PDF(Sci-hub)
Abstract：
Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results.
摘要：
代谢异质性是癌症的标志，可以区分正常表型和癌症表型。在系统生物学领域，特定于上下文的模型有助于从高质量数据中提取生理相关信息。这里,利用代谢模式的异质性来发现所有癌症的生物标志物，我们使用完善的算法将组学数据整合到通用人类代谢模型Recon3D中,对数千个特定环境模型进行了基准测试.通过通量平衡分析分析能够携带通量的活性反应及其大小，我们证明了每种癌症的代谢模式是独特的，可以作为癌症代谢指纹。随后，我们搜索了适当的特征选择方法来对表征每种癌症的通量状态进行聚类。我们采用了基于PCA的降维和随机森林学习算法来揭示包含最相关信息的反应，以有效地识别最有影响力的通量。最后，我们发现了可能是癌症代谢异质性的主要来源的不同途径.我们设计了GEMbench网站以交互方式呈现数据，方法,和分析结果。
