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免疫微环境和药物耐药性特征的综合多基因分析,用于前列腺癌的精确预后
Chao Li1,2, Longxiang Wu3,2, Bowen Zhong3,2
1Department of Urology, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan, China.
Cancer drug resistance (Alhambra, Calif.)
|August 22, 2025
概括
这项研究通过分析瘤微环境 (TME) 数据开发了前列腺癌 (PCa) 的新预后模型. 该模型识别了具有特定基因突变的高风险PCa患者,并预测了他们对不同疗法的反应.
科学领域:
- 癌症学
- 免疫学
- 基因组学
背景情况:
- 前列腺癌 (PCa) 仍然是男性癌症死亡的主要原因.
- 复杂的瘤微环境 (TME) 显著影响PCa的治疗耐药性.
研究的目的:
- 为PCa开发一种以免疫为中心的预后模型.
- 关联TME动态,基因组不稳定性和药物耐药性的异质性.
主要方法:
- 来自TCGA和GEO数据库的综合多组数据 (554个PCa样本).
- 使用CIBERSORT和ESTIMATE进行免疫细胞透的评估.
- 使用WGCNA识别与免疫相关的模块.
- 使用单细胞RNA测序 (ScRNA-seq) 来发现耐药性模式.
- 使用LASSO回归构建并验证了10基因预后模型.
主要成果:
- 确定了两种免疫亚型:高风险亚组显示TP53突变,增加瘤突变负担 (TMB) 和丰富的能量代谢.
- 在ScRNA-seq检测中,发现PCa细胞群具有高风险亚型,对bendamustine/ dacomitinib敏感,对 apalutamide/ neratinib有抗性.
- 在验证数据集中,10基因模型准确地将患者分为高风险/低风险组,具有明显的生存结果 (日志等级P< 0. 0001) 和高预测精度 (AUC: 0. 854- 0. 889).
结论:
- 建立了TME驱动的预后框架,将PCa中的免疫异质性,基因组不稳定性和治疗耐药性联系起来.
- 鉴定了针对性治疗的代谢依赖性和亚型特定的脆弱性.
- 通过针对能量代谢或根据耐药性特征定制治疗来克服治疗失败的策略.
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