Neutrophil-Related Genes Predict Prognosis and Contribute to Immunosuppression in Acute Myeloid Leukemia.

IF 4.9 Q2 ONCOLOGY

Blood and Lymphatic Cancer-Targets and Therapy Pub Date : 2025-08-18 eCollection Date: 2025-01-01 DOI:10.2147/BLCTT.S529074

Di Zhang, Yongjian Li, Tingting Liu, Xiaomin Liu, Jingru Zhang

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引用次数: 0

Abstract

Introduction: Acute myeloid leukemia (AML) prognosis remains challenging due to limited biomarkers integrating tumor microenvironment (TME) dynamics. Neutrophils, key mediators of immune regulation, exhibit dual roles in cancer progression, yet their prognostic significance in AML is poorly defined. This study aimed to construct a neutrophil-related gene signature for AML risk stratification and explore its clinical and immunological implications.

Methods: Utilizing transcriptomic and clinical data from TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus), and OHSU cohorts (n=1537), we identified 148 neutrophil-related genes through literature mining. Prognostic genes were selected via univariate Cox regression and LASSO regression (R packages: survival, glmnet). A 5-gene model (CSF3R, BRAF, FFAR2, CD300A, CD37) was validated across internal (TCGA) and external cohorts (GSE10358, GSE14468, OHSU). Immune profiling, drug sensitivity analysis (GDSC database), and TIDE scoring were performed to assess immunotherapy relevance.

Results: The neutrophil-based model stratified AML patients into high- and low-risk groups with distinct overall survival (OS, p<0.0001 in TCGA). Multivariate Cox analysis confirmed its independence from age, FLT3, and TP53 mutations (HR=2.14, p=0.015). CD37 emerged as the strongest prognostic marker (AUC 5-year=0.680, p=0.0026), correlating with immunosuppressive TME features: elevated myeloid-derived suppressor cells (MDSCs, p<0.01), Treg infiltration (p <0.05), and upregulated immune checkpoints (PD1, CTLA4, LAG3; p<0.001). High CD37 expression predicted immunotherapy responsiveness (TIDE score, p=0.004) and interacted with 146 potential therapeutic agents (eg, BCL2 inhibitors).

Discussion: This study advances a novel 5-gene prognostic model integrating neutrophil biology into AML risk stratification. CD37, a key regulator of immune evasion, serves as a dual biomarker for prognosis and immunotherapy prediction. While validated across multiple cohorts, experimental studies are warranted to unravel CD37's mechanistic role. Our findings highlight the potential of neutrophil-centric biomarkers in guiding personalized AML therapy.

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中性粒细胞相关基因预测急性髓系白血病的预后并参与免疫抑制。

由于整合肿瘤微环境（TME）动态的生物标志物有限，急性髓性白血病（AML）的预后仍然具有挑战性。中性粒细胞是免疫调节的关键介质，在癌症进展中表现出双重作用，但其在AML中的预后意义尚不明确。本研究旨在构建AML风险分层的中性粒细胞相关基因标记，并探讨其临床和免疫学意义。方法：利用TCGA （The Cancer Genome Atlas）、GEO （Gene Expression Omnibus）和OHSU队列（n=1537）的转录组学和临床数据，通过文献挖掘鉴定出148个中性粒细胞相关基因。通过单因素Cox回归和LASSO回归选择预后基因（R包：survival， glmnet）。5基因模型（CSF3R， BRAF, FFAR2, CD300A, CD37）在内部（TCGA）和外部队列（GSE10358, GSE14468， OHSU）中进行验证。通过免疫谱分析、药物敏感性分析（GDSC数据库）和TIDE评分来评估免疫治疗的相关性。结果：基于中性粒细胞的模型将AML患者分为高风险和低风险组，总生存率不同（OS, pp=0.015）。CD37成为最强的预后标志物（5年AUC =0.680, p=0.0026），与免疫抑制TME特征相关：髓源性抑制细胞（MDSCs, pp pp=0.004）升高，并与146种潜在的治疗药物（如BCL2抑制剂）相互作用。讨论：本研究提出了一种新的5基因预后模型，将中性粒细胞生物学纳入AML风险分层。CD37是免疫逃避的关键调节因子，作为预后和免疫治疗预测的双重生物标志物。虽然在多个队列中得到验证，但实验研究有必要揭示CD37的机制作用。我们的发现强调了以中性粒细胞为中心的生物标志物在指导个性化AML治疗中的潜力。

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来源期刊

Blood and Lymphatic Cancer-Targets and Therapy ONCOLOGY-

自引率

7.10%

发文量

审稿时长

16 weeks

期刊介绍： Blood and Lymphatic Cancer: Targets and Therapy is an international, peer reviewed, open access journal focusing on blood and lymphatic cancer research, identification of therapeutic targets, and the optimal use of preventative and integrated treatment interventions to achieve improved outcomes, enhanced survival, and quality of life for the cancer patient. Specific topics covered in the journal include: Epidemiology, detection and screening Cellular research and biomarkers Identification of biotargets and agents with novel mechanisms of action Optimal clinical use of existing anticancer agents, including combination therapies Radiation, surgery, bone marrow transplantation Palliative care Patient adherence, quality of life, satisfaction Health economic evaluations.