我们从癌症基因组图谱（TCGA）数据库下载了LUAD患者的mRNA表达谱和相应的临床数据，并使用最小绝对收缩和选择算子Cox回归模型在TCGA队列中构建了一个多基因标记，该标记通过来自GEO队列的患者数据进行了验证。结果显示，正常和肿瘤组织之间存在120个与死亡凋亡相关的基因的表达水平差异。通过单变量Cox回归分析构建了一个由8个基因（CYLD，FADD，H2AX，RBCK1，PPIA，PPID，VDAC1和VDAC2）组成的标记，将患者分为两个风险组。TCGA和GEO队列中高风险组的患者总体生存率明显低于低风险组的患者，表明该标记具有良好的预测效果。时间-ROC曲线显示该标记在TCGA和GEO队列中具有可靠的预测作用。富集分析显示风险亚组中的差异基因与肿瘤免疫和抗肿瘤药物敏感性有关。然后我们构建了一个mRNA-miRNA-lncRNA调控网络，确定了lncRNA AL590666.2/let-7c-5p/PPIA作为LUAD的调控轴。实时定量聚合酶链反应（RT-qPCR）被用于验证8个基因标记的表达。总之，与死亡凋亡相关的基因是预测LUAD预后和潜在治疗靶点的重要因素。© 2023. Springer Nature Limited.

We downloaded the mRNA expression profiles of patients with LUAD and corresponding clinical data from The Cancer Genome Atlas (TCGA) database and used the Least Absolute Shrinkage and Selection Operator Cox regression model to construct a multigene signature in the TCGA cohort, which was validated with patient data from the GEO cohort. Results showed differences in the expression levels of 120 necroptosis-related genes between normal and tumor tissues. An eight-gene signature (CYLD, FADD, H2AX, RBCK1, PPIA, PPID, VDAC1, and VDAC2) was constructed through univariate Cox regression, and patients were divided into two risk groups. The overall survival of patients in the high-risk group was significantly lower than of the patients in the low-risk group in the TCGA and GEO cohorts, indicating that the signature has a good predictive effect. The time-ROC curves revealed that the signature had a reliable predictive role in both the TCGA and GEO cohorts. Enrichment analysis showed that differential genes in the risk subgroups were associated with tumor immunity and antitumor drug sensitivity. We then constructed an mRNA-miRNA-lncRNA regulatory network, which identified lncRNA AL590666. 2/let-7c-5p/PPIA as a regulatory axis for LUAD. Real-time quantitative PCR (RT-qPCR) was used to validate the expression of the 8-gene signature. In conclusion, necroptosis-related genes are important factors for predicting the prognosis of LUAD and potential therapeutic targets.© 2023. Springer Nature Limited.