Zhong Fangmin, Yao Fangyi, Wang Zihao, Liu Jing, Huang Bo, Wang Xiaozhong
Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Front Immunol. 2025 Feb 24;16:1558496. doi: 10.3389/fimmu.2025.1558496. eCollection 2025.
Neutrophil extracellular traps (NETs) play pivotal roles in various pathological processes. The formation of NETs is impaired in acute myeloid leukemia (AML), which can result in immunodeficiency and increased susceptibility to infection.
The gene set variation analysis (GSVA) algorithm was employed for the calculation of NET score, while the consensus clustering algorithm was utilized to identify molecular subtypes. Weighted gene coexpression network analysis (WGCNA) revealed potential genes and biological pathways associated with NETs, and a total of 10 machine learning algorithms were applied to construct the optimal prognostic model.
Through the analysis of multiomics data, we identified two molecular subtypes with high and low NET scores. The low-NET score subgroup exhibited increased infiltration of immune effector cells. Conversely, the high-NET score subtype presented an abundance of monocytes and M2 macrophages, accompanied by elevated expression levels of immune checkpoint genes. These findings suggest that a pronounced immunosuppressive effect is associated with a significantly worse prognosis for this subtype. The optimal risk score model was selected by employing the C-index as the criterion on the basis of training 10 machine learning algorithms on 9 multicenter AML cohorts. Survival analysis confirmed that patients with high-risk scores had considerably poorer prognoses than those with lower scores. Receiver operating characteristic (ROC) curve and Cox regression analyses further validated the strong independent prognostic value of the risk score model. The nomogram, which was constructed by integrating the risk score model and clinicopathological factors, demonstrated high accuracy in predicting the overall survival of AML patients. Moreover, patients with refractory or chemotherapy-unresponsive AML had significantly higher risk scores. By analyzing drug therapy data from AML cells, we identified a subset of drugs that demonstrated increased sensitivity in the high-risk score group. Additionally, patients with a high risk score were also predicted to exhibit a favorable response to anti-PD-1 therapy, suggesting that these individuals may derive greater benefits from immunotherapy.
The NET-related signature, derived from a combination of diverse machine learning algorithms, has promising potential as a valuable tool for prognostic prediction, preventive measures, and personalized medicine in patients with AML.
中性粒细胞胞外陷阱（NETs）在各种病理过程中起关键作用。急性髓系白血病（AML）中NETs的形成受损，这可能导致免疫缺陷和感染易感性增加。
采用基因集变异分析（GSVA）算法计算NET评分，同时利用共识聚类算法识别分子亚型。加权基因共表达网络分析（WGCNA）揭示了与NETs相关的潜在基因和生物学途径，并应用总共10种机器学习算法构建最佳预后模型。
通过对多组学数据的分析，我们确定了NET评分高和低的两种分子亚型。NET评分低的亚组显示免疫效应细胞浸润增加。相反，NET评分高的亚型有大量单核细胞和M2巨噬细胞，同时免疫检查点基因表达水平升高。这些发现表明，明显的免疫抑制作用与该亚型明显更差的预后相关。基于在9个多中心AML队列上训练10种机器学习算法，以C指数为标准选择了最佳风险评分模型。生存分析证实，高风险评分患者的预后明显比低风险评分患者差。受试者工作特征（ROC）曲线和Cox回归分析进一步验证了风险评分模型强大的独立预后价值。通过整合风险评分模型和临床病理因素构建的列线图在预测AML患者总生存方面显示出高准确性。此外，难治性或化疗无反应的AML患者的风险评分明显更高。通过分析AML细胞的药物治疗数据，我们确定了一组在高风险评分组中显示出更高敏感性的药物。此外，高风险评分患者也被预测对抗程序性死亡蛋白1（PD-1）治疗有良好反应，这表明这些个体可能从免疫治疗中获得更大益处。
源自多种机器学习算法组合的NET相关特征，作为AML患者预后预测、预防措施和个性化医疗的有价值工具具有广阔潜力。
