Construction and validation of a novel prediction system for detection of overall survival in lung cancer patients

doi:10.12998/wjcc.v10.i18.5984

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World Journal of Clinical Cases

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Clinical and Translational Research

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.

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.