大量和单细胞RNA-seq数据的机器学习集成揭示了结直肠癌中的糖分异质性
Yuanyuan Du1,2, Zefeng Miao2, Peng Li2
1College of Laboratory Medicine, Dalian Medical University, Dalian, 116044, Liaoning, China.
Medical oncology (Northwood, London, England)
|August 30, 2025
概括
结肠直肠癌细胞表现出代谢多样性,高糖解状态的亚群与干状特征和不良结果有关. 这些发现突出了结直肠癌的潜在治疗点.
科学领域:
- 癌症学
- 代谢途径
- 癌症生物学
背景情况:
- 结肠直肠癌 (CRC) 是一个全球性恶性瘤.
- 在瘤生长和免疫规避方面,CRC严重依赖糖解.
- 需要进一步研究CRC的代谢调节和细胞多样性.
研究的目的:
- 系统地探索结肠直肠癌中的糖解代谢的调节场景和细胞多样性.
- 根据代谢特征识别关键的调节基因和不同的细胞亚群.
- 在多个独立的队列中验证结果.
主要方法:
- 分析多组数据集,包括批量/scRNA-seq和空间转录组.
- 使用集成路径映射和具有五个特征选择算法的机器学习框架.
- 通过代谢特征划分细胞亚群,并通过连接体-受体网络建模了利基相互作用.
主要成果:
- 鉴定出具有干状性质的高糖解状态 (HGS) 瘤亚群.
- 空间分析显示HGS细胞在中部瘤区域富含,表明它们适应营养缺乏.
- 五种候选调节剂 (PFKP,ERO1A,FKBP4,HDLBP,HSPA5) 与高血压和不良临床结果相关.
结论:
- 在结直肠癌中表现出显著的代谢异质性.
- 表明高糖解状态细胞在形成瘤微环境中起作用.
- 确定了可能用于新型结直肠癌治疗策略的分子特征.
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