Dai Qian, Wang Hua, Li Fang, Huang Runchun, Jiang Chenjun, Yuan Liuya, Wang Yayun, Li Xun
The First Clinical Medical College, Lanzhou University, Lanzhou, China.
Department of Urology, First Hospital of Lanzhou University, Lanzhou, China.
Front Immunol. 2025 Aug 18;16:1467357. doi: 10.3389/fimmu.2025.1467357. eCollection 2025.
Prostate cancer (PCa) is a common and deadly cancer in men, and despite its low specificity, PSA testing is the main method that is used to predict prognosis. Effective methods for predicting prognosis in clinical practice are lacking. Here, ① in this retrospective analysis of clinical data of PCa patients, we discovered that patients with PCa have elevated neutrophil levels and a greater risk of complications than patients with prostatic hyperplasia. ② We integrated LASSO regression analysis and machine learning analyses to create a prognostic prediction model involving 6 genes, and this model effectively categorized patients into high-risk and low-risk groups, with higher risk scores indicating a poorer prognosis. Furthermore, we used multivariate regression analysis to confirm that the risk score was an independent prognostic factor and created nomograms on the basis of clinical characteristics. Notably, the deconvolution algorithm revealed different compositions of the tumor microenvironment, with a greater proportion of neutrophils observed in the high-risk group. ③ Finally, we conducted single-cell sequencing analysis and established a prostate cancer organoid model to confirm that TANs may exacerbate the TME in PCa via neutrophil trap formation, which is mediated by the PSMA1-NF-κB-HIF-1α signaling axis. Overall, this novel NET-related signature of PCa provides new insights for in-depth understanding and prediction of PCa prognosis.
前列腺癌（PCa）是男性常见的致命性癌症，尽管其特异性较低，但前列腺特异性抗原（PSA）检测仍是用于预测预后的主要方法。临床实践中缺乏有效的预后预测方法。在此，① 在这项对PCa患者临床数据的回顾性分析中，我们发现PCa患者的中性粒细胞水平升高，且与前列腺增生患者相比，并发症风险更高。② 我们整合了套索（LASSO）回归分析和机器学习分析，创建了一个包含6个基因的预后预测模型，该模型有效地将患者分为高风险和低风险组，风险评分越高表明预后越差。此外，我们使用多变量回归分析证实风险评分是一个独立的预后因素，并根据临床特征创建了列线图。值得注意的是，反卷积算法揭示了肿瘤微环境的不同组成，在高风险组中观察到更高比例的中性粒细胞。③ 最后，我们进行了单细胞测序分析并建立了前列腺癌类器官模型，以证实肿瘤相关中性粒细胞（TANs）可能通过由前列腺特异性膜抗原1（PSMA1）-核因子κB（NF-κB）-缺氧诱导因子1α（HIF-1α）信号轴介导的中性粒细胞胞外陷阱形成来加剧PCa中的肿瘤微环境（TME）。总体而言，这种新的PCa相关中性粒细胞胞外陷阱（NET）特征为深入理解和预测PCa预后提供了新的见解。
