机器学习和对超性心肌病和癌症的新生物标志物的实验验证
Hualei Dai1,2, Ying Liu3, Meng Zhu4
1Cardiovascular Center, The Affiliated Hospital of Yunnan University, Yunnan University, Kunming, Yunnan, China.
Journal of cellular and molecular medicine
|August 19, 2024
概括
新的生物标志物GATM和MGST1显示出对诊断多变性心肌病 (HCM) 的前景. 这些发现可能会导致更好的风险评估和对HCM进展的理解,可能涉及免疫细胞.
科学领域:
- 心脏病学 心脏病学
- 生物标志物发现发现
- 机器学习 机器学习
背景情况:
- 增高性心肌病变 (HCM) 是一种具有显著死亡率的遗传性心脏病.
- 免疫炎症在HCM病原发生中的作用尚未完全理解.
研究的目的:
- 通过机器学习,识别超性心肌病 (HCM) 的新生物标志物.
- 开发和验证HCM患者的风险评估模型.
主要方法:
- 采用了五种机器学习算法 (LASSO,SVM,RF,Boruta,XGBoost) 来识别HCM生物标志物.
- 使用GATM和MGST1进行风险评估,并通过临床样本进行验证.
- 分析了HCM和正常心脏组织中的GATM和MGST1表达水平.
主要成果:
- 确定了五种新的HCM生物标志物:DARS2,GATM,MGST1,SDSL和ARG2. 这些生物标志物包括:
- 在培训和测试队列中,GATM和MGST1对HCM (AUC>0.8) 显示出显著的诊断效用.
- 基于GATM和MGST1的风险评估模型显示出高性能 (AUC为0.88-0.9).
- 在HCM组织中,GATM和MGST1被上调,使它们与正常组织区别开来 (AUC为0.79-0.86).
- 单细胞和多能原始细胞 (MPP) 参与HCM病变的发生.
结论:
- GATM和MGST1是用于HCM诊断和风险分层的新型,验证的生物标志物.
- 在HCM组织中GATM和MGST1表达的升高表明它们在疾病进展中的作用.
- 单细胞和MPP可能有助于HCM的发展,与癌症途径的潜在联系.
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