MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers

Yanling Sun(Beijing Institute of Genomics), Xinchang Zheng(Beijing Institute of Genomics), Guoliang Wang(Beijing Institute of Genomics), Yibo Wang(Beijing Institute of Genomics), Xiaoning Chen(Beijing Institute of Genomics), Jiani Sun(Beijing Institute of Genomics), Zhuang Xiong(Beijing Institute of Genomics), Sisi Zhang(Beijing Institute of Genomics), Tianyi Wang(Southern University of Science and Technology), Zhuojing Fan(Beijing Institute of Genomics), Congfan Bu(Beijing Institute of Genomics), Yīmíng Bào(Beijing Institute of Genomics), Wenming Zhao(Beijing Institute of Genomics)
Molecular Cancer Research
April 7, 2023
Cited by 5Open Access
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Abstract

Cancer is one of the leading causes of human death. As metabolomics techniques become more and more widely used in cancer research, metabolites are increasingly recognized as crucial factors in both cancer diagnosis and treatment. In this study, we developed MACdb (https://ngdc.cncb.ac.cn/macdb), a curated knowledgebase to recruit the metabolic associations between metabolites and cancers. Unlike conventional data-driven resources, MACdb integrates cancer-metabolic knowledge from extensive publications, providing high quality metabolite associations and tools to support multiple research purposes. In the current implementation, MACdb has integrated 40,710 cancer-metabolite associations, covering 267 traits from 17 categories of cancers with high incidence or mortality, based entirely on manual curation from 1,127 studies reported in 462 publications (screened from 5,153 research papers). MACdb offers intuitive browsing functions to explore associations at multi-dimensions (metabolite, trait, study, and publication), and constructs knowledge graph to provide overall landscape among cancer, trait, and metabolite. Furthermore, NameToCid (map metabolite name to PubChem Cid) and Enrichment tools are developed to help users enrich the association of metabolites with various cancer types and traits. IMPLICATION: MACdb paves an informative and practical way to evaluate cancer-metabolite associations and has a great potential to help researchers identify key predictive metabolic markers in cancers.


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