整合多omics和机器学习系统地解读细胞异质性和纤维性调节网络在从MASLD到MASH的进展中
Weiheng Wen1,2, Zenghui Liu1,3, Wenliang Tan1,4
1Tumor ImmunoMetabolism Institute (TIMI), Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, Hunan, China.
NPJ digital medicine
|January 16, 2026
概括
研究人员确定了一种特定的巨细胞亚群 (DTNA+),与代谢功能障碍相关的脂肪肝炎 (MASH) 的进展有关. 这一发现为诊断和治疗MASH提供了潜在的新目标.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 免疫学 免疫学 免疫学
- 分子生物学分子生物学
背景情况:
- 与代谢功能障碍相关的脂肪性肝病 (MASLD) 可以发展为与代谢功能障碍相关的脂肪性肝炎 (MASH),这是肝硬化和肝癌的前体.
- 推动MASLD发展为MASH的细胞机制尚未完全理解.
研究的目的:
- 为了阐明MASLD-MASH进展期间的细胞和分子变化.
- 确定MASH的新生物标志物和治疗点.
主要方法:
- 单细胞,空间和批量转录数据集的整合.
- 使用伪时间轨迹分析识别和描述巨细胞亚群.
- 细胞间细胞通信通路的分析.
- 开发用于生物标志物发现的机器学习模型.
主要成果:
- 一个独特的DTNA+巨细胞亚群,以M2极化,缺氧和炎症为特征,在MASH中得到丰富.
- 这些DTNA+巨细胞起源于库普弗细胞,由RUNX2.2调节.
- 在DTNA+巨细胞和肝星细胞之间确定了一个RUNX2-PLG-PARD3信号轴,促进纤维化.
- DTNA被验证为预测生物标志物,以高精度 (AUC=0.839) 从MASH区分MASLD.
结论:
- DTNA+巨细胞和RUNX2-PLG-PARD3轴是MASH病变发生的关键机制.
- 作为MASH诊断的非侵入性生物标志物,DTNA显示出希望.
- 准DTNA+巨细胞或已识别的信号轴可能为MASH提供新的治疗策略.
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