骨肌肉转录组的集成机器学习和网络分析确定了在多囊性卵巢综合征中对pioglitazone反应的候选生物标志物
Ahmad Al Athamneh1, Mahmoud E Farfoura2, Anas Khaleel3
1Department of Nutrition, Faculty of Pharmacy and Medical Sciences, University of Petra, Amman 11196, Jordan.
Genes
|January 28, 2026
概括
在患有多囊卵巢综合征 (PCOS) 的女性骨肌肉基因表达的机器学习分析中,确定了ITK,WT1和BRD1.1等关键基因. 这些发现可能会导致PCOS治疗反应的新生物标志物.
科学领域:
- 基因组学和生物信息学
- 内分泌学和新陈代谢学
- 精准医学是一门精准的医学.
背景情况:
- 多囊卵巢综合征 (PCOS) 是一种常见的内分泌代谢障碍.
- 在PCOS中,骨肌肉的胰岛素耐药性对心脏代谢风险有显著的贡献.
- 皮奥格利塔可以改善PCOS患者的胰岛素敏感性,但其分子机制和生物标志物潜力尚未完全理解.
研究的目的:
- 重新分析皮奥格利塔治疗的PCOS患者的骨肌肉基因表达数据.
- 通过机器学习和网络方法识别候选生物标志物和监管中心.
- 探索PCOS精密疗法的潜力.
主要方法:
- 从PCOS和控制骨肌中重新处理公开的微阵列数据 (GSE8157).
- 鉴定差异表达基因 (DEGs) 和途径分析 (英才途径分析).
- 应用四个监督机器学习算法与基因分析的交叉验证.
- 基因共同表达网络的构建,以识别枢纽基因.
- 模拟的多学科框架集成成绩单和临床变量.
主要成果:
- 在皮奥格利塔后的PCOS骨肌中发现了1459个DEG,涉及免疫,纤维,干扰素和表观遗传信号.
- 机器学习模型使用紧的基因面板实现了PCOS和对照之间的优秀歧视.
- 确定的关键调节节点包括ITK,WT1,BRD1链接的位点和长非编码RNA.
- 模拟的多omics签名显示稳定了发现性能.
结论:
- 综合机器学习和网络分析揭示了区分PCOS的候选基因和监管枢纽.
- 这些发现表明皮奥格利塔在PCOS中具有免疫代谢和表观遗传的作用.
- ITK,WT1,BRD1关联基因和网络基因被提名为有希望的生物标志物,用于更大的队列中的验证.
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