Clinical and Translational Research
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
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
Abstract
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.

AIM

To establish a system for predicting poor survival in patients with LC.

METHODS

The expression matrix and clinical information for this study were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. After the differential analysis of all screened genes, weighted gene coexpression network analysis was performed to analyze hub genes related to patient survival. A logistic regression model was used to construct the scoring system. The expression of the hub genes was verified by performing quantitative reverse transcription-polymerase chain reaction.

RESULTS

A total of 5007 differentially expressed genes were selected for the Weighted Gene Co-expression Network Analysis algorithm. We found that 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. The 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 then 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 then 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.

CONCLUSION

We constructed a predictive model based on 11 independent risk genes to establish a system predicting the survival status of patients with non-small-cell lung carcinoma.

Keywords: Lung cancer; Weighted Gene Co-expression Network Analysis; Hub genes; prognosis; Logistic regression

Core Tip: This was a bioinformatics-based study aimed at identifying a novel system for predicting overall survival in lung cancer patients. We constructed a predictive model using Weighted Gene Co-expression Network Analysis, protein-protein interaction network, and least absolute contraction and selection operator-logistic regression analysis. And the expression of hub genes was verified by polymerase chain reaction, immunohistochemistry in lung cancer cell lines, and patient samples.