ORIGINAL RESEARCH article
Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
This article is part of the Research TopicCancer Biomarkers: Molecular Insights into Diagnosis, Prognosis, and Risk PredictionView all 17 articles
Prognostic model for lung adenocarcinoma based on experimental drug-resistant cell lines and clinical patients
Provisionally accepted
Jiasheng Zhang1
Junnan Li2Xinyang Zhang2Chengwu Cao2Tianjie Zhou2Fengxian Liu2
Hang Fai Kwok1*
Hui Zou2*
Liqing Hu2*- 1University of Macau, Taipa, Macao, SAR China
- 2College of Medicine, Hunan Normal University, Changsha, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective: Despite advances in EGFR-TKIs for lung adenocarcinoma (LUAD), resistance remains a major hurdle. This study aimed to develop a prognostic model integrating immune microenvironment features and in vitro resistance mechanisms to predict outcomes and guide therapy. Materials and Methods: erlotinib-, gefitinib-, and osimertinib-resistant HCC827 cell lines were established by exposing them to increasing EGFR-TKIs concentrations. RNA-sequencing was conducted on non-resistant HCC827 and erlotinib/gefitinib resistant cell lines. From the erlotinib-resistant, gefitinib-resistant cell lines and The Cancer Genome Atlas Program-Lung adenocarcinoma (TCGA-LUAD) data, a prognostic risk score model was constructed via Least Absolute Shrinkage and Selection Operator- Cox Proportional Hazards Model (LASSO-COX). Furthermore, immune infiltration was assessed using Gene Set Variation Analysis (GSVA), and single-cell RNA-seq (GSE241934) resolved expression patterns in EGFR-mutant vs. wild-type tumors. In vitro validation included RT-PCR in Osimertinib resistant (OR)-HCC827 cells. Results: A 3-gene (PPP1R3G, CREG2, LYPD3) RiskScore was developed. The RiskScore predicted poor survival and resistance across all EGFR-TKI generations, with osimertinib-resistant HCC827 cells showing significant upregulation of signature genes. High-risk patients exhibited immune-suppressive microenvironments (enriched regulatory T cells, depleted mast cells) and distinct scRNA-seq profiles. A nomogram (C-index = 0.7) integrated RiskScore with clinical factors for personalized prognosis. Conclusion: This model bridges in vitro resistance mechanisms with clinical immune landscapes, offering a tool to stratify patients for EGFR-TKIs, immunotherapies, or combinatorial strategies.
Keywords: lung cancer, Prognosis prediction, EGFR-TKIs resistance, immune microenvironment, single cell RNA-seq (scRNA-seq)
Received: 26 Jun 2025; Accepted: 29 Oct 2025.
Copyright: © 2025 Zhang, Li, Zhang, Cao, Zhou, Liu, Kwok, Zou and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hang Fai Kwok, hfkwok@um.edu.mo
Hui Zou, zouhui@hunnu.edu.cn
Liqing Hu, huliqing@hunnu.edu.cn
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.