机器学习计算框架开发了多重编程细胞死亡指数,以改善膀癌的临床结果
Chunhong Li1, Wangshang Qin2, Jiahua Hu3
1Central Laboratory, Guangxi Health Commission Key Laboratory of Glucose and Lipid Metabolism Disorders, The Second Affiliated Hospital of Guilin Medical University, Guilin, 541199, Guangxi, China. chunhongli@glmc.edu.cn.
一个新的多重编程细胞死亡指数 (MPCDI) 预测了膀癌 (BLCA) 患者的预后和治疗反应. 高的MPCDI表明前景更糟,而低的MPCDI则表明更好的预后,并指导化疗决策.
科学领域:
- 在瘤学瘤学.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 膀癌 (BLCA) 中的编程细胞死亡 (PCD) 机制尚未完全理解.
- 调查PCD模式对于推进BLCA治疗策略至关重要.
研究的目的:
- 开发和验证一种用于评估BLCA中PCD模式的新型索引.
- 评估该指数对患者的治疗结果和治疗反应的预后和预测价值.
主要方法:
- 利用机器学习框架分析了1911个PCD相关基因和19个PCD模式.
- 使用TCGA-BLCA和GSE13507队列开发了一种多重编程细胞死亡指数 (MPCDI).
- 构建并验证了一个将MPCDI与临床特征结合在一起的名录.
主要成果:
- 高MPCDI与更糟糕的预后相关,而低MPCDI表明BLCA患者的预后更好.
- 开发的诺米图表显示出在单个变量上更高的准确性和临床效用.
- 根据免疫透,免疫治疗反应和药物敏感性,MPCDI有效地区分了患者组.
- 高MPCDI患者表现出更好的疗效与常见的化疗药物.
结论:
- 该MPCDI作为一个新的临床分类器和BLCA可靠的预测器.
- 在BLCA患者中,MPCDI得分可以指导化学治疗和免疫治疗的临床决策.
- 该指数为BLCA异质性和潜在的治疗策略提供了有价值的见解.
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