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September 20, 2025Frontiers in BioinformaticsOpen Access

Interpretable artificial intelligence based on immunoregulation-related genes predicts prognosis and immunotherapy response in lung adenocarcinoma

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Authors

MWMinghao WangYWYu WangYLYitong Li

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Overview

Analysis predicts immune checkpoint inhibitor response in lung adenocarcinoma, indicating potential for enhanced treatment strategies.

Key Points

  • High immunoregulation-related gene expression indicates significantly better survival in lung adenocarcinoma patients.
  • TIDE scores show improved immune checkpoint inhibitor response in patients with high IRG patterns.
  • A predictive model based on identified IRGs effectively categorizes patients into risk groups, revealing significant prognosis differences.
  • The XGBoost model demonstrates high predictive performance with an AUC of 0.975, highlighting its utility in clinical settings.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68d439f4713b0b5dfea79a85https://doi.org/10.3389/fbinf.2025.1613761
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