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
ARTICLE HIGHLIGHTS
Research background

With the popularization and advancement of high throughput sequencing technologies, there is a real possibility of establishing multiple-gene signatures based on data integration and bioinformatics analysis in cancer research. The present study aimed to identify prognostic biomarkers for gastric carcinoma (GC) patients using comprehensive bioinformatics analyses.

Research motivation

GC is one of the most aggressive primary digestive tumors. It has unsatisfactory therapeutic outcomes and is difficult to diagnose early. Therefore, it is necessary to search for a multiple-gene signature-derived model for predicting prognosis and accurately identifying anti-cancer targeted therapies to improve the prognostic stratification and personalized therapy for GC patients.

Research objectives

We aimed to explore the potential multiple-gene prognostic biomarkers and effective therapeutic targets for GC. In this study, we performed integrated bioinformatics analysis to establish a nine-gene risk score model (COL8A1, CTHRC1, COL5A2, SERPINE1, COL1A2, FNDC1 AADAC, MAOA, and MAMDC2) associated with prognosis and treatment response in GC patients. The nine-gene signature-derived risk score allows to predict GC prognosis and might prove useful for guiding therapeutic strategies for GC patients.

Research methods

Differentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas and GEO databases for GC. Overlapping DEGs were analyzed using univariate and multivariate Cox regression analyses. A risk score model was then constructed and signature prognostic values were validated utilizing an independent GEO dataset (GSE15459). CBioPortal, GEPIA, and KM-plotter databases were used to analyze each gene in the risk score model. Gene set enrichment analysis and the connectivity map database were used to predict high-risk score-associated pathways and therapeutic small molecule drugs, respectively.

Research 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.

Research conclusions

Using a series of comprehensive bioinformatics analyses and validations, a novel nine-gene signature was constructed. The signature-derived risk score model had a robust prognostic capacity and therapeutic response in GC. Several small molecule drugs were identified to serve as potential therapeutic candidates for GC using bioinformatics. Further experimental studies are necessary to validate these findings and to elucidate the mechanisms for GC-related signaling pathways.

Research perspectives

Multiple-gene assays are of great importance for precision medicine of GC patients. To further verify the prognostic capacity of the nine-gene signature, our future study may pay more attention to exploring the potential regulatory mechanisms how the nine-gene signature affects the development of GC.