免疫原性细胞死亡（ICD）已被证明可以激活T细胞杀死肿瘤细胞，这与肿瘤的发生发展密切相关，长链非编码RNA（lncRNA）也参与其中。然而，尚不清楚 ICD 相关 lncRNA 是否与肺腺癌 (LUAD) 的发生有关。我们从 GeneCards 下载了 ICD 相关基因，并从癌症基因组图谱 (TCGA) 下载了 LUAD 患者的转录组统计数据，随后开发并验证了预测模型。成功的模型与其他临床特征一起构建了用于预测患者生存的列线图。为了进一步研究肿瘤作用机制并指导治疗，我们进行了富集分析、肿瘤微环境分析、体细胞突变分析、药物敏感性分析和实时定量聚合酶链反应（RT-qPCR）分析。选择九个具有显着预后相关性的 ICD 相关 lncRNA 进行模型构建。生存分析表明，高风险组的总生存期明显短于低风险组（P<0.001）。该模型可以预测所有临床亚组的预后。 Cox回归分析进一步支持了模型的独立预测能力。最终，创建了取决于阶段和风险评分的列线图，并且显示出比没有风险评分的列线图更好的预测性能。通过富集分析，发现高危组的富集通路主要与代谢和DNA复制相关。肿瘤微环境分析表明，高危组的免疫细胞浓度较低。体细胞突变分析显示，高危组肿瘤突变较多（P = 0.00018）。高危组的肿瘤免疫功能障碍和排除评分对免疫治疗表现出更高的敏感性（P<0.001）。药物敏感性分析表明预测模型也可以应用于化疗药物的选择。 RT-qPCR 分析还验证了基于 9 个 ICD 相关 lncRNA 构建的模型的准确性。基于9种ICD相关lncRNA构建的预后模型在评估预后和指导临床治疗方面显示出良好的应用价值。© 2023。作者。

Immunogenic cell death (ICD) has been demonstrated to activate T cells to kill tumor cells, which is closely related to tumor development, and long noncoding RNAs (lncRNAs) are also involved. However, it is not known whether ICD-related lncRNAs are associated with the development of lung adenocarcinoma (LUAD). We downloaded ICD-related genes from GeneCards and the transcriptome statistics of LUAD patients from The Cancer Genome Atlas (TCGA) and subsequently developed and verified a predictive model. A successful model was used together with other clinical features to construct a nomogram for predicting patient survival. To further study the mechanism of tumor action and to guide therapy, we performed enrichment analysis, tumor microenvironment analysis, somatic mutation analysis, drug sensitivity analysis and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Nine ICD-related lncRNAs with significant prognostic relevance were selected for model construction. Survival analysis demonstrated that overall survival was substantially shorter in the high-risk group than in the low-risk group (P < 0.001). This model was predictive of prognosis across all clinical subgroups. Cox regression analysis further supported the independent prediction ability of the model. Ultimately, a nomogram depending on stage and risk score was created and showed a better predictive performance than the nomogram without the risk score. Through enrichment analysis, the enriched pathways in the high-risk group were found to be primarily associated with metabolism and DNA replication. Tumor microenvironment analysis suggested that the immune cell concentration was lower in the high-risk group. Somatic mutation analysis revealed that the high-risk group contained more tumor mutations (P = 0.00018). Tumor immune dysfunction and exclusion scores exhibited greater sensitivity to immunotherapy in the high-risk group (P < 0.001). Drug sensitivity analysis suggested that the predictive model can also be applied to the choice of chemotherapy drugs. RT-qPCR analysis also validated the accuracy of the constructed model based on nine ICD-related lncRNAs. The prognostic model constructed based on the nine ICD-related lncRNAs showed good application value in assessing prognosis and guiding clinical therapy.© 2023. The Author(s).