MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers

Author:

Sun Yanling12ORCID,Zheng Xinchang12ORCID,Wang Guoliang123ORCID,Wang Yibo123ORCID,Chen Xiaoning123ORCID,Sun Jiani1234ORCID,Xiong Zhuang123ORCID,Zhang Sisi12ORCID,Wang Tianyi5ORCID,Fan Zhuojing12ORCID,Bu Congfan12ORCID,Bao Yiming123ORCID,Zhao Wenming123ORCID

Affiliation:

1. 1National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.

2. 2CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.

3. 3University of Chinese Academy of Sciences, Beijing, China.

4. 4Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China.

5. 5School of Medicine, Southern University of Science and Technology, Shenzhen, China.

Abstract

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.

Funder

Chinese Academy of Sciences

Publisher

American Association for Cancer Research (AACR)

Subject

Cancer Research,Oncology,Molecular Biology

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