Wang Minghui, Xian Huajian, Xia Xiaoli, Zhang Wenjie, Huang Zixuan, Lu Chaoqun, Zheng Yuling, Wang Yixin, Xie Shufeng, Pan Renyao, Yu YaoYifu, Wang Ruiheng, Zheng Huijian, Huang Guorui, Liu Han
Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
J Transl Med. 2025 May 21;23(1):566. doi: 10.1186/s12967-025-06615-y.
Acute myeloid leukemia (AML) is a highly heterogeneous malignancy, presenting significant challenges in accurately predicting patient prognosis. Dysregulation of endoplasmic reticulum (ER) stress and resistance to programmed cell death (PCD) are hallmarks of AML cells. However, the prognostic significance of the interplay between ER stress and cell death pathways in AML remains largely unexplored.
We analyzed RNA sequencing and clinical data from 887 AML patients across 4 cohorts to develop an ER stress-related cell death index (ERCDI) using 10 machine-learning algorithms with 117 unique combinations. Survival and time-dependent Receiver Operating Characteristic Curve (ROC) analyses were performed to assess the model's efficacy. Clinical characteristics, the tumor immune microenvironment, and drug sensitivity differences between the high- and low-risk groups were also analyzed. The CMap database was used to identify potential therapeutic drugs. In vitro and in vivo experiments, including CCK-8, colony formation, flow cytometry, Transwell assays, and xenograft mouse models, were conducted to evaluate the effects of the target genes and candidate drugs.
The ERCDI demonstrated strong prognostic and predictive performance for prognosis in AML patients. Furthermore, the ERCDI effectively predicted immunotherapy and chemotherapy outcomes and was associated with the immune features of the different risk groups. DNA damage-inducible transcript 4 protein (DDIT4), a key gene associated with ERCDI, is related to poor prognosis in AML patients with high expression. Additionally, the knockdown of DDIT4 significantly inhibited AML cell proliferation, induced cell apoptosis, and promoted cell cycle arrest. Chaetocin was subsequently identified as a candidate compound for AML treatment. Subsequent experiments suggested that combining chaetocin and venetoclax is a potentially promising therapeutic strategy for AML.
The ERCDI provides personalized risk assessment and treatment recommendations for individual AML patients. The combined use of chaetocin and venetoclax can potentially be repurposed for AML therapy.
急性髓系白血病(AML)是一种高度异质性的恶性肿瘤,在准确预测患者预后方面面临重大挑战。内质网(ER)应激失调和对程序性细胞死亡(PCD)的抗性是AML细胞的标志。然而,ER应激与AML细胞死亡途径之间相互作用的预后意义在很大程度上仍未得到探索。
我们分析了来自4个队列的887例AML患者的RNA测序和临床数据,使用10种机器学习算法和117种独特组合开发了内质网应激相关细胞死亡指数(ERCDI)。进行生存分析和时间依赖性受试者操作特征曲线(ROC)分析以评估模型的有效性。还分析了高风险组和低风险组之间的临床特征、肿瘤免疫微环境和药物敏感性差异。使用CMap数据库识别潜在的治疗药物。进行了体外和体内实验,包括CCK-8、集落形成、流式细胞术、Transwell实验和异种移植小鼠模型,以评估靶基因和候选药物的作用。
ERCDI对AML患者的预后显示出强大的预后和预测性能。此外,ERCDI有效地预测了免疫治疗和化疗结果,并与不同风险组的免疫特征相关。DNA损伤诱导转录4蛋白(DDIT4)是与ERCDI相关的关键基因,与高表达的AML患者预后不良有关。此外,敲低DDIT4可显著抑制AML细胞增殖,诱导细胞凋亡,并促进细胞周期停滞。随后鉴定出chaetocin作为AML治疗的候选化合物。后续实验表明,将chaetocin和维奈克拉联合使用是一种潜在的有前景的AML治疗策略。
ERCDI为个体AML患者提供个性化的风险评估和治疗建议。chaetocin和维奈克拉的联合使用可能有潜力重新用于AML治疗。