Zhang L, Liu Y, Zhuang JG, Guo J, Li YT, Dong Y, Song G. Identification of key genes and biological pathways in lung adenocarcinoma by integrated bioinformatics analysis. World J Clin Cases 2023; 11(23): 5504-5518 [PMID: 37637684 DOI: 10.12998/wjcc.v11.i23.5504]
Corresponding Author of This Article
Gang Song, MD, PhD, Chief Physician, Second Department of Respiratory Medicine, Hebei Provincial Hospital of Traditional Chinese Medicine, No. 389 Zhongshan East Road, Shijiazhuang 050000, Hebei Province, China. songgang202302@163.com
Research Domain of This Article
Mathematical & Computational Biology
Article-Type of This Article
Meta-Analysis
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Clin Cases. Aug 16, 2023; 11(23): 5504-5518 Published online Aug 16, 2023. doi: 10.12998/wjcc.v11.i23.5504
Identification of key genes and biological pathways in lung adenocarcinoma by integrated bioinformatics analysis
Lin Zhang, Yuan Liu, Jian-Guo Zhuang, Jie Guo, Yan-Tao Li, Yan Dong, Gang Song
Lin Zhang, Yuan Liu, Yan-Tao Li, Yan Dong, Department of Critical Medicine, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang 050000, Hebei Province, China
Jian-Guo Zhuang, Department of Internal Medicine, Xiongxian Hospital of Traditional Chinese Medicine, Baoding 071800, Hebei Province, China
Jie Guo, Gang Song, Second Department of Respiratory Medicine, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang 050000, Hebei Province, China
Author contributions: Zhang L designed the manuscript and analyzed the data; Zhang L, Liu Y, and Zhuang JG wrote the main text; Guo J and Li YT prepared the figures and tables; Song G reviewed the manuscript; and all authors read and approved the final version of the manuscript.
Conflict-of-interest statement: The author claims that there is no competitive advantage.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: Gang Song, MD, PhD, Chief Physician, Second Department of Respiratory Medicine, Hebei Provincial Hospital of Traditional Chinese Medicine, No. 389 Zhongshan East Road, Shijiazhuang 050000, Hebei Province, China. songgang202302@163.com
Received: May 19, 2023 Peer-review started: May 19, 2023 First decision: June 21, 2023 Revised: June 29, 2023 Accepted: July 25, 2023 Article in press: July 25, 2023 Published online: August 16, 2023 Processing time: 88 Days and 23.1 Hours
Abstract
BACKGROUND
The objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma (LUAD) via bioinformatics analysis, and investigate potential therapeutic targets.
AIM
To determine reliable prognostic biomarkers for early diagnosis and treatment of LUAD.
METHODS
To identify potential therapeutic targets for LUAD, two microarray datasets derived from the Gene Expression Omnibus (GEO) database were analyzed, GSE3116959 and GSE118370. Differentially expressed genes (DEGs) in LUAD and normal tissues were identified using the GEO2R tool. The Hiplot database was then used to generate a volcanic map of the DEGs. Weighted gene co-expression network analysis was conducted to cluster the genes in GSE116959 and GSE118370 into different modules, and identify immune genes shared between them. A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database, then the CytoNCA and CytoHubba components of Cytoscape software were used to visualize the genes. Hub genes with high scores and co-expression were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform enrichment analysis of these genes. The diagnostic and prognostic values of the hub genes were calculated using receiver operating characteristic curves and Kaplan-Meier survival analysis, and gene-set enrichment analysis was conducted. The University of Alabama at Birmingham Cancer data analysis portal was used to analyze relationships between the hub genes and normal specimens, as well as their expression during tumor progression. Lastly, validation of protein expression was conducted on the identified hub genes via the Human Protein Atlas database.
RESULTS
Three hub genes with high connectivity were identified; cellular retinoic acid binding protein 2 (CRABP2), matrix metallopeptidase 12 (MMP12), and DNA topoisomerase II alpha (TOP2A). High expression of these genes was associated with a poor LUAD prognosis, and the genes exhibited high diagnostic value.
CONCLUSION
Expression levels of CRABP2, MMP12, and TOP2A in LUAD were higher than those in normal lung tissue. This observation has diagnostic value, and is linked to poor LUAD prognosis. These genes may be biomarkers and therapeutic targets in LUAD, but further research is warranted to investigate their usefulness in these respects.
Core Tip: Lung cancer is an important cause of cancer-related death worldwide. This study conducted multiple bioinformatics analysis methods to explore potential therapeutic targets for lung adenocarcinoma (LUAD). Finally, three hub genes with high connectivity were identified, namely cellular retinoic acid binding protein 2, matrix metallopeptidase 12, and DNA topoisomerase II alpha. High expression of these genes was associated with a poor LUAD prognosis, and the genes exhibited high diagnostic value. Therefore, these genes may be biomarkers and therapeutic targets for LUAD.
