弥漫性大B细胞淋巴瘤（DLBCL）是一种侵袭性恶性肿瘤。中性粒细胞外网（NETs）是肿瘤微环境中用于捕获病原体的结构，影响着DLBCL的进展。然而，与DLBCL相关联的NET相关基因（NRGs）的预测功能却鲜有研究关注。本研究旨在探究NRGs与DLBCL预后的相互作用以及它们与免疫微环境的可能关联。 本研究从基因表达文库下载了DLBCL患者的基因表达和临床数据。通过人工收集文献，我们确定了148个NRGs。其中GSE10846数据集（n = 400, GPL570）被用作训练数据集，并按7:3比例分为训练集和测试集。我们利用单变量Cox回归分析确定了与总生存期（OS）相关的NETs，并使用最小绝对收缩选择算子评估了NRGs的预测效能。我们使用Kaplan-Meier绘图来可视化生存函数。我们还使用受试者工作特征曲线（ROC曲线）评估了NRG特征的预后预测能力。我们利用多变量逻辑回归和Cox比例风险回归模型构建了包含患者临床信息和预后得分的评分卡。 我们确定了36个显著影响患者总生存期（OS）的NRGs。我们发现有8个NRGs（PARVB，LYZ，PPARGC1A，HIF1A，SPP1，CDH1，S100A9和CXCL2）对患者的生存具有良好的预测能力。对于1年、3年和5年的生存率，所得到的受试者工作特征曲线下面积分别为0.8、0.82和0.79。在训练集中，高NRG风险组的患者显示出较差的预后（p < 0.0001），这一结果在两个外部数据集（GSE11318和GSE34171）中得到验证。评分卡的校准曲线表明其具有优秀的预测能力。此外，体外定量实时PCR（qPCR）的结果显示DLBCL组中CXCL2，LYZ和PARVB的mRNA表达水平明显较高。 我们基于NRGs开发了一个预测DLBCL患者预后的遗传风险模型，有助于为这些患者选择治疗药物。 © 2023. BioMed Central Ltd., part of Springer Nature.

Diffuse large B-cell lymphoma (DLBCL) is an aggressive malignancy. Neutrophil extracellular traps (NETs) are pathogen-trapping structures in the tumor microenvironment that affect DLBCL progression. However, the predictive function of NET-related genes (NRGs) in DLBCL has received little attention. This study aimed to investigate the interaction between NRGs and the prognosis of DLBCL as well as their possible association with the immunological microenvironment.The gene expression and clinical data of patients with DLBCL were downloaded from the Gene Expression Omnibus database. We identified 148 NRGs through the manual collection of literature. GSE10846 (n = 400, GPL570) was used as the training dataset and divided into training and testing sets in a 7:3 ratio. Univariate Cox regression analysis was used to identify overall survival (OS)-related NETs, and the least absolute shrinkage and selection operator was used to evaluate the predictive efficacy of the NRGs. Kaplan-Meier plots were used to visualize survival functions. Receiver operating characteristic (ROC) curves were used to assess the prognostic predictive ability of NRG-based features. A nomogram containing the clinical information and prognostic scores of the patients was constructed using multivariate logistic regression and Cox proportional risk regression models.We identified 36 NRGs that significantly affected patient overall survival (OS). Eight NRGs (PARVB, LYZ, PPARGC1A, HIF1A, SPP1, CDH1, S100A9, and CXCL2) were found to have excellent predictive potential for patient survival. For the 1-, 3-, and 5-year survival rates, the obtained areas under the receiver operating characteristic curve values were 0.8, 0.82, and 0.79, respectively. In the training set, patients in the high NRG risk group presented a poorer prognosis (p < 0.0001), which was validated using two external datasets (GSE11318 and GSE34171). The calibration curves of the nomogram showed that it had excellent predictive ability. Moreover, in vitro quantitative real-time PCR (qPCR) results showed that the mRNA expression levels of CXCL2, LYZ, and PARVB were significantly higher in the DLBCL group.We developed a genetic risk model based on NRGs to predict the prognosis of patients with DLBCL, which may assist in the selection of treatment drugs for these patients.© 2023. BioMed Central Ltd., part of Springer Nature.