Clinical and Translational Research
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jul 16, 2023; 11(20): 4763-4787
Published online Jul 16, 2023. doi: 10.12998/wjcc.v11.i20.4763
Identification of survival-associated biomarkers based on three datasets by bioinformatics analysis in gastric cancer
Long-Kuan Yin, Hua-Yan Yuan, Jian-Jun Liu, Xiu-Lian Xu, Wei Wang, Xiang-Yu Bai, Pan Wang
Long-Kuan Yin, Hua-Yan Yuan, Jian-Jun Liu, Xiu-Lian Xu, Wei Wang, Xiang-Yu Bai, Pan Wang, Department of Gastrointestinal Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Long-Kuan Yin, Xiang-Yu Bai, Pan Wang, Sichuan Key Laboratory of Medical Imaging, North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Author contributions: Yin LK and Yuan HY contributed equally to this work. Yin LK, Yuan HY, Liu JJ and Wang P contributed to data collection and manuscript drafting; Yin LK, Xu XL, Wang W, Bai XY and Wang P prepared for the figures and tables; and all authors have approved the final manuscript.
Institutional review board statement: All of the data of this paper are from the public database of TCGA, GEO and GEPIA, and there is no ethical statement needed to be declared for this manuscript.
Clinical trial registration statement: All of the data of this paper are from the public database of TCGA, GEO and GEPIA, and no clinical trial registration needed for this manuscript.
Informed consent statement: All of the data of this paper are from the public database of TCGA, GEO and GEPIA, and no informed consent form documents are needed to be signed by any patients.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data supporting the results of this study are available from GEO database (GSE19826, GSE79973 and GSE29998).
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Pan Wang, MD, Doctor, Professor, Department of Gastrointestinal Surgery, Affiliated Hospital of North Sichuan Medical College, No. 1 Maoyuan South Road, Shunqing District, Nanchong 637000, Sichuan Province, China. ncwangpan@126.com
Received: January 4, 2023
Peer-review started: January 4, 2023
First decision: April 3, 2023
Revised: April 11, 2023
Accepted: June 6, 2023
Article in press: June 6, 2023
Published online: July 16, 2023
Processing time: 174 Days and 6.9 Hours
ARTICLE HIGHLIGHTS
Research background

Gastric cancer (GC) is one of the most common malignant tumors, and its pathogenesis and biomarkers are still unclear.

Research motivation

The present study for the first time investigated the 10 Hub genes as the potential biomarkers of the prognosis of patients using bioinformatics.

Research objectives

The aims of this study are to explore the potential biomarkers of the prognosis of patients with GC, so as to provide new strategies for the treatment of GC.

Research methods

In this study, bioinformatics strategy was used to obtain Datasets from The Cancer Genome Atlas, Gene Expression Omnibus and Gene Expression Profiling Interactive Analysis. The software of R software, STRING, Kaplan-Meier plotter and Human Protein Atlas, were performed to analyze and integrate the mRNA datasets, respectively.

Research results

The signal pathways of the involvement of the co-expression of differential genes in GC were screened out, and the 10 Hub genes, including BGN, CEP55, COL1A2, COL4A1, FZD2, MAOA, PDGFRB, SPARC, TIMP1 and VCAN, were associated with prognosis of GC and identified as the potential prognostic biomarkers of GC.

Research conclusions

The 10 key genes obtained through the analysis of multiple datasets may be used as objective and reliable biomarkers for the survival analysis of patients. In addition, these genes or their encoded proteins can also be as potential therapeutic targets for GC, improving the survival time of patients with GC.

Research perspectives

The mechanisms of 10 Hub genes in GC is still unclear, which needs further confirmation through molecular biology and clinical experiments.