Basic Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Jan 24, 2023; 14(1): 27-39
Published online Jan 24, 2023. doi: 10.5306/wjco.v14.i1.27
5-mRNA-based prognostic signature of survival in lung adenocarcinoma
Qian-Lin Xia, Xiao-Meng He, Yan Ma, Qiu-Yue Li, Yu-Zhen Du, Jin Wang
Qian-Lin Xia, Yu-Zhen Du, Laboratory Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
Xiao-Meng He, Yan Ma, Qiu-Yue Li, Jin Wang, Scientific Research Center, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
Author contributions: Wang J, Du YZ, and Xia QL conceived and designed the experiments; Xia QL, He XM and Ma Y analyzed the data; Li QY contributed to analysis tools; Xia QL wrote the manuscript; Wang J and Xia QL revised the manuscript; all authors have read and approved the final manuscript.
Supported by the Science and Technology Development Fund of the Pudong New Area, No. PKJ2021-Y53; and the National Natural Science Foundation of China, No. 81974315.
Institutional review board statement: The study was reviewed and approved by the Shanghai Public Health Clinical Center Laboratory Animal Welfare & Ethics Committee Institutional Review Board [(Approval No. 2020-A006-01]).
Conflict-of-interest statement: All the authors declare no competing financial interests.
Data sharing statement: The mRNA expression and clinical data of lung adenocarcinoma analyzed during the current study are available on the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA databases (https://www.cbioportal.org/). The protein expression of model-related genes of LUAD analyzed in this study is also available on the THPA database (http://www.proteinatlas.org).
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: Jin Wang, PhD, Professor, Scientific Research Center, Shanghai Public Health Clinical Center, Fudan University, No 2901 Caolang Road, Jinshan District, Shanghai 201508, China. wjincityu@yahoo.com
Received: October 5, 2022
Peer-review started: October 5, 2022
First decision: October 24, 2022
Revised: November 2, 2022
Accepted: December 13, 2022
Article in press: December 13, 2022
Published online: January 24, 2023
Processing time: 97 Days and 3.4 Hours
Abstract
BACKGROUND

Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis.

AIM

To construct effective predictive models to evaluate the prognosis of LUAD patients.

METHODS

In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram.

RESULTS

A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively.

CONCLUSION

A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

Keywords: Lung adenocarcinoma; Differentially expressed genes; Prognostic signature; Risk score; Nomogram

Core Tip: Five differentially expressed genes (DEGs) (TCN1, CENPF, MAOB, CRTAC1, and PLEK2) selected by multiple Cox regression analysis in the prognostic risk models could be considered as independent prognostic factors for lung adenocarcinoma.