肺腺癌（LUAD）是呼吸系统的恶性肿瘤。目前治疗方法的疗效差异很大，个体化治疗显而易见。因此，寻找预测治疗预后的生物标志物，并为制定治疗方案提供参考和指导是迫切的。在过去几十年中，癌症免疫治疗取得了明显的进展，并在LUAD上产生了显著的效果。免疫原性细胞死亡（ICD）可以重塑肿瘤的免疫微环境，有助于免疫治疗。因此，探索ICD生物标志物以构建预后模型可能有助于个体化治疗。我们使用肺腺癌（LUAD）数据集鉴定与ICD相关的差异表达基因（DEGs）。然后，将这些DEGs聚类并分成亚组。我们还在不同维度上进行了方差分析。此外，通过LASSO Cox回归分析建立并验证了预后模型。该模型的风险评分用于通过生存分析评估预后差异。我们还预测了不同治疗方法的治疗预后。将LUAD样本分为两个亚组。ICD高亚组与免疫热表型相关，对免疫治疗更敏感。预后模型是基于六个与ICD相关的DEG构建的。我们发现高风险评分患者对免疫治疗的反应更好。ICD预后模型被验证为评估个体LUAD患者ICD亚型的独立因素，可能有助于更有效的治疗。© 2023年 Springer Nature Limited。

Lung adenocarcinoma (LUAD) is a malignant tumor in the respiratory system. The efficacy of current treatment modalities varies greatly, and individualization is evident. Therefore, finding biomarkers for predicting treatment prognosis and providing reference and guidance for formulating treatment options is urgent. Cancer immunotherapy has made distinct progress in the past decades and has a significant effect on LUAD. Immunogenic Cell Death (ICD) can reshape the tumor's immune microenvironment, contributing to immunotherapy. Thus, exploring ICD biomarkers to construct a prognostic model might help individualized treatments. We used a lung adenocarcinoma (LUAD) dataset to identify ICD-related differentially expressed genes (DEGs). Then, these DEGs were clustered and divided into subgroups. We also performed variance analysis in different dimensions. Further, we established and validated a prognostic model by LASSO Cox regression analysis. The risk score in this model was used to evaluate prognostic differences by survival analysis. The treatment prognosis of various therapies were also predicted. LUAD samples were divided into two subgroups. The ICD-high subgroup was related to an immune-hot phenotype more sensitive to immunotherapy. The prognostic model was constructed based on six ICD-related DEGs. We found that high-risk score patients responded better to immunotherapy. The ICD prognostic model was validated as a standalone factor to evaluate the ICD subtype of individual LUAD patients, which might contribute to more effective therapies.© 2023. Springer Nature Limited.