基于批量/单细胞RNA测序的细胞衰老和代谢重编程模型揭示了PTGER4作为ccRCC治疗点
Lijie Zhou1,2, Youmiao Zeng3,4, Yuanhao Liu3,5
1Department of Urology, First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan Province, China. zhouxiaozhou29@126.com.
BMC cancer
|April 11, 2024
概括
一种新的衰老代谢相关风险模型 (SeMRM) 准确地预测了清细胞细胞癌 (ccRCC) 患者的结果. 该模型为ccRCC提供了改进的预后评估和治疗策略选择.
科学领域:
- 在瘤学瘤学.
- 代谢重编程 代谢重编程
- 细胞衰老 细胞衰老
背景情况:
- 清细胞细胞癌 (ccRCC) 呈现出独特的代谢重编程.
- 细胞衰老是癌症的一个新兴的标志.
- 与衰老相关的代谢变化和ccRCC之间的关系需要进一步研究.
研究的目的:
- 开发和验证一个风险评分模型,将衰老和新陈代谢整合为ccRCC预后.
- 评估与衰老代谢相关风险模型 (SeMRM) 与现有临床标志物的预测性能.
- 探索与衰老代谢相关的风险评分 (SeMRS) 和ccRCC患者进展之间的关联,包括代谢活动,免疫微环境和药物反应.
主要方法:
- 蛋白质与蛋白质相互作用网络分析.
- 最小绝对收缩和选择操作员 (LASSO) 回归分析.
- 使用ICGC-KIRC,GSE29609和GSE156632数据集以及临床数据进行验证.
主要成果:
- 与当前的临床预后标志物相比,开发的SeMRM在ccRCC患者的整体生存期 (OS) 中表现出更高的预测准确性.
- 在SeMRS分层的子组中观察到代谢活动,免疫格局,突变概况和药物敏感性的显著变化.
- 发现PTGER4基因是SeMRM的关键组成部分,可以调节ccRCC细胞增殖,脂质代谢和细胞循环进展在体外和体内.
结论:
- 在ccRCC中,SeMRM作为可靠的临床特征来提高预后准确性.
- 这个模型可以帮助选择适合和个性化的治疗策略,为ccRCC患者.
- 对ccRCC中衰老-代谢相互作用的进一步研究是有必要的.
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