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
ARTICLE HIGHLIGHTS
Research background
Adenocarcinoma of the lung (LUAD) is currently a cancer with high mortality. This study identified the biomarkers and therapeutic targets related to LUAD through bioinformatics analysis.
Research motivation
As of now, there are few biological analyses related to LUAD. Therefore, this study hopes to further study through big data analysis.
Research objectives
To determine reliable prognostic biomarkers for early diagnosis and treatment of Adenocarcinoma of the lung.
Research methods
This article adopts bioinformatics methods such as gene Expression Omnibus (GEO) database, weighted gene co-expression network analysis, GEO2R, Gene Ontology analysis, protein-protein interaction network construction, University of Alabama at Birmingham Cancer data analysis portal database, etc.
Research results
We found three genes, namely, cellular retinoic acid binding protein 2, matrix Metalloprotein peptidase 12 and DNA Topoisomerase II α. The high expression of these genes is related to the poor prognosis of LUAD, and these Gene expression have high diagnostic value.
Research conclusions
We identified genes related to LUAD treatment and prognosis through bioinformatics methods, providing important information for the complete cure of LUAD.
Research perspectives
At present, there is little bioinformatics research related to LUAD. Through Big data screening, this study has more accurately identified the biomarkers and therapeutic targets related to LUAD, providing important information for the complete cure of LUAD in the future.
