差异关联 告知代谢物 集丰富分析 解读疾病的代谢异质性
Genjin Lin1, Liheng Dong2, Kian-Kai Cheng3
1Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.
Analytical chemistry
|August 9, 2023
概括
代谢基组丰富分析 (MSEA) 识别了疾病中改变的代谢途径. 一种新的方法,差异相关性告知MSEA (dci-MSEA),通过创建表型特定网络来改善途径识别,增强疾病机制洞察力.
科学领域:
- 代谢学和系统生物学
- 生物信息学和计算生物学
背景情况:
- 代谢基组丰富分析 (MSEA) 对于识别与感兴趣的表型 (POI) 相关的代谢途径至关重要.
- 传统的MSEA经常面临由于代谢物覆盖率低的局限性.
- 现有的基于随机步行 (RW) 的算法使用一般的代谢物网络,可能降低表型特异性.
研究的目的:
- 引入一种新的途径分析策略,即基于差异相关性的MSEA (dci-MSEA).
- 通过结合POI特定的代谢物网络信息来提高MSEA的准确性和表型特异性.
- 应用dci-MSEA来分析疾病异质性,特别是结肠直肠癌.
主要方法:
- 使用代谢物之间的差异关联来反映POI影响,构建表型特定的代谢物网络.
- 在表型特定网络上应用随机步行 (RW) 算法,用于代谢物干扰传播.
- 利用dci-MSEA识别改变的代谢途径,分析结直肠癌样本中的疾病异质性.
主要成果:
- 与传统的基于RW的MSEA相比,dci-MSEA在识别与结直肠癌相关的代谢途径方面表现优越.
- 该策略通过基于集群特定差异性途径的样本集群成功破译了结直肠癌异质性.
- 证实了dci-MSEA在疾病异质性分析中的可行性.
结论:
- 在代谢学中,dci-MSEA对已有的途径分析方法提供了显著的改进.
- 该方法为疾病机制和疾病异质性的确定提供了宝贵的见解.
- dci-MSEA是一种多功能策略,可扩展到各种疾病特定分析和异质性研究.
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