ORIGINAL RESEARCH article
Front. Bioinform.
Sec. Genomic Analysis
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1613761
This article is part of the Research TopicAI in Integrative BioinformaticsView all 3 articles
Interpretable Artificial Intelligence Based on Immunoregulation-Related Genes Predicts Prognosis and Immunotherapy Response in Lung Adenocarcinoma
Provisionally accepted
- 1China Medical University, Shenyang, China
- 2Center for National Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, Beijing Municipality, China
- 3Department of Radiotherapy, The First Hospital of China Medical University., Shenyang, China
- 4Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer, and its benefit from immune checkpoint inhibitors (ICIs) is controversial, especially for patients without driver gene mutations. The potential of immunoregulation-related genes (IRGs) in predicting the prognosis of LUAD and the efficacy of immunotherapy becomes emerging. The GEO LUAD cohort was divided into two clusters based on IRG expression, with significantly better survival outcomes and immune cell infiltration in the IRG-high group compared to the IRG-low group. TIDE scores indicated that the group with high IRG pattern showed a better response to ICI treatment. Then, we developed an IRG index (IRGI) model based on identified 2 key IRGs, GREM1 and PLAU, and IRGI effectively divided patients into high-risk and low-risk groups, revealing significant differences in prognosis, mutational profiles, and immune cell infiltration in the TME between two groups. Subsequently, the interpretable XBGoost machine learning model established based on IRGs could further improve the predictive performance (AUC = 0.975), and SHAP analysis demonstrated that GREM1 had the greatest impact on the overall prediction. IRGs play a crucial role in shaping the diversity and complexity of TME cell infiltration, which may provide valuable guidance for ICI treatment decisions for LUAD patients. Additionally, qRT-PCR was employed to validate the expression levels of genes involved in the signature within the LAUD cell line.
Keywords: Lung Adenocarcinoma, Immunotherapy efficacy, Risk model, Single-Cell Analysis, machine learning, prognosis
Received: 17 Apr 2025; Accepted: 04 Sep 2025.
Copyright: © 2025 Wang, Wang, Li, Zhang, Li and Bi. 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: Nan Bi, Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
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