铁死亡被确认为一种新型编程性细胞死亡方式，对肺腺癌的发展产生了重要影响。然而，目前还没有一套基于铁死亡的治疗靶点。本研究旨在运用机器学习方法确定肺腺癌中铁死亡的调节因子。研究对318个肺腺癌样本进行调查，确定了肺腺癌中三个铁死亡分子表型，然后采用Boruta维度约简结合主成分分析来测量患者的铁死亡调节得分（FRS）。我们还提出了DeepFerr，一种使用11个铁死亡调节因子的转录组图谱来预测肺腺癌中铁死亡的深度学习神经网络模型。采用LASSO、SVM-RFE和弹性网络来研究差异化的铁死亡调节因子，8个关键的铁死亡调节因子具有相当的铁死亡预测能力。研究表明，RRM2和AURKA是铁死亡的关键抑制因子，RRM2和AURKA的耗竭导致H358细胞中铁死亡的增加，它们不仅作为促增殖因子阻碍免疫浸润在LUAD中，还对抗PD1疗法和化疗至关重要。总之，本研究证实了RRM2和AURKA在铁死亡中的调节作用，可能有助于预测LUAD患者的个性化治疗。版权所有 © 2023 Elsevier B.V. 发布。

Ferroptosis has been identified as a novel type of programmed cell death that has a major effect on the development of lung adenocarcinoma. Nevertheless, there has yet to be a clear set of therapeutic targets based on ferroptosis. This study seeks to employ machine learning methods to determine the regulators of ferroptosis in LUAD. 318 LUAD samples were investigated to determine three ferroptosis molecular phenotypes in LUAD, and then Boruta dimensionality reduction combined with principal component analysis was used to measure the ferroptosis regulation score (FRS) of patients. We additionally presented DeepFerr, a deep learning neural network model, which used the transcriptome map of 11 ferroptosis regulators to predict ferroptosis in LUAD. LASSO, SVM-RFE and elastic net were used to dissect the differential ferroptosis regulators, and the eight pivotal ferroptosis regulators have considerable ferroptosis prediction ability. It was established that RRM2 and AURKA are key suppressors of ferroptosis, and the depletion of RRM2 and AURKA caused an increase in ferroptosis in H358 cells. In addition, not only did they act as pro-proliferative factors that hindered immune infiltration in LUAD, but they were also essential for anti-PD1 therapy and chemotherapy. In summary, this research confirms the regulatory role of RRM2 and AURKA in ferroptosis, and could be useful in predicting individualized treatment for patients with LUAD.Copyright © 2023. Published by Elsevier B.V.