基于675组合机器学习计算框架的非阻断性亚精菌的编程细胞死亡相关特征的多组学识别
Shuqiang Huang1, Cuiyu Tan1, Wanru Chen2
1Center for Reproductive Medicine, the Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong 511518, China.
Genomics
|December 11, 2024
概括
这项研究使用多组学和机器学习确定了非阻断性精症 (NOA) 的关键编程细胞死亡 (PCD) 生物标志物. 开发的PCDscore系统有助于诊断NOA并了解其分子机制.
科学领域:
- 生殖生物学 生殖生物学
- 遗传学 是一个遗传学.
- 计算生物学 计算生物学
背景情况:
- 异常编程细胞死亡 (PCD) 与精子生成功能障碍有关.
- 在非阻塞性精症 (NOA) 中,PCD的分子机制和生物标志物尚不清楚.
研究的目的:
- 在NOA患者中阐明PCD机制.
- 为了确定NOA诊断的新型PCD生物标志物.
- 开发一个评分系统来评估NOA中的PCD.
主要方法:
- 对NOA患者的多组学分析 (批量和单细胞转录学,SNP数据).
- 机器学习可以识别12个具有特征的PCD相关基因 (PCDRGs).
- 开发PCDscore系统并使用分子和组织学技术在NOA小鼠模型中进行验证.
主要成果:
- 通过机器学习识别的12个PCDRG建立了一个PCDscore系统.
- PCD得分与改变的基因相互作用和免疫活性相关,特别是涉及莱迪格,塞尔托利和T细胞.
- 五个PCDRG (BCL2L14,GGA1,GPX4,PHKG2,SLC39A8) 显示出与精子生成和PCD状态有很强的联系,可能受到SNP的影响.
结论:
- 该研究通过多组学数据提供了对NOA中PCD机制的新见解.
- 为NOA诊断提出了一个可靠的PCDscore系统和生物标志物.
- 了解这些机制可以推进NOA的临床诊断和治疗策略.
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