全癌症单细胞和空间转录组学涉及与癌症相关的纤维细胞在中性粒细胞免疫抑制表型转变和免疫治疗耐药性中的作用
Zhiyu Guo1,2,3,4, Xujia Li1,2,3,4, Lingli Huang5
1VIP Department, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, P.R. China.
Functional & integrative genomics
|October 9, 2025
概括
这项研究揭示了21种癌症类型中的中性粒细胞异质性,识别了CXCR2+VNN2+Neu作为免疫抑制剂. 纤维细胞活动影响这种过渡,影响免疫疗法耐药性和患者的预后.
科学领域:
- 免疫学 免疫学 免疫学
- 在瘤学瘤学.
- 基因组学就是基因组学.
背景情况:
- 中性粒细胞是关键的免疫细胞,但表现出显著的异质性,使其在癌症中的作用复杂化.
- 目前中性粒细胞分化的模型不足以解释它们在瘤中的多样性功能.
- 了解中性粒细胞异质性对于开发有效的癌症疗法至关重要.
研究的目的:
- 在21种癌症类型中创建一个全面的中性粒细胞单细胞地图.
- 阐明驱动中性粒细胞异质性和免疫抑制功能的调节机制.
- 确定潜在的治疗点,并开发患者分层的预测模型.
主要方法:
- 462名癌症患者中性粒细胞的单细胞RNA测序.
- 空间转录学用于分析瘤微环境相互作用.
- 基因调控网络的构建和深度学习模型 (Deepsurv) 的开发.
主要成果:
- 确定了CXCR2+VNN2+Neu作为主要的免疫抑制性中性粒细胞亚群.
- 连接纤维细胞活动到CXCR2+VNN2+Neu通过连接体-受体相互作用和信号传递的表型过渡.
- 发现BACH1和ATF2是潜在的治疗点.
- 发现CXCR2+VNN2+Neu过渡与免疫疗法耐药性相关.
- 开发了Deepsurv模型,用于准确的患者分层和预后预测.
结论:
- 瘤微环境成分显著影响中性粒细胞异质性和功能.
- 针对特定的中性粒细胞亚群和调节途径提供了新的治疗策略.
- Deepsurv模型为个性化癌症治疗和结果预测提供了有价值的工具.
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