机器学习支持的空间多态学揭示了乳酸驱动的目标和瘤微环境重编程在癌症中
Yingzheng Tan1,2,3, Wenliang Tan2,3, Yanchao Liang4
1Department of Infectious Disease, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, Hunan, China.
NPJ digital medicine
|December 30, 2025
概括
在肺腺癌 (LUAD) 中的乳酸积累重塑瘤微环境,影响细胞群体并促进血管生成. 针对乳酸路径提供了一个有希望的治疗策略,以改善患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 癌症新陈代谢 癌症新陈代谢
- 分子生物学分子生物学
背景情况:
- 乳酸积累是癌症新陈代谢的标志.
- 它在肺腺癌 (LUAD) 中的确切的空间和细胞类型特异性作用尚不清楚.
研究的目的:
- 研究LUAD中乳酸蓄积的空间和细胞类型特异性影响.
- 确定与乳酸相关的分子程序及其预后影响.
主要方法:
- 单细胞转录学,空间转录学,空间代谢学和免疫光学的整合.
- 对TCGA生存数据的分析和多种机器学习模型的应用 (随机森林,弹性网络,SVM,ANN,决策树).
主要成果:
- 高乳酸盐的LUAD瘤显示了上皮细胞和纤维细胞的增加,而低乳酸盐的瘤丰富了T/NK细胞和巨细胞.
- 空间代谢学显示了不同细胞类型的乳酸盐和酸盐分布;内皮细胞显示了最小的乳酸盐积累.
- 高乳酸盐区域的内皮子集群表现出血管新生和压力特征,与预后不佳相关.
- 机器学习始终将内皮细胞和纤维细胞程序确定为高乳酸状态和不良结果的关键驱动因素.
结论:
- 乳酸积累显著改变了LUAD微环境,促进了血管生成和免疫抑制.
- 空间分析突出了乳酸在LUAD患者预后分层中的作用.
- 乳酸中心路径代表了LUAD治疗的潜在治疗点.
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