综合生物信息学和机器学习将S100A9和VGLL1确定为精神分裂症的枢纽基因
Jiankang Lv1, Xueru Wang2, Wei Qin3
1Department of Severe Psychiatry, Shaoxing Seventh People's Hospital (Affiliated Mental Health Center, Medical College of Shaoxing University), Shaoxing, Zhejiang, China.
Frontiers in psychiatry
|September 22, 2025
概括
研究人员确定S100A9和VGLL1是精神分裂症 (SCZ) 的潜在生物标志物. 这些发现强调了免疫调节的作用.
科学领域:
- 神经科学是一个神经科学.
- 基因组学就是基因组学.
- 免疫学 免疫学 免疫学
背景情况:
- 精神分裂症 (SCZ) 是一种复杂的神经精神疾病,病因不明.
- 目前的SCZ诊断依赖于主观评估,治疗方法不足以解决认知和负面症状.
- 识别可靠的SCZ生物标志物对于改善诊断和向治疗的发展至关重要.
研究的目的:
- 通过综合生物信息学和机器学习,识别精神分裂症 (SCZ) 的潜在诊断生物标志物.
- 探索与已识别的SCZ生物标志物相关的潜在生物机制.
主要方法:
- 分析了来自五个独立队伍的转录组数据,使用差异表达式分析和强大的排名聚合 (RRA).
- 采用蛋白与蛋白相互作用 (PPI) 网络,拉索回归和随机森林 (RF) 来识别枢纽基因.
- 使用后勤回归构建了一个诊断模型,并通过ROC曲线,名图和校准曲线评估其性能.
主要成果:
- S100A9和VGLL1被确定为SCZ的潜在诊断生物标志物.
- 诊断模型表现强,在训练中AUC值为0.806,在验证队列中为0.702-0.739.
- 丰富分析表明参与免疫调节和PI3K-Akt信号传递;免疫细胞透率升高与S100A9水平相关.
结论:
- 建议S100A9和VGLL1作为SCZ的新生物标志物,这意味着免疫系统在病变发生过程中的失调.
- 这些生物标志物为改善SCZ诊断和治疗策略提供了潜力.
- 这项研究为SCZ病理生理学提供了新的见解,重点关注与免疫相关的机制.
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