细胞死亡相关基因机器学习模型用于肝细胞癌的精确治疗和临床药物选择
Mingyang Du1, Yonggang Qu2, Lingshan Qin3
1Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Journal of cellular and molecular medicine
|March 18, 2024
概括
这项研究开发了使用编程细胞死亡 (PCD) 模式的肝细胞癌 (HCC) 的预测模型,识别了不同的风险群体和潜在的向疗法,以改善患者的治疗结果.
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:
- 肝细胞癌 (HCC) 是癌症相关死亡的主要原因.
- 多重编程细胞死亡 (PCD) 机制与HCC进展之间的关系尚未完全理解.
- 现有的研究缺乏全面的模型,以基于PCD模式来预测HCC患者的结果.
研究的目的:
- 开发HCC中不同PCD模式的预测模型.
- 研究PCD模式对HCC患者的生存,预后和药物反应的影响.
- 确定潜在的治疗点,并为HCC个性化治疗策略.
主要方法:
- 与PCD相关的基因 (PCDRGs) 的功能注释和途径分析.
- 使用GEO数据集和共识集群进行预测值验证.
- 整合基因表达分析,免疫细胞透和丰富分析.
- 为生存预测开发PCD模型和名图.
- 对12种PCD机制的分析和预后PCDRG的识别.
主要成果:
- 确定了1254个PCDRG,其中37个具有HCC的预后价值.
- 将HCC患者分为两个PCD群 (A和B),其中B群的生存率更好.
- 发现瘤突变负担 (TMB) 增加与死亡风险增加有关.
- 分别对低风险和高风险的HCC患者有效的特定药物 (例如,AZD2014,PD173074).
- 根据PCD表达模式观察到免疫治疗 (ICI) 反应的异质性.
结论:
- 结合多种PCD模式的新型预后模型可以预测HCC患者的生存率和药物敏感性.
- PCD B 集群可能对 HCC 进展有保护作用.
- 基于风险组和PCD表达模式的个性化治疗选择可以改善临床结果.
- 需要进一步的研究来验证该模型及其对HCC管理的临床适用性.
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