Immunogenic cell death (ICD) genes predict immunotherapy response and therapeutic targets in acute myeloid leukemia (AML)

被引:0
|
作者
Wen, Shuang [1 ]
Lv, Xuefeng [2 ,3 ]
Ma, Xiaohan [2 ,3 ]
Deng, Shu [2 ,3 ]
Xie, Jinming [4 ]
Yuan, Enwu [2 ,3 ]
机构
[1] Zhengzhou Univ, Affiliated Hosp 1, Reprod Ctr, Zhengzhou, Henan, Peoples R China
[2] Zhengzhou Univ, Affiliated Hosp 3, Dept Lab Med, Zhengzhou, Henan, Peoples R China
[3] Zhengzhou Key Lab Vitro Diag Hypertens Disorders P, Zhengzhou, Henan, Peoples R China
[4] Deyang Prison, Deyang, Sichuan, Peoples R China
关键词
AML; immunogenic cell death; pseudogene; prognosis; survival; immunotherapy; chemotherapy; prognostic model; CANCER; RECOMMENDATIONS;
D O I
10.3389/fgene.2024.1419819
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Introduction Numerous studies have demonstrated acute myeloid leukemia (AML) is one of the malignancies with high mortality worldwide. Immunogenic cell death (ICD) is a form of cell death that is specialised in that it triggers the body's immune response, particularly the adaptive immune response. Recent evidence has confirmed that pseudogenes are implicated in multiple human tumorigenesis and progression although lacking the function of coding protein. However, the roles of ICD-associated genes in AML remain largely unascertained.Methods TCGA-AML and GSE71014 cohorts were picked out and we combined them into a merged dataset by removing the batch effect using the sva package in the R project. A consensus clustering analysis of the ICD genes in AML was performed to define subgroups. Based on the expression of 15 prognostic-related pseudogenes, we developed a prognostic model and categorized AML samples into low and high-risk groups.Results AML was differentiated into two subgroups (C1 and C2 clusters). Most ICD-related genes were significantly up-regulated in the C2 cluster. The single sample gene set enrichment analysis (ssGSEA) revealed that the immune cell infiltration and immune checkpoint gene expression of the C2 cluster was strongly high, suggesting that the C2 population responded well to immune checkpoint blockade (ICB) therapy and had better survival. The C1 group was sensitive to chemotherapy, including Cytarabine, Midostaurin, and Doxorubicin. On the other hand, 15 ICD-related pseudogenes were identified to be associated with AML prognosis. The receiver operator curve (ROC) analysis and nomogram manifested that our prognostic model had high accuracy in predicting survival. However, the high-risk group was sensitive to ICB therapy and chemotherapy such as Methotrexate, Cytarabine, and Axitinib while the low-risk group benefited from 5-Fluorouracil, Talazoparib, and Navitoclax therapy.Discussion In summary, we defined two subgroups relying on 33 ICD-related genes and this classification exerted a decisive role in assessing immunotherapy and chemotherapy benefit. Significantly, a prognostic signature identified by critical ICD-related pseudogene was created. The pseudogene prognostic signature had a powerful performance in predicting prognosis and therapeutic efficacy, including immunotherapy and chemotherapy to AML. Our research points out novel implications of ICD in cancer prognosis and treatment approach choice.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Aclacinomycin enhances the killing effect of allogeneic NK cells on acute myeloid leukemia cells by inducing immunogenic cell death
    Ye, Yongbin
    Liu, Ning
    Zeng, Yunxin
    Guo, Ziwen
    Wang, Xiaobo
    Xu, Xiaojun
    FRONTIERS IN IMMUNOLOGY, 2025, 16
  • [32] The Unfolded Protein Response in Immunogenic Cell Death and Cancer Immunotherapy
    Rufo, Nicole
    Garg, Abhishek D.
    Agostinis, Patrizia
    TRENDS IN CANCER, 2017, 3 (09): : 643 - 658
  • [33] Tracking Response and Resistance in Acute Myeloid Leukemia through Single-Cell DNA Sequencing Helps Uncover New Therapeutic Targets
    Bruno, Samantha
    Borsi, Enrica
    Patuelli, Agnese
    Bandini, Lorenza
    Mancini, Manuela
    Forte, Dorian
    Nanni, Jacopo
    Barone, Martina
    Grassi, Alessandra
    Cristiano, Gianluca
    Venturi, Claudia
    Robustelli, Valentina
    Atzeni, Giulia
    Mosca, Cristina
    De Santis, Sara
    Monaldi, Cecilia
    Poletti, Andrea
    Terragna, Carolina
    Curti, Antonio
    Cavo, Michele
    Soverini, Simona
    Ottaviani, Emanuela
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2024, 25 (18)
  • [34] Developing a novel SARS-CoV-2 risk index to predict the prognostic and therapeutic effects in acute myeloid leukemia
    Guo, Jiaxin
    Wei, Yiyi
    Cen, Qingyan
    Chen, Jianyu
    Li, Yuhua
    HELIYON, 2023, 9 (11)
  • [35] Immune Checkpoint Inhibitors in Acute Myeloid Leukemia: Novel Combinations and Therapeutic Targets
    Stahl, Maximilian
    Goldberg, Aaron D.
    CURRENT ONCOLOGY REPORTS, 2019, 21 (04)
  • [36] Comprehensive analysis for cellular senescence-related immunogenic characteristics and immunotherapy prediction of acute myeloid leukemia
    Mao, Yan
    Xu, Jinwen
    Xu, Xuejiao
    Qiu, Jiayun
    Hu, Zhengyun
    Jiang, Feng
    Zhou, Guoping
    FRONTIERS IN PHARMACOLOGY, 2022, 13
  • [37] Proteogenomic profiling of acute myeloid leukemia to identify therapeutic targets
    Murray, Heather C.
    Sillar, Jonathan
    Chambers, Maddison
    Verrills, Nicole M.
    EXPERT REVIEW OF PROTEOMICS, 2024,
  • [38] A model based on immunogenic cell death-related genes predicts prognosis and response to immunotherapy in kidney renal clear cell carcinoma
    Dong, Pei
    Zhao, Lincong
    Zhao, Lianmei
    Zhang, Jinyan
    Lil, Gang
    Zhang, Hong
    Ma, Ming
    TRANSLATIONAL CANCER RESEARCH, 2024, 13 (01) : 249 - 267
  • [39] Establishment of an immunogenic cell death-related model for prognostic prediction and identification of therapeutic targets in endometrial carcinoma
    Liu, Zhenran
    Huang, Yue
    Zhang, Pin
    Yang, Chen
    Wang, Yujie
    Yu, Yaru
    Xiang, Huifen
    AGING-US, 2024, 16 (05): : 4920 - 4942
  • [40] An Immunogenic Cell Death-Related Classification Predicts Prognosis and Response to Immunotherapy in Head and Neck Squamous Cell Carcinoma
    Wang, Xinwen
    Wu, Shouwu
    Liu, Feng
    Ke, Dianshan
    Wang, Xinwu
    Pan, Dinglong
    Xu, Weifeng
    Zhou, Ling
    He, Weidong
    FRONTIERS IN IMMUNOLOGY, 2021, 12