Zhong C, Liang Y, Wang Q, Tan HW, Liang Y. Construction and validation of a novel prediction system for detection of overall survival in lung cancer patients. World J Clin Cases 2022; 10(18): 5984-6000 [PMID: 35949842 DOI: 10.12998/wjcc.v10.i18.5984]
Corresponding Author of This Article
Yun Liang, MD, Attending Doctor, Department of Hematology and Oncology, Fengdu People's Hospital, No. 33 Lutang Street, Sanhe Town, Chongqing 408200, China. dr_ly123@163.com
Research Domain of This Article
Oncology
Article-Type of This Article
Clinical and Translational Research
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. Jun 26, 2022; 10(18): 5984-6000 Published online Jun 26, 2022. doi: 10.12998/wjcc.v10.i18.5984
Construction and validation of a novel prediction system for detection of overall survival in lung cancer patients
Cheng Zhong, Yun Liang, Qun Wang, Hao-Wei Tan, Yan Liang
Cheng Zhong, Qun Wang, Hao-Wei Tan, Department of Respiratory, Fenghua District People’s Hospital, Ningbo 315000, Zhejiang Province, China
Yun Liang, Yan Liang, Department of Hematology and Oncology, Fengdu People's Hospital, Chongqing 408200, China
Author contributions: Zhong C and Liang Y conceptualized and designed the article; Zhong C and Wang Q analyzed and interpreted the data; Zhong C drafted of the article; Liang Y and Tang HW were responsible for critical revision of the article for important intellectual content.
Institutional review board statement: This study was approved by the Ethics Committee of the Fenghua District People’s Hospital.
Clinical trial registration statement: This study does not involve the clinical trials, so the clinical trial registration is not required.
Informed consent statement: The data that support the findings of current study are publicly available, so the signed informed consent document is not required.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yun Liang, MD, Attending Doctor, Department of Hematology and Oncology, Fengdu People's Hospital, No. 33 Lutang Street, Sanhe Town, Chongqing 408200, China. dr_ly123@163.com
Received: February 21, 2022 Peer-review started: February 21, 2022 First decision: March 23, 2022 Revised: March 30, 2022 Accepted: April 29, 2022 Article in press: April 29, 2022 Published online: June 26, 2022 Processing time: 116 Days and 1.6 Hours
ARTICLE HIGHLIGHTS
Research background
Many factors have an aberrant effect on the overall survival of lung cancer (LC) patients. In recent years, remarkable progress has been made in immunotherapy, targeted treatment, and promising biomarkers. However, the available treatments and diagnostic methods are not specific for all patients.
Research motivation
Identifying new diagnostic and therapeutic biomarkers for cancer treatment is urgent.
Research objectives
We aimed to establish a system for predicting poor survival in patients with LC.
Research methods
Weighted Gene Co-expression Network Analysis (WGCNA), functional enrichment analysis, quantitative real-time polymerase chain reaction, and other bioinformatics analysis were used in this study.
Research results
A total of 5007 differentially expressed genes were selected for the WGCNA algorithm. The turquoise module showed the highest correlation with patient prognosis. The gene module with the greatest positive correlation with patient survival was located in the turquoise area. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses performed for the genes contained in the turquoise module indicated the potential roles of the selected genes in the regulation of LC development. In addition, protein–protein interaction analysis was performed to screen hub genes, which identified 100 hub genes located in the core area of the network. We intersected the 100 hub genes with 75 key genes sorted by module members to identify real hub genes associated with prognosis. Forty-one genes were finally selected. We used a logistic regression model to determine 11 independent risk genes, namely CCNB2, CDC20, CENPO, FOXM1, HJURP, NEK2, OIP5, PLK1, PRC1, SKA1, UBE2C and SPARC.
Research conclusions
We constructed a model based on 11 independent risk genes to establish a system to predict the survival status of patients with non-small-cell lung carcinoma.
Research perspectives
The new predictive model could play a role in overall survival.
