The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. However, there is currently a lack of reliable signatures for identifying patients who would benefit from immunotherapy. We conducted a comparative analysis of two immunotherapy cohorts (OAK and POPLAR) and utilized single‐factor COX regression to identify genes that significantly impact the prognosis of LUAD. Based on the TCGA‐LUAD dataset, we employed a combination of 101 machine learning algorithms to construct a model and selected the optimal model. The model was validated on five GEO datasets and compared with 144 previously published signatures to assess its performance. Subsequently, we explored the underlying biological mechanisms through tumor mutation burden analysis, enrichment analysis, and immune infiltration analysis. An immunotherapy prognostic prediction signature (IPPS) was constructed based on 13 genes, showing robust performance in the TCGA‐LUAD dataset. IPPS exhibited consistent predictive accuracy in the validation cohorts. Compared to 144 previously published signatures, IPPS consistently ranked among the top in terms of C‐index values. Further exploration revealed differences between high and low‐IPPS groups in terms of tumor mutation burden, pathway enrichment, and immune infiltration. IPPS demonstrates strong predictive capabilities for the prognosis of LUAD patients, offering the potential to identify suitable candidates for immunotherapy and contribute to precision treatment strategies for LUAD.

中文翻译：

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推进肺腺癌的预测标记：基于机器学习的免疫治疗预后预测特征

肺腺癌（LUAD）的预后通常较差。免疫疗法已成为一种有前途的治疗方式，显示出显着延长 LUAD 患者总体生存期的巨大潜力。然而，目前缺乏可靠的特征来识别将从免疫治疗中受益的患者。我们对两个免疫治疗队列（OAK 和 POPLAR）进行了比较分析，并利用单因素 COX 回归来识别显着影响 LUAD 预后的基因。基于TCGA-LUAD数据集，我们采用了101种机器学习算法的组合来构建模型并选择最佳模型。该模型在 5 个 GEO 数据集上进行了验证，并与之前发布的 144 个签名进行了比较，以评估其性能。随后，我们通过肿瘤突变负荷分析、富集分析和免疫浸润分析探索了潜在的生物学机制。基于 13 个基因构建了免疫治疗预后预测特征 (IPPS)，在 TCGA-LUAD 数据集中显示出稳健的性能。 IPPS 在验证队列中表现出一致的预测准确性。与之前发布的 144 个签名相比，IPPS 在 C 指数值方面始终名列前茅。进一步的探索揭示了高 IPPS 组和低 IPPS 组在肿瘤突变负荷、通路富集和免疫浸润方面的差异。 IPPS 对 LUAD 患者的预后表现出强大的预测能力，有可能识别出合适的免疫治疗候选者，并有助于制定 LUAD 的精准治疗策略。