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Nearly all the energy used by cells comes from the bonds that make up complex organic compounds. These organic compounds are broken down into simpler molecules, such as glucose. As a result, cells extract energy from glucose over many chemical reactions—a process called cellular respiration.
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宫癌中放射治疗结果的免疫预测因素

Linghao Wang1, Jie Zhu2, Zequn Ding1

  • 1Med-X Research Institute & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
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概括

这项研究揭示了瘤免疫微环境在放射治疗后的变化,确定了预测患者反应的关键免疫细胞和相互作用. 这些发现为改善宫癌放射治疗效率提供了新的生物标志物.

关键词:
宫癌:子宫癌是一种癌症.补充路径是补充路径.机器学习是机器学习.巨细胞的两极分化放射治疗的结果.

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科学领域:

  • 在瘤学瘤学.
  • 免疫学 免疫学 免疫学
  • 放射治疗研究 放射治疗研究

背景情况:

  • 瘤免疫微环境显著影响放射治疗 (RT) 的敏感性,但抵抗机制尚未完全理解.
  • 识别免疫预测因子对于优化RT结果和开发个性化治疗策略至关重要.

研究的目的:

  • 为了研究放射治疗后免疫微环境的动态变化.
  • 确定免疫细胞子集和分子相互作用,预测患者对RT的反应.
  • 开发一种基于RT结果的患者分层的预测模型.

主要方法:

  • 单细胞转录组学和机器学习算法的集成.
  • 对免疫细胞种群,激活状态和细胞间通信 (CellChat) 的综合分析.
  • 在临床前小鼠模型中的验证和预测性预后模型 (CCRTIM) 的开发.

主要成果:

  • 放射治疗改变了瘤的微环境,其特点是上皮细胞减少,亡增加和炎症.
  • 在RT后观察到M1-类HSPA1B+巨细胞的积累,具有增强的抗原呈现能力.
  • 增加了T和NK细胞细胞毒性,并加剧了疲劳标记 (PDCD1,TIGIT).
  • 通过C3/C3AR1轴的上皮-骨髓交叉被确定为关键;C3AR1对抗性降低了RT疗效.
  • 一个8个特征模型 (CCRTIM) 准确预测了预后 (AUC=0.76) 并启用了风险分层.

结论:

  • 放射治疗在瘤微环境中诱导了显著的免疫重塑.
  • C3/C3AR1轴和特定的巨细胞子集是RT反应的关键参与者.
  • 开发的CCRTIM模型提供了可操作的生物标志物,用于提高宫癌精度放射治疗.