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MACdb: A Curated Knowledgebase for Metabolic Associations across Human Cancers.
Sun, Yanling; Zheng, Xinchang; Wang, Guoliang; Wang, Yibo; Chen, Xiaoning; Sun, Jiani; Xiong, Zhuang; Zhang, Sisi; Wang, Tianyi; Fan, Zhuojing; Bu, Congfan; Bao, Yiming; Zhao, Wenming.
Afiliação
  • Sun Y; National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Zheng X; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Wang G; National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Wang Y; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Chen X; National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Sun J; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Xiong Z; University of Chinese Academy of Sciences, Beijing, China.
  • Zhang S; National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Wang T; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Fan Z; University of Chinese Academy of Sciences, Beijing, China.
  • Bu C; National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Bao Y; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
  • Zhao W; University of Chinese Academy of Sciences, Beijing, China.
Mol Cancer Res ; 21(7): 691-697, 2023 07 05.
Article em En | MEDLINE | ID: mdl-37027007
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Mol Cancer Res Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Mol Cancer Res Ano de publicação: 2023 Tipo de documento: Article