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基于神经元炎症基因的机器学习模型用于乳腺癌:对临床预后和瘤免疫力的新观点
Hongxing Wang1, Haihong Hu2, Jingdi Zhang2
1Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China.
Discover oncology
|December 14, 2025
概括
这项研究确定了与神经元炎症相关的基因 (NIRG),并开发了乳腺癌的预后模型. 该模型采用VDAC1,改善风险评估,并确定新的治疗目标,以改善患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 遗传学 遗传学 是一个
背景情况:
- 乳腺癌的异质性给个性化治疗和预后带来了挑战.
- 目前的预后指标有限,神经元炎症在乳腺癌进展中的作用尚不清楚.
- 这项研究旨在确定与神经元炎症相关的生物标志物,并开发一种预后模型,以提高风险评估和治疗策略.
研究的目的:
- 确定与神经元炎症相关的基因 (NIRG) 作为乳腺癌的潜在生物标志物.
- 使用NIRG构建基于机器学习的预测模型,以改善风险分层.
- 研究关键预后基因在瘤免疫细胞相互作用中的作用.
主要方法:
- 使用无监督聚类来根据NIRG表达方式对患者进行分类.
- 机器学习技术,包括LASSO和Cox回归,用于识别预后基因并构建模型.
- 单细胞RNA测序 (scRNA-seq) 和CellChat分析进行,以选关键基因并分析细胞通信.
主要成果:
- 基于NIRG的预后模型显示出良好的预测性能,低风险组显示出更好的结果和更丰富的免疫透.
- 通过scRNA-seq.识别VDAC1作为一个关键的神经元炎症相关基因.
- 细胞通信分析揭示了VDAC1+乳腺癌细胞,耗尽的CD8+T细胞和M2巨细胞之间的相互作用,可能通过MIF途径.
结论:
- 基于NIRG的预后模型提供了有效的预测乳腺癌存活率和治疗反应.
- 在乳腺癌微环境中,VDAC1在神经元炎症和瘤免疫细胞相互作用中发挥着关键作用.
- 这项研究为乳腺癌中神经元炎症提供了新的见解,突出了VDAC1作为潜在的治疗点,并为临床应用提供了一个网络工具.
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