在HCC中解读胆固醇-亡轴:基于机器学习的多omics集成和单细胞转录组分析
Yang Li1, Jun Shi2, Aiqing Zhao3
1Department of Emergency, Ningbo Medical Center Lihuili Hospital, The Affiliated Lihuili Hospital of Ningbo University, Ningbo, 315040, Zhejiang, China.
Discover oncology
|November 25, 2025
概括
这项研究确定了六个关键基因与亡和胆固醇代谢相关,作为肝肝细胞癌 (LIHC) 的关键预后标志物. 这一发现有助于早期风险评估和LIHC患者的个性化治疗策略.
科学领域:
- 在瘤学瘤学.
- 分子生物学分子生物学
- 遗传学 是一个遗传学.
背景情况:
- 肝肝细胞癌 (LIHC) 是一种流行癌症,发病率不断增加,对健康和经济构成重大挑战.
- 早期诊断LIHC是困难的,导致晚期检测和治疗效率降低.
研究的目的:
- 研究LIHC中亡和胆固醇代谢的作用.
- 确定新的预后生物标志物,并开发LIHC的风险预测模型.
主要方法:
- 利用多omics数据和机器学习进行基因表达分析.
- 采用LASSO回归来识别枢纽基因,并构建了一个预后风险评分模型.
- 进行了免疫细胞透,基因丰富和单细胞分析.
主要成果:
- 确定了与亡和胆固醇代谢相关的差异表达基因 (ACMRDEGs).
- 在LIHC.中,六个枢纽基因 (EPHX2,FABP5,SQLE,ADH4,HMGCS2,CYP7A1) 被验证为总生存期 (OS) 的显著预后生物标志物.
- 在LIHC中揭示了显著的免疫细胞透差异和丰富的胆固醇/酒精代谢途径,单细胞分析详细说明了细胞异质性.
结论:
- 开发并验证了基于细胞亡和胆固醇代谢途径的LIHC的第一个综合性六基因预后模型.
- 该模型显示了改善LIHC临床实践中的风险分层和指导个性化治疗策略的潜力.
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