Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Sep 16, 2022; 10(26): 9285-9302
Published online Sep 16, 2022. doi: 10.12998/wjcc.v10.i26.9285
Development and validation of an epithelial–mesenchymal transition-related gene signature for predicting prognosis
De-Hua Zhou, Qian-Cheng Du, Zheng Fu, Xin-Yu Wang, Ling Zhou, Jian Wang, Cheng-Kai Hu, Shun Liu, Jun-Min Li, Meng-Li Ma, Hua Yu
De-Hua Zhou, Xin-Yu Wang, Ling Zhou, Hua Yu, Department of General Surgery, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
Qian-Cheng Du, Zheng Fu, Jian Wang, Cheng-Kai Hu, Shun Liu, Department of Thoracic surgery, Shanghai Xuhui Central Hospital, Shanghai 200031, China
Jun-Min Li, Meng-Li Ma, Surgical Intensive Care Unit, Shanghai Xuhui Central Hospital, Shanghai 200031, China
Author contributions: Zhou DH and Du QC analyzed the data and wrote the manuscript, and both contributed equally to this work; Yu H designed the study; Wang XY, Fu Z, Zhou L, Wang J, Hu CK, Liu S, Li JM and Ma ML collected the data and revised the paper. All authors have read and approved the final manuscript.
Institutional review board statement: The data for the study came from public databases and did not involve blood or tissue samples from humans or animals. Therefore, there were no ethical issues involved in this study.
Conflict-of-interest statement: The authors declare no conflicts of interest.
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: Hua Yu, MM, Associate Chief Physician, Department of General Surgery, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, No. 1279 Sanmen Road, Shanghai 200434, China. luckyyuhua@163.com
Received: April 2, 2022
Peer-review started: April 2, 2022
First decision: June 16, 2022
Revised: June 30, 2022
Accepted: July 20, 2022
Article in press: July 20, 2022
Published online: September 16, 2022
Processing time: 152 Days and 14.5 Hours
ARTICLE HIGHLIGHTS
Research background

The transformation between epithelial and stromal cells during cancer progression is important in cancer progression. Understanding the relationship between epithelial-mesenchymal transition (EMT)-related genes and lung adenocarcinoma is valuable for improving its prognosis.

Research motivation

To develop an EMT-related gene signature to predict the prognosis of lung adenocarcinoma.

Research objectives

To construct and validate prognostic polygenic signatures of differentially expressed genes associated with EMT.

Research methods

We constructed a prognostic model based on differentially expressed EMT-related genes from 445 lung adenocarcinoma samples and validated its feasibility with another 439 Lung adenocarcinoma samples.

Research results

Seven EMT-related prognostic gene signatures were developed and validated. Kaplan–Meier survival analysis showed that the overall survival of patients in the high-risk group was statistically significantly poorer than that of patients in the low-risk group. The risk score based on EMT-associated genes was an independent risk factor for overall survival.

Research conclusions

The EMT-related gene signature could be used as a feasible indicator to predict the overall survival of lung adenocarcinoma patients. The molecular structures of potential therapeutic agents associated with EMT genes that could be used to treat lung adenocarcinoma were identified.

Research perspectives

More accurate genetic markers and models are needed to predict prognosis because of the low survival rate of lung adenocarcinoma.