataset (GSE15459). Multiple databases were used to analyze each gene in the risk score model. High-risk score-associated pathways and therapeutic small molecule drugs were analyzed and predicted, respectively.

RESULTS

A total of 95 overlapping DEGs were found and a nine-gene signature (COL8A1, CTHRC1, COL5A2, AADAC, MAMDC2, SERPINE1, MAOA, COL1A2, and FNDC1) was constructed for the GC prognosis prediction. Receiver operating characteristic curve performance in the training dataset (The Cancer Genome Atlas-stomach adenocarcinoma) and validation dataset (GSE15459) demonstrated a robust prognostic value of the risk score model. Multiple database analyses for each gene provided evidence to further understand the nine-gene signature. Gene set enrichment analysis showed that the high-risk group was enriched in multiple cancer-related pathways. Moreover, several new small molecule drugs for potential treatment of GC were identified.

CONCLUSION

The nine-gene signature-derived risk score allows to predict GC prognosis and might prove useful for guiding therapeutic strategies for GC patients.

Keywords: Gastric carcinoma; Bioinformatic analysis; Prognosis; Overall survival; Differentially expressed genes

Core Tip: A total of 95 differentially expressed genes were found by mining the datasets of Gene Expression Omnibus and the Cancer Genome Atlas databases. Overlapping differentially expressed genes were analyzed using univariate and multivariate Cox regression analyses. Receiver operating characteristic curve performance in the training and validation datasets demonstrated a robust prognostic value of the risk score model. Multiple database analyses for each gene provided evidence to further understand the nine-gene signature. Gene set enrichment analysis showed that the high-risk group was enriched in multiple cancer-related pathways. Moreover, several new small molecule drugs for potential treatment of gastric carcinoma (GC) were identified. A nine-gene signature was identified to predict GC prognosis and prove potentially useful for guiding therapeutic strategies for GC patients.