肺腺癌（LUAD）仍是最常见和致命的恶性肿瘤之一，预后不良。程序化细胞死亡（PCD）是一种在进化中保守的细胞自杀过程，调控肿瘤发生、发展和转移。然而，目前对于PCD在LUAD中的作用的综合分析还不可用。我们对LUAD中与PCD相关基因（PCDRGs）进行了多组学变异的分析。我们使用10种机器学习算法（101种组合）进行交叉验证，通过PCDRGs的表达谱合成开发和验证一种最优的细胞死亡评分（CDS）模型。患者根据其中位数CDS值被分为高和低CDS组。接下来，我们比较了两组患者的基因组学、生物功能和肿瘤微环境的差异。此外，我们评估了CDS对免疫治疗队列患者的治疗反应的预测能力。最后，对CDS中的关键基因进行了功能验证。 我们基于四个PCDRGs构建了CDS，可以有效而一致地分层LUAD患者（高CDS的患者预后不良）。我们的CDS表现优于之前发表的77个LUAD特征。结果显示高CDS的患者观察到显著的遗传变异，如突变计数、TMB和CNV。此外，我们观察到CDS与免疫细胞浸润、微卫星不稳定性、SNV新抗原的关联。低CDS患者的免疫状态更活跃。此外，CDS可以可靠地预测多个免疫治疗队列中的治疗反应。体外实验证明，LUAD细胞中高DNA损伤诱导转录因子4（DDIT4）表达介导了促肿瘤效应。 使用机器学习基于PCDRGs构建了CDS。该模型可以准确预测患者的预后和对治疗的反应。这些结果为临床管理提供了新的有前景的工具，并有助于设计LUAD患者的个体化治疗策略。 版权所有 © 2023 张、王、陈、夏和黄。

lung adenocarcinoma (LUAD) remains one of the most common and lethal malignancies with poor prognosis. Programmed cell death (PCD) is an evolutionarily conserved cell suicide process that regulates tumorigenesis, progression, and metastasis of cancer cells. However, a comprehensive analysis of the role of PCD in LUAD is still unavailable.We analyzed multi-omic variations in PCD-related genes (PCDRGs) for LUAD. We used cross-validation of 10 machine learning algorithms (101 combinations) to synthetically develop and validate an optimal prognostic cell death score (CDS) model based on the PCDRGs expression profile. Patients were classified based on their median CDS values into the high and low-CDS groups. Next, we compared the differences in the genomics, biological functions, and tumor microenvironment of patients between both groups. In addition, we assessed the ability of CDS for predicting the response of patients from the immunotherapy cohort to immunotherapy. Finally, functional validation of key genes in CDS was performed.We constructed CDS based on four PCDRGs, which could effectively and consistently stratify patients with LUAD (patients with high CDS had poor prognoses). The performance of our CDS was superior compared to 77 LUAD signatures that have been previously published. The results revealed significant genetic alterations like mutation count, TMB, and CNV were observed in patients with high CDS. Furthermore, we observed an association of CDS with immune cell infiltration, microsatellite instability, SNV neoantigens. The immune status of patients with low CDS was more active. In addition, CDS could be reliable to predict therapeutic response in multiple immunotherapy cohorts. In vitro experiments revealed that high DNA damage inducible transcript 4 (DDIT4) expression in LUAD cells mediated protumor effects.CDS was constructed based on PCDRGs using machine learning. This model could accurately predict patients' prognoses and their responses to therapy. These results provide new promising tools for clinical management and aid in designing personalized treatment strategies for patients with LUAD.Copyright © 2023 Zhang, Wang, Chen, Xia and Huang.