Optimal Combination of Immune Checkpoint and Senescence Molecule Predicts Adverse Outcomes in Patients with Acute Myeloid Leukemia.

ANNALS OF MEDICINE(2023)

引用 4|浏览10
摘要
Background High expression of immune checkpoints (ICs) and senescence molecules (SMs) contributes to T cell dysfunction, tumor escape, and progression, but systematic evaluation of them in co-expression patterns and prognosis in acute myeloid leukemia (AML) was lacking. Methods Three publicly available datasets (TCGA, Beat-AML, and GSE71014) were first used to explore the effect of IC and SM combinations on prognosis and the immune microenvironment in AML, and bone marrow samples from 68 AML patients from our clinical center (GZFPH) was further used to validate the findings. Results High expression of CD276, Bcl2-associated athanogene 3 (BAG3), and SRC was associated with poor overall survival (OS) of AML patients. CD276/BAG3/SRC combination, standard European Leukemia Net (ELN) risk stratification, age, and French-American-British (FAB) subtype were used to construct a nomogram model. Interestingly, the new risk stratification derived from the nomogram was better than the standard ELN risk stratification in predicting the prognosis for AML. A weighted combination of CD276 and BAG3/SRC positively corrected with TP53 mutation, p53 pathway, CD8+ T cells, activated memory CD4+ T cells, T-cell senescence score, and Tumor Immune Dysfunction and Exclusion (TIDE) score estimated by T-cell dysfunction. Conclusion High expression of ICs and SMs was associated with poor OS of AML patients. The co-expression patterns of CD276 and BAG3/SRC might be potential biomarkers for risk stratification and designing combinational immuno-targeted therapy in AML. Key Messages High expression of CD276, BAG3, and SRC was associated with poor overall survival of AML patients. The co-expression patterns of CD276 and BAG3/SRC might be potential biomarkers for risk stratification and designing combinational immuno-targeted therapy in AML.
更多
查看译文
关键词
Prognosis,immune checkpoint,senescence,risk stratification,acute myeloid leukemia
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
0
您的评分 :

暂无评分

数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn