微分相关性代谢物组富集分析可破译疾病的代谢异质性,Analytical Chemistry

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微分相关性代谢物组富集分析可破译疾病的代谢异质性
Analytical Chemistry ( IF 6.7 ) Pub Date : 2023-08-09 , DOI: 10.1021/acs.analchem.3c02246
Genjin Lin ₁ , Liheng Dong ₂ , Kian-Kai Cheng ₃ , Xiangnan Xu ₄ , Yongpei Wang ₁ , Lingli Deng ₅ , Daniel Raftery ₆ , Jiyang Dong ₁

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代谢途径被认为是生物系统的功能和基本组成部分。在代谢组学中，代谢物集富集分析 (MSEA) 通常用于识别与感兴趣的表型 (POI)（例如疾病）相关的改变的代谢途径（代谢物集）。然而，在大多数研究中，MSEA 受到代谢物覆盖率低的限制。基于随机游走 (RW) 的算法可用于通过 MSEA 之前的代谢物网络模型将检测到的代谢物的扰动传播到未检测到的代谢物。然而，现有的基于RW的算法大多数运行在基于公共数据库（例如KEGG）构建的通用代谢物网络上，没有考虑POI对代谢物网络的潜在影响，这可能会降低MSEA结果的表型特异性。为了解决这个问题，本文提出了一种新的路径分析策略，即差分相关信息MSEA（dci-MSEA）。统计上，利用代谢物之间的差异相关性来评估 POI 对代谢物网络的影响，从而构建表型特异性的代谢物网络以进行基于 RW 的传播。实验结果表明，在识别与结直肠癌相关的代谢途径改变方面，dci-MSEA 优于传统的基于 RW 的 MSEA。此外，通过整合个体特异性代谢物网络，dci-MSEA 策略可以轻松扩展到疾病异质性分析。在这里，dci-MSEA 用于破译结直肠癌的异质性。目前的结果强调了结直肠癌样本的聚类及其对差异通路的聚类特异性选择，并证明了 dci-MSEA 在异质性分析中的可行性。总而言之，所提出的 dci-MSEA 可以提供对疾病机制和疾病异质性确定的见解。

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Differential Correlations Informed Metabolite Set Enrichment Analysis to Decipher Metabolic Heterogeneity of Disease

Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.

更新日期：2023-08-09

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