酶促反应在调节细胞过程中发挥着关键作用，对生物功能具有高度特异性。当酶反应被疾病中的基因、蛋白质或代谢功能障碍破坏时，可视化由此产生的扰动酶反应诱导的多组学网络变得至关重要。多组学网络可视化有助于全面了解生物系统内的功能和调控机制。在这项研究中，我们设计了 PhenoMultiOmics，这是一种基于酶反应的多组学网络服务器，旨在探索多组学网络的范围跨越各种癌症类型。我们首先策划了 PhenoMultiOmics 数据库 (PMODB)，该数据库能够根据酶促反应检索癌症-基因-蛋白质-代谢物关系。然后，我们开发了 MultiOmics 网络可视化模块来描述基因、蛋白质和代谢物之间的相互作用，以响应特定的癌症相关酶反应。生物标志物发现模块通过差异组学特征表达和通路富集分析促进功能分析。 PhenoMultiOmics 已用于分析胃癌的转录组学数据和肺癌的代谢组学数据，提供对中断的酶反应和相关多组学网络的见解。PhenoMultiOmics 可通过 https://phenomultiomics.shinyapps.io/cancer/ 免费访问用户友好的交互式网络界面。补充数据可在生物信息学在线获取。© 作者 2024。由牛津大学出版社出版。

Enzymatic reactions play a pivotal role in regulating cellular processes with a high degree of specificity to biological functions. When enzymatic reactions are disrupted by gene, protein, or metabolite dysfunctions in diseases, it becomes crucial to visualize the resulting perturbed enzymatic reaction-induced multi-omics network. Multi-omics network visualization aids in gaining a comprehensive understanding of the functionality and regulatory mechanisms within biological systems.In this study, we designed PhenoMultiOmics, an enzymatic reaction-based multi-omics web server designed to explore the scope of the multi-omics network across various cancer types. We first curated the PhenoMultiOmics Database (PMODB), which enables the retrieval of cancer-gene-protein-metabolite relationships based on the enzymatic reactions. We then developed the MultiOmics network visualization module to depict the interplay between genes, proteins, and metabolites in response to specific cancer-related enzymatic reactions. The biomarker discovery module facilitates functional analysis through differential omic feature expression and pathway enrichment analysis. PhenoMultiOmics has been applied to analyze transcriptomics data of gastric cancer and metabolomics data of lung cancer, providing insights into interrupted enzymatic reactions and the associated multi-omics network.PhenoMultiOmics is freely accessed at https://phenomultiomics.shinyapps.io/cancer/ with a user-friendly and interactive web interface.Supplementary data are available at Bioinformatics online.© The Author(s) 2024. Published by Oxford University Press.