ORIGINAL RESEARCH article

Front. Cell Dev. Biol.

Sec. Cancer Cell Biology

Volume 13 - 2025 | doi: 10.3389/fcell.2025.1622345

This article is part of the Research TopicProgress in Molecular Mechanisms and Targeted Therapies for Solid Tumor MicroenvironmentsView all 4 articles

Machine learning developed an immune evasion signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma

Provisionally accepted
Dongxiao DingDongxiao Ding1Gang HuangGang Huang1Liangbin WangLiangbin Wang1Ke ShiKe Shi1Junjie YingJunjie Ying1Wenjun ShangWenjun Shang1Li WangLi Wang1Chong ZhangChong Zhang2Maofen JiangMaofen Jiang1*Yaxing ShenYaxing Shen3
  • 1Beilun District People's Hospital of Ningbo, Ningbo, China
  • 2First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
  • 3Zhongshan Hospital, Fudan University, Shanghai, China

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Background: Lung adenocarcinoma (LUAD) is one of the most common cancers worldwide and a major cause of cancer-related deaths. The advancement of immunotherapy has expanded the treatment options for LUAD. However, the clinical outcomes of LUAD patients have not been as anticipated, potentially due to immune escape mechanisms. Methods: An integrative machine learning approach, comprising ten methods, was applied to construct an immune escape-related signature (IRS) using the TCGA, GSE72094, GSE68571, GSE68467, GSE50081, GSE42127, GSE37745, GSE31210 and GSE30129 datasets. The relationship between IRS and the tumor immune microenvironment was analyzed through multiple techniques. In vivo experiments were performed to investigate the biological roles of the key gene.Results: The model developed by Lasso was regarded as the optional IRS, which served as an independent risk factor and had a good performance in predicting the clinical outcome of LUAD patients. Low IRS-based risk score indicated higher level of NK cells, CD8+ T cells, and immune activation-related functions. The C-index of IRS was higher than that of many developed signatures for LUAD and clinical stage. Low risk score indicated had a lower tumor escape score, lower TIDE score, higher TMB score and higher CTLA4&PD1 immunophenoscore, suggesting a better immunotherapy response. knockdown of PVRL1 suppressed tumor cell proliferation and colony formation by regulating PD-L1 expression.Conclusion: Our study developed a novel IRS for LUAD patients, which served as an indicator for predicting the prognosis and immunotherapy response.

Keywords: immune escape, machine learning, Lung Adenocarcinoma, Prognostic signature, Immunotherapy

Received: 03 May 2025; Accepted: 09 Jun 2025.

Copyright: © 2025 Ding, Huang, Wang, Shi, Ying, Shang, Wang, Zhang, Jiang and Shen. 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: Maofen Jiang, Beilun District People's Hospital of Ningbo, Ningbo, China

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.

Supplementary Material