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建立一个由免疫相关基因模块组成的模型,以预测瘤免疫治疗反应
Deqiang Fu1, Xiaoyuan Weng2,3, Yunxia Su1
1Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Scientific reports
|July 18, 2024
概括
这项研究开发了一种新的预测模型,使用修改过的权重基因联合表达网络分析 (WGCNA) 算法来准确预测抗瘤免疫反应对癌症免疫疗法的免疫反应. 该模型在临床数据集中显示出可靠的预测准确性.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
背景情况:
- 瘤免疫治疗是一种关键的癌症治疗方法,但预测其有效性仍然具有挑战性.
- 准确预测治疗反应对于优化瘤学患者的治疗结果至关重要.
研究的目的:
- 使用修改的权重基因联合表达网络分析 (WGCNA) 算法,构建针对抗瘤免疫反应的强有力的预测模型.
- 确定与免疫治疗反应相关的关键免疫相关基因 (irDEGs) 和基因模块.
- 在临床癌症数据集中验证模型的预测性能.
主要方法:
- 在小鼠结肠癌模型中查差异表达基因 (DEGs).
- 使用基因组丰富分析 (GSEA) 来分析与免疫相关的DEGs (irDEGs).
- 应用修改后的WGCNA算法来识别基因模块,并建立基于与免疫治疗反应状态的相关系数的预测模型.
主要成果:
- 确定了34个重要的irDEG和四个关键基因模块 (同基因,抗PD-1免疫基因,化疗免疫基因,免疫基因).
- 开发了一个具有0.727的曲线下面积 (AUC) 的预测模型,用于模型得分和0.705的模型得分分类.
- 与已建立的ESTIMATE和TIDE分数和分类相比,已证明的预测性能不劣.
- 在胃癌和其他癌症患者的独立临床数据集中验证了模型的准确性.
结论:
- 基于修改后的WGCNA开发的预测模型为准确评估免疫治疗对抗瘤免疫反应提供了一个有希望的工具.
- 该模型的性能与现有方法相比,表明其在指导癌症治疗决策方面的潜在临床实用性.
- 该模型的进一步验证和应用可以改善患者分层和免疫疗法治疗选择.
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