来自多中心研究的综合性分析确定了在卵巢癌中基于基因共同表达网络分析的Tregs签名的加权基因共同表达特征
Yang Cao1, Ying-Lei Liu1, Xiao-Yan Lu1
1Department of Obstetrics and Gynecology, Affiliated Hospital 2 of Nantong University, Nantong First People's Hospital, Nantong, China.
Environmental toxicology
|September 15, 2023
概括
在卵巢癌 (OC) 中高调节性T细胞 (Treg) 透与预后不佳相关. 这项研究确定了关键的Tregs相关基因 (TRGs),并开发了一种机器学习模型,用于预测OC患者的生存率和免疫治疗反应.
科学领域:
- 免疫学 免疫学 免疫学
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
背景情况:
- 卵巢癌 (OC) 的特点是预后不佳,通常受到瘤免疫微环境的影响.
- 调节性T细胞 (Tregs) 在OC微环境中在免疫抑制中发挥着关键作用,但它们与预后的特定基因关联尚未完全理解.
研究的目的:
- 调查Tregs相关基因 (TRGs) 与卵巢癌预后之间的关联.
- 确定与OC中的Tregs相关的潜在预后生物标志物和治疗点.
- 为OC患者开发基于机器学习的预后模型.
主要方法:
- 在多个患者队列中使用xCell算法分析Tregs得分.
- 权重基因共同表达网络分析 (WGCNA) 来识别TRG和分子亚型.
- 使用九种机器学习算法开发和验证预后风险模型.
- 在临床样本中通过RT-qPCR和免疫光检测验证关键的TRG表达和Tregs的免疫抑制功能.
主要成果:
- 较高的Tregs得分与OC患者的整体存活率较差显著相关.
- 复发的OC病例显示Tregs透率增加和CD8+T细胞水平降低.
- 分子亚型确定了一个"B亚型",与丰富的致癌途径,高Tregs水平,免疫抑制和更糟糕的预后相关.
- 机器学习模型有效地预测了不同队列的患者存活率和免疫治疗疗效.
- 在临床样本中证实了关键TRG (CD24,FHL2,GPM6A,HOXD8,NAP1L5,REN,TOX3) 的差异表达.
结论:
- Tregs显著影响OC免疫微环境和患者预后.
- 确定了七个关键的TRG作为个性化OC治疗的潜在治疗点.
- 开发了一种经过验证的基于机器学习的预后模型,以改善OC患者的临床管理.
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