研究动态
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基于机器学习的集成开发了一种免疫原性细胞死亡衍生的 lncRNA 特征,用于预测肺腺癌的预后和免疫治疗反应。

Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma.

发表日期:2024 May 22
作者: Jiazheng Sun, Hehua Guo, Siyu Zhang, Yalan Nie, Sirui Zhou, Yulan Zeng, Yalu Sun
来源: Cell Death & Disease

摘要:

越来越多的证据表明lncRNA参与免疫微环境和早期肿瘤发展的调节。免疫原性细胞死亡主要是通过释放或增加肿瘤相关抗原和肿瘤特异性抗原,暴露“危险信号”来刺激机体的免疫反应。鉴于肺腺癌免疫治疗的最新发展,我们探讨了肿瘤免疫原性细胞死亡相关lncRNA在肺腺癌中对预后和免疫治疗益处的作用,这一点尚未被揭示。该研究基于TCGA数据库和GEO数据库中的肺腺癌队列,通过多种机器学习算法开发了免疫原性细胞死亡指数特征,然后验证了该特征对肺腺癌患者的预后和免疫治疗益处,具有比传统肺腺癌患者更稳定的性能。具有已发表的预测预后特征,并证明了在多种癌症的多个队列中受益于免疫治疗的预测价值,并指导了化疗药物的使用。© 2024。作者。
Accumulating evidence demonstrates that lncRNAs are involved in the regulation of the immune microenvironment and early tumor development. Immunogenic cell death occurs mainly through the release or increase of tumor-associated antigen and tumor-specific antigen, exposing "danger signals" to stimulate the body's immune response. Given the recent development of immunotherapy in lung adenocarcinoma, we explored the role of tumor immunogenic cell death-related lncRNAs in lung adenocarcinoma for prognosis and immunotherapy benefit, which has never been uncovered yet. Based on the lung adenocarcinoma cohorts from the TCGA database and GEO database, the study developed the immunogenic cell death index signature by several machine learning algorithms and then validated the signature for prognosis and immunotherapy benefit of lung adenocarcinoma patients, which had a more stable performance compared with published signatures in predicting the prognosis, and demonstrated predictive value for benefiting from immunotherapy in multiple cohorts of multiple cancers, and also guided the utilization of chemotherapy drugs.© 2024. The Author(s).